Question: An implicit segmentation technique for the recognition of handwritten character strings.

This paper presents a comprehensive perspective on pattern classification, integrating both statistical and neural network approaches. Unlike traditional views that treat these methodologies as distinct entities, this unified approach highlights the synergies and interdependencies between statistical analysis and neural network techniques in the field of pattern recognition. It elucidates how statistical methods, rooted in probability theory and decision theory, provide a solid foundation for understanding pattern classification problems. Simultaneously, it showcases the flexibility and adaptability of neural networks, particularly in handling complex and non-linear patterns. By merging these two paradigms, the paper offers a holistic framework for pattern classification that can be particularly advantageous in scenarios involving large, high-dimensional data sets or intricate feature spaces. This integrated approach not only enhances the robustness and accuracy of classification models but also facilitates a deeper understanding of underlying data structures, ultimately leading to more effective and efficient pattern recognition systems. Specifically, in the context of recognizing handwritten strings of characters, this unified view can provide insights into combining statistical methods for feature extraction and selection with neural network models for pattern recognition, potentially leading to improved recognition rates and more sophisticated handling of variations in handwriting styles.

A real time static isolated gesture recognition application using a Hidden Markov Model approach with features extracted from gestures' silhouettes is presented. Nine different hand poses with various degrees of rotation are considered. The system, both simple and effective, uses color images of the hands to be recognized directly from the camera and is capable of processing 23 frames per second on a Quad Core Intel Processor.

Digitizing early music sources requires new ways of dealing with musical documents. Assuming that current technologies cannot guarantee a perfect automatic transcription, our intention is to develop an interactive system in which user and software collaborate to complete the task. Since conventional score post-editing might be tedious, the user is allowed to interact using an electronic pen. Although this provides a more ergonomic interface, this interaction must be decoded as well. In our framework, the user traces the symbols using the electronic pen over a digital surface, which provides both the underlying image (offline data) and the drawing made by the e-pen (online data) to improve classification. Applying this methodology over 70 scores of the target musical archive, a dataset of 10 230 bimodal samples of 30 different symbols was obtained and made available for research purposes. This paper presents experimental results on classification over this dataset, in which symbols are recognized by combining the two modalities. This combination of modes has demonstrated its good performance, decreasing the error rate of using each modality separately and achieving an almost error-free performance.

A handwritten digit recognition system was used in a demonstration project to visualize artificial neural networks, in particular Kohonen’s self-organizing feature map. The purpose of this project was to introduce neural networks through a relatively easy-to-understand application to the general public. This paper describes several techniques used for preprocessing the handwritten digits, as well as a number of ways in which neural networks were used for the recognition task. Whereas the main goal was a purely educational one, a moderate recognition rate of 98% was reached on a test set.

Based on a multi-step process, an automatic hair segmentation method is created and tested on a database of 115 manually segmented hair images. By extracting various information components from an image, including background color, face position, hair color, skin color, and skin mask, a heuristic-based method is created for the detection and segmentation of hair that can detect hair with an accuracy of approximately 75% and with a false hair overestimation error of 34%. Furthermore, it is shown that downsampling the image down to a face width of 25px results in a 73% reduction in computation time with insignificant change in detection accuracy.

Handwriting analysis is a method to predict personality of an author and to better understand the writer. Allograph and allograph combination analysis is a scientific method of writer identification and evaluating the behavior. To make this computerized we considered six main different types of features: (i) size of letters, (ii) slant of letters and words, (iii) baseline, (iv) pen pressure, (v) spacing between letters and (vi) spacing between words in a document to identify the personality of the writer. Segmentation is used to calculate the features from digital handwriting and is trained to SVM which outputs the behavior of the writer. For this experiment 100 different writers were used for different handwriting data samples. The proposed method gives about 94% of accuracy rate with RBF kernel. In this paper an automatic method has been proposed to predict the psychological personality of the writer. The system performance is measured under two different conditions with the same sample.

This study reports the development of a Myanmar word segmentation method using Unicode standard encoding. Word segmentation is an essential step prior to natural language processing in the Myanmar language, because a Myanmar text is a string of characters without explicit word boundary delimiters. The proposed method has two phases: syllable segmentation and syllable merging. A rule-based heuristic approach was adopted for syllable segmentation, and a dictionary-based statistical approach for syllable merging. Evaluation of test results showed that the method is very effective for the Myanmar language.

An integrated OCR system for mathematical documents, called INFTY, is presented. INFTY consists of four procedures, i.e., layout analysis, character recognition, structure analysis of mathematical expressions, and manual error correction. In those procedures, several novel techniques are utilized for better recognition performance. Experimental results on about 500 pages of mathematical documents showed high character recognition rates on both mathematical expressions and ordinary texts, and sufficient performance on the structure analysis of the mathematical expressions.

Off-line recognition requires transferring the text under consideration into an image file. This represents the only available solution to bring the printed materials to the electronic media. However, the transferring process causes the system to lose the temporal information of that text. Other complexities that an off-line recognition system has to deal with are the lower resolution of the document and the poor binarisation, which can contribute to readability when essential features of the characters are deleted or obscured. Recognising Arabic script presents two additional challenges: orthography is cursive and letter shape is context sensitive. Certain character combinations form new ligature shapes, which are often font-dependent. Some ligatures involve vertical stacking of characters. Since not all letters connect, word boundary location becomes an interesting problem, as spacing may separate not only words, but also certain characters within a word. Various techniques have been implemented to achieve high recognition rates. These techniques have tackled different aspects of the recognition system. This review is organised into five major sections, covering a general overview, Arabic writing characteristics, Arabic text recognition system, Arabic OCR software and conclusions.

This paper describes a performance evaluation study in which some efficient classifiers are tested in handwritten digit recognition. The evaluated classifiers include a statistical classifier (modified quadratic discriminant function, MQDF), three neural classifiers, and an LVQ (learning vector quantization) classifier. They are efficient in that high accuracies can be achieved at moderate memory space and computation cost. The performance is measured in terms of classification accuracy, sensitivity to training sample size, ambiguity rejection, and outlier resistance. The outlier resistance of neural classifiers is enhanced by training with synthesized outlier data. The classifiers are tested on a large data set extracted from NIST SD19. As results, the test accuracies of the evaluated classifiers are comparable to or higher than those of the nearest neighbor (1-NN) rule and regularized discriminant analysis (RDA). It is shown that neural classifiers are more susceptible to small sample size than MQDF, although they yield higher accuracies on large sample size. As a neural classifier, the polynomial classifier (PC) gives the highest accuracy and performs best in ambiguity rejection. On the other hand, MQDF is superior in outlier rejection even though it is not trained with outlier data. The results indicate that pattern classifiers have complementary advantages and they should be appropriately combined to achieve higher performance.

In this brief paper, we propose a method of feature extraction for digit recognition that is inspired by vision research: a sparse-coding strategy and a local maximum operation. We show that our method, despite its simplicity, yields state-of-the-art classification results on a highly competitive digit-recognition benchmark. We first employ the unsupervised Sparsenet algorithm to learn a basis for representing patches of handwritten digit images. We then use this basis to extract local coefficients. In a second step, we apply a local maximum operation to implement local shift invariance. Finally, we train a support vector machine (SVM) on the resulting feature vectors and obtain state-of-the-art classification performance in the digit recognition task defined by the MNIST benchmark. We compare the different classification performances obtained with sparse coding, Gabor wavelets, and principal component analysis (PCA). We conclude that the learning of a sparse representation of local image patches combined with a local maximum operation for feature extraction can significantly improve recognition performance.


Question: Algorithms for detecting objects in real-time within images.

The significance of robust features in object detection from a limited dataset is highlighted in this research. Despite recent advancements in face detection systems, which have achieved impressive detection rates and real-time performance, these systems often require an extensive training database—typically around 5,000 positive examples—to attain optimal results. This reliance on large datasets presents a significant challenge when attempting to detect multiple objects or various views of a single object, particularly in scenarios where such large datasets are not readily available. For instance, multi-view face detection systems have not yet achieved satisfactory performance. In this study, we address the issue of learning object detection from a small training set. Our findings emphasize the critical role of feature selection in enhancing detection accuracy. Specifically, we demonstrate that employing local edge orientation histograms (EOH) as features can markedly improve performance compared to the conventional linear features utilized in current systems. For frontal face detection, using local orientation histograms allows us to achieve state-of-the-art performance with only a few hundred training examples. Similarly, for profile view face detection, these histograms enable us to develop a system that appears to outperform existing real-time systems, even when trained on a limited dataset. This underscores the potential of robust feature extraction methods in scenarios where large training datasets are not feasible.

Pedestrian detection will play a major role in future driver assistance and autonomous driving. One powerful algorithm in this field uses HOG features to describe the specific properties of pedestrians in images. To determine their locations, features are extracted and classified window-wise from different scales of an input image. The results of the classification are finally merged to remove overlapping detections. The real-time execution of this method requires specific FPGA- or ASIC-architectures. Recent work focused on accelerating the feature extraction and classification. Although merging is an important step in the algorithm, it is only rarely considered in hardware implementations. A reason for that could be its complexity and irregularity that is not trivial to implement in hardware. In this paper, we present a new bottom-up FPGA architecture that maps the full HOG-based algorithm for pedestrian detection including feature extraction, SVM classification, and multi-scale processing in combination with merging. For that purpose, we also propose a new hardware-optimized merging method. The resulting architecture is highly efficient. Additionally, we present an FPGA-based full real-time and multi-scale pedestrian detection demonstration system.

With the growth of digitized comics, image understanding techniques are becoming important. In this paper, we focus on object detection, which is a fundamental task of image understanding. Although convolutional neural networks (CNN)-based methods archived good performance in object detection for naturalistic images, there are two problems in applying these methods to the comic object detection task. First, there is no large-scale annotated comics dataset. The CNN-based methods require large-scale annotations for training. Secondly, the objects in comics are highly overlapped compared to naturalistic images. This overlap causes the assignment problem in the existing CNN-based methods. To solve these problems, we proposed a new annotation dataset and a new CNN model. We annotated an existing image dataset of comics and created the largest annotation dataset, named Manga109-annotations. For the assignment problem, we proposed a new CNN-based detector, SSD300-fork. We compared SSD300-fork with other detection methods using Manga109-annotations and confirmed that our model outperformed them based on the mAP score.

We propose a branch-and-cut strategy for efficient region-based object detection. Given an oversegmented image, our method determines the subset of spatially contiguous regions whose collective features will maximize a classifier's score. We formulate the objective as an instance of the prize-collecting Steiner tree problem, and show that for a family of additive classifiers this enables fast search for the optimal object region via a branch-and-cut algorithm. Unlike existing branch-and-bounddetection methods designed for bounding boxes, our approach allows scoring of irregular shapes — which is especially critical for objects that do not conform to a rectangular window. We provide results on three challenging object detection datasets, and demonstrate the advantage of rapidly seeking best-scoring regions rather than subwindow rectangles.

Based on a multi-step process, an automatic hair segmentation method is created and tested on a database of 115 manually segmented hair images. By extracting various information components from an image, including background color, face position, hair color, skin color, and skin mask, a heuristic-based method is created for the detection and segmentation of hair that can detect hair with an accuracy of approximately 75% and with a false hair overestimation error of 34%. Furthermore, it is shown that downsampling the image down to a face width of 25px results in a 73% reduction in computation time with insignificant change in detection accuracy.

Moving vehicles are detected and tracked automatically in monocular image sequences from road traffic scenes recorded by a stationary camera. In order to exploit the a priori knowledge about shape and motion of vehicles in traffic scenes, a parameterized vehicle model is used for an intraframe matching process and a recursive estimator based on a motion model is used for motion estimation. An interpretation cycle supports the intraframe matching process with a state MAP-update step. Initial model hypotheses are generated using an image segmentation component which clusters coherently moving image features into candidate representations of images of a moving vehicle. The inclusion of an illumination model allows taking shadow edges of the vehicle into account during the matching process. Only such an elaborate combination of various techniques has enabled us to track vehicles under complex illumination conditions and over long (over 400 frames) monocular image sequences. Results on various real-world road traffic scenes are presented and open problems as well as future work are outlined.

This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance. The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion. An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.

Most pedestrian detection approaches that achieve high accuracy and precision rate and that can be used for realtime applications are based on histograms of gradient orientations. Usually multiscale detection is attained by resizing the image several times and by recomputing the image features or using multiple classifiers for different scales. In this paper we present a pedestrian detection approach that uses the same classifier for all pedestrian scales based on image features computed for a single scale. We go beyond the low level pixel-wise gradient orientation bins and use higher level visual words organized into Word Channels. Boosting is used to learn classification features from the integral Word Channels. The proposed approach is evaluated on multiple datasets and achieves outstanding results on the INRIA and Caltech-USA benchmarks. By using a GPU implementation we achieve a classification rate of over 10 million bounding boxes per second and a 16 FPS rate for multiscale detection in a 640×480 image.

Color is an efficient feature for object detection as it has the advantage of being invariant to changes in scaling, rotation, and partial occlusion. Skin color detection is an essential required step in various applications related to computer vision. The rapidly-growing research in human skin detection is based on the premise that information about individuals, intent, mode, and image contents can be extracted from colored images, and computers can then respond in an appropriate manner. Detecting human skin in complex images has proven to be a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination, race, aging, imaging conditions, and complex background. However, many methods have been developed to deal with skin detection problem in color images. The purpose of this study is to provide an up-to-date survey on skin color modeling and detection methods. We also discuss relevant issues such as color spaces, cost and risks, databases, testing, and benchmarking. After investigating these methods and identifying their strengths and limitations, we conclude with several implications for future direction.

.4 long standing problem in computer vision is the extraction of meaningfulfeatures from images. This paper describes a method of segmenting curves in images into a combination of circular arcs and straight lines. This uses a recursive algorithm that first analyses lists of connected edge points and finds a polygonal description, and then analyses this description fitting arcs to groups of connected lines. The result is a description of image edges consisting of circular arcs and lines. The algorithm uses no thresholding. Instead the best option is chosen at each decision stage.

We describe a GPU-based implementation of motion detection from a moving platform. Motion detection from a moving platform is inherently difficult as the moving camera induces 2D motion field in the entire image. A step compensating for camera motion is required prior to estimating of the background model. Due to inevitable registration errors, the background model is estimated according to a sliding window of frames to avoid the case where erroneous registration influences the quality of the detection for the whole sequence. However, this approach involves several characteristics that put a heavy burden on real-time CPU implementation. We exploit GPU to achieve significant acceleration over standard CPU implementations. Our GPU-based implementation can build the background model and detect motion regions at around 18 fps on 320times240 videos that are captured for a moving camera.


Question: Methods for real-time object recognition in image data.

This paper introduces an innovative approach for face expression recognition that utilizes AdaBoost for selective feature extraction, focusing on the most informative global and local appearance features. This selectivity not only streamlines the feature space but also accelerates online classification processes. Our methodology is contrasted with another prominent margin-based classifier, Support Vector Machines (SVM), highlighting the benefits of AdaBoost in this domain. We employ histograms derived from Gabor and Gaussian derivative responses as the basis for our appearance features. The technique is specifically tailored for face expression recognition, where the importance of local appearances is paramount. Our findings reveal that while SVM can achieve comparable performance, AdaBoost's feature selection yields a hypothesis model that is visually comprehensible and easily interpretable, a quality that is notably absent in the high-dimensional support vectors produced by SVM. This makes AdaBoost a particularly suitable choice for applications where interpretability is as crucial as accuracy.

This paper describes the implementation of a LCD stripe projection system, based on a new processing scheme called line shift processing. One advantage of the method is, that the projection device can, but is not required to be stable over time nor does it have to be calibrated. The new method therefore allows us to use an off-the-shelf multimedia projector to acquire dense 3-D data (several 100K Triangles) of large objects in a matter of seconds. Since we are able to control our source of illumination, we can also acquire registered color information using standard monochrome cameras. Besides the description of the system some practical examples are given. The calibration of the system is also addressed.

A new concept for the detection of small objects from modular optoelectronic multispectral scanner (MOMS-02) high spatial resolution panchromatic satellite imagery is presented. We combine supervised shape classification with unsupervised image segmentation in an iterative procedure which allows a target-oriented search for specific object shapes.

Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g. ADD, BITWISE SHIFT, etc.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single laptop CPU) generates a small set of category-independent, high quality object windows, yielding 96.2% object detection rate (DR) with 1, 000 proposals. Increasing the numbers of proposals and color spaces for computing BING features, our performance can be further improved to 99.5% DR.

This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance. The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion. An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.

A vision-based real-time driver fatigue detection system is proposed for driving safely. The driver's face is located, from color images captured in a car, by using the characteristic of skin colors. Then, edge detection is used to locate the regions of eyes. In addition to being used as the dynamic templates for eye tracking in the next frame, the obtained eyes' images are also used for fatigue detection in order to generate some warning alarms for driving safety. The system is tested on a Pentium III 550 CPU with 128 MB RAM. The experiment results seem quite encouraging andpromising. The system can reach 20 frames per second for eye tracking, and the average correct rate for eye location and tracking can achieve 99.1% on four test videos. The correct rate for fatigue detection is l00%, but the average precision rate is 88.9% on the test videos.

Autonomous vehicles, surveillance systems, face detection systems lead to the development of accurate object detection system [1]. These systems recognize, classify and localize every object in an image by drawing bounding boxes around the object [2]. These systems use existing classification models as backbone for Object Detection purpose. Object detection is the process of finding instances of real-world objects such as human faces, animals and vehicles etc., in pictures, images or in videos. An Object detection algorithm uses extracted features and learning techniques to recognize the objects in an image. In this paper, various Object Detection techniques have been studied and some of them are implemented. As a part of this paper, three algorithms for object detection in an image were implemented and their results were compared. The algorithms are “Object Detection using Deep Learning Framework by OpenCV”, “Object Detection using Tensorflow” and “Object Detection using Keras models”.

We provide a novel hardware and software system for micro air vehicles (MAV) that allows high-speed, low-latency onboard image processing. It uses up to four cameras in parallel on a miniature rotary wing platform. The MAV navigates based on onboard processed computer vision in GPS-denied in- and outdoor environments. It can process in parallel images and inertial measurement information from multiple cameras for multiple purposes (localization, pattern recognition, obstacle avoidance) by distributing the images on a central, low-latency image hub. Furthermore the system can utilize low-bandwith radio links for communication and is designed and optimized to scale to swarm use. Experimental results show successful flight with a range of onboard computer vision algorithms, including localization, obstacle avoidance and pattern recognition.

For a machine to be able to ‘see’, it must know something about the object it is ‘looking’ at, A common method in machine vision is to provide the machine with general rather than specific knowledge about the object. An alternative technique, and the one used in thti paper, b a model-based approach in which particulars aboat the object are given and this drives the analysis. The computer program described here, the WALKER model, maps images into a description in which a person is represented by the series of hierarchical levels, i.e. a person has an arm which has a lower-arm which has a hand. l7te performance of the program ti illustrated by superimposing the machinegenerated picture over the original photographic images.

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45-90 m) than RCNN. RCNN has a similar performance at a short range (0-30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

In this paper, we propose a computer vision based system for real-time robust traffic sign detection and recognition, especially developed for intelligent vehicle. In detection phase, a color-based segmentation method is used to scan the scene in order to quickly establish regions of interest (ROI). Sign candidates within ROIs are detected by a set of Haar wavelet features obtained from AdaBoost training. Then, the Speeded Up Robust Features (SURF) is applied for the sign recognition. SURF finds local invariant features in a candidate sign and matches these features to the features of template images that exist in data set. The recognition is performed by finding out the template image that gives the maximum number of matches. We have evaluated the proposed system on our intelligent vehicle SmartVII. A recognition accuracy of over 90% in real-time has been achieved.


Question: Methods for identifying objects in real-time image processing algorithms.

This paper explores the implementation of Support Vector Machines (SVMs) in the field of computer vision, specifically focusing on the task of face detection. SVMs, a learning technique pioneered by V. Vapnik and his team at AT&T Bell Labs, offer a novel method for training polynomial, neural network, and Radial Basis Functions classifiers. The core of SVMs lies in solving a quadratic programming problem subject to linear constraints, which presents a significant computational challenge due to the dense quadratic form and memory demands that scale with the square of the data points. To address these challenges, the paper introduces a decomposition algorithm designed to ensure global optimality, making it suitable for training SVMs on large datasets. The algorithm works by iteratively solving sub-problems and assessing optimality conditions, which not only aid in generating improved iterative values but also define the stopping criteria for the algorithm. The effectiveness of this SVM implementation is demonstrated through experimental results on a face detection problem involving a dataset of 50,000 points, showcasing the practicality of the proposed approach in real-world applications. This study contributes to the growing body of knowledge on real-time object detection algorithms by highlighting the potential of SVMs in tackling complex visual recognition tasks.

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems.

Real-time stereo matching is necessary for many practical applications, including robotics. There are already many real-time stereo systems, but they typically use local approaches that cause object boundaries to be blurred and small objects to be removed. We have selected the Semi-Global Matching (SGM) method for implementation on graphics hardware, because it can compete with the currently best global stereo methods. At the same time, it is much more efficient than most other methods that produce a similar quality. In contrast to previous work, we have fully implemented SGM including matching with mutual information, which is partly responsible for the high quality of disparity images. Our implementation reaches 4.2 fps on a GeForce 8800 ULTRA with images of 640×480 pixel size and 128 pixel disparity range and 13 fps on images of 320×240 pixel size and 64 pixel disparity range.

This work investigated two different machine learning techniques: Cascade Learning and Deep Learning, to find out which algorithm performs better to detect the number plate of vehicles registered in Bangladesh. To do this, we created a dataset of about 1000 images collected from a security camera of Independent University, Bangladesh. Each image in the dataset were then labelled manually by selecting the Region of Interest (ROI). In the Cascade Learning approach, a sliding window technique was used to detect objects. Then a cascade classifier was employed to determine if the window contained object of interest or not. In the Deep Learning approach, CIFAR-10 dataset was used to pre-train a 15-layer Convolutional Neural Network (CNN). Using this pretrained CNN, a Regions with CNN (R-CNN) was then trained using our dataset. We found that the Deep Learning approach (maximum accuracy 99.60% using 566 training images) outperforms the detector constructed using Cascade classifiers (maximum accuracy 59.52% using 566 positive and 1022 negative training images) for 252 test images.

Closed Circuit Television (CCTV) is currently used in daily basis for a wide variety of purposes. The development of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognition in CCTV video is used as the basis of decision making to produce automatic, effective and efficient integrated system. This CCTV video process provides three outputs, motion detection information, face detection information and face identification information. Accumulative Differences Images (ADI) method is used for motion detection, and Haar Classifiers Cascade method is used for face detection. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). Then, these features are trained by Counter-Propagation Network (CPN). Offline tests are performed on 45 CCTV video. The result shows 92.655% success rate on motion detection,76% success rate on face detection, and 60% success rate on face detection. It shows that this faces detection and identification through CCTV video have not been able to obtain optimal results. The motion detection process is ideal to be applied in real-time conditions. Yet if it’s combined with face recognition process, it causes a significant time delay. Keywords— ADI, Haar Cascade Classifiers, SURF, PCA, CPN  ISSN (print): 1978-1520, ISSN (online): 2460-7258 IJCCS Vol. 12, No. 2, July 2018 : 107 – 118 108

We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.

Among the most important research in Intelligent Transportation Systems (ITS) is the development of systems that automatically monitor traffic flow at intersections. Rather than being based on global flow analysis as is currently done, these automatic monitoring systems should be based on local analysis of the behavior of each vehicle at the intersection. The systems should be able to identify each vehicle and track its behavior, and to recognize situations or events that are likely to result from a chain of such behavior. The most difficult problem associated with vehicle tracking is the occlusion effect among vehicles. In order to solve this problem we have developed an algorithm, referred to as spatio-temporal Markov random field (MRF), for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the – image axes as well as the time axes. Vehicles, of course, are of various shapes and they move in random fashion, thereby leading to full or partial occlusion at intersections. Despite these complications, our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%–96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness.

We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition. We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9–13 FPS detection speed on a moderate commercial GPU.

Humans detect and identify faces in a scene with little or no effort . However, building an automated system that accomplishes this task is very difficult . There are several related Subproblems : detection of a pattern as a face . identification of the face, analysis of facial expressions, and classification based on physical features of the face . A system that performs these operations will find many applications, e .g . criminal identification, authentication in secure systems, etc . Most of the work to date has been in identification . This paper surveys the past work in solving these problems . The capability of the human visual system with respect to these problems is also discussed . It is meant to serve as a guide for an automated system . Some new approaches to these problems are also briefly discussed . Face detection Face identification Facial expressions Classification Facial features

We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture model and to simultaneously select the appropriate number of components for each pixel. We also present a simple non-parametric adaptive density estimation method. The two methods are compared with each other and with some previously proposed algorithms. 2005 Elsevier B.V. All rights reserved.

In image sequence processing kalman filtering is used for an adaptive background estimation, in order to separate the foreground from the background. The presented work is an approach which takes into account that changing illumination should be considered in the background estimation, and should not be detected as foreground. The new approach assumes a stationary CCD cameras with fixed focal length and considers non-rigid objects moving non-continuously like human bodies. Furthermore, statistic based methods are used to overcome the problems caused by shadow borders and the adaptation, when the background is covered by the foreground.


Question: Methods for Real-Time Object Recognition in Image Data

This paper presents a novel algorithm, Parameter-Sensitive Hashing (PSH), designed to tackle the computational challenges associated with high-dimensional parameter estimation tasks, such as pose estimation. Traditional example-based methods are efficient for simple systems or when the input dimensionality is low. However, for complex scenarios like pose estimation, the exponential increase in required examples and computational resources can become prohibitive. Our proposed PSH algorithm addresses this issue by learning a set of hashing functions that can efficiently index and retrieve relevant examples for specific estimation tasks. Building upon the concept of locality-sensitive hashing, which accelerates the search for approximate neighbors in a sublinear time relative to the number of examples, PSH critically depends on the selection of hash functions. We demonstrate how to identify the optimal set of hash functions tailored to a specific estimation problem. Experimental results confirm that PSH can swiftly and accurately estimate the articulated pose of human figures from a vast database of example images. This advancement, partly developed while G.S. and P.V. were affiliated with Mitsubishi Electric Research Labs in Cambridge, MA, showcases the algorithm's potential for real-time object detection in images.

A common method for real-time segmentation of moving regions in image sequences involves “background subtraction,” or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.

Real Time Human-machine Interaction system using hand gesture Recognition to handle the mouse event , media player , image viewer .Users have to repeat same mouse and keyboard actions, inducing waste of time. Gestures have long been considered as an interaction technique that can potentially deliver more natural. A fast gesture recognition scheme is proposed to be an interface for the human-machine interaction (HMI) of systems. The system presents some lowcomplexity algorithms and gestures to reduce the gesture recognition complexity and be more suitable for controlling real-time computer systems. In this paper we use the webcam for capturing the image. After capturing the image it converts into the binary image. A gesture is a specific combination of hand position.

This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.

This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings. That is, two successive frames are likely to contain the same object or objects. This coherence is used as a supervisory signal over the unlabeled data, and is used to improve the performance on a supervised task of interest. We demonstrate the effectiveness of this method on some pose invariant object and face recognition tasks.

Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. To tie these ideas together we compute multiple segmentations of each image and then: (i) learn the object classes; and (ii) choose the correct segmentations. We demonstrate that such an algorithm succeeds in automatically discovering many familiar objects in a variety of image datasets, including those from Caltech, MSRC and LabelMe.

In this paper, we introduce a real-time face recognition (and announcement) system targeted at aiding the blind and low-vision people. The system uses a Microsoft Kinect sensor as a wearable device, performs face detection, and uses temporal coherence along with a simple biometric procedure to generate a sound associated with the identified person, virtualized at his/her estimated 3-D location. Our approach uses a variation of the K-nearest neighbors algorithm over histogram of oriented gradient descriptors dimensionally reduced by principal component analysis. The results show that our approach, on average, outperforms traditional face recognition methods while requiring much less computational resources (memory, processing power, and battery life) when compared with existing techniques in the literature, deeming it suitable for the wearable hardware constraints. We also show the performance of the system in the dark, using depth-only information acquired with Kinect's infrared camera. The validation uses a new dataset available for download, with 600 videos of 30 people, containing variation of illumination, background, and movement patterns. Experiments with existing datasets in the literature are also considered. Finally, we conducted user experience evaluations on both blindfolded and visually impaired users, showing encouraging results.

Pfinder is a real-time system for tracking people and interpreting their behavior. It runs at 10Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions. Pfinder has been successfully used in a wide range of applications including wireless interfaces, video databases, and low-bandwidth coding. Index Terms —Blobs, blob tracking, real-time, person tracking, 3D person tracking, segmentation, gesture recognition, mixture model, MDL.

We introduce the first visual dataset of fast foods with a total of 4,545 still images, 606 stereo pairs, 303 360° videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers. This work was motivated by research on fast food recognition for dietary assessment. The data was collected by obtaining three instances of 101 foods from 11 popular fast food chains, and capturing images and videos in both restaurant conditions and a controlled lab setting. We benchmark the dataset using two standard approaches, color histogram and bag of SIFT features in conjunction with a discriminative classifier. Our dataset and the benchmarks are designed to stimulate research in this area and will be released freely to the research community.

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (runtime) computational complexity and the (training-time) sample complexity scale linearly with the number of classes to be detected. We present a multitask learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required and, therefore, the runtime cost of the classifier, is observed to scale approximately logarithmically with the number of classes. The features selected by joint training are generic edge-like features, whereas the features chosen by training each class separately tend to be more object-specific. The generic features generalize better and considerably reduce the computational cost of multiclass object detection

This paper addresses automatic interpretation of images of outdoor scenes. The method allows instances of objects from a number of generic classes to be identi ed: vegetation, buildings, vehicles, roads, etc., thereby enabling image databases to be queried on scene content. The feature set is based, in part, on psychophysical principles and includes measures of colour, texture and shape. Using a large database of ground-truth labelled images, a neural network is trained as a pattern classi er. The method is demonstrated on a large test set to provide highly accurate image interpretations, with over 90% of the image area labelled correctly. Scene Understanding Object Recognition Classi cation Image Segmentation Neural Networks


Question: Methods for Real-Time Object Recognition in Image Analysis

A novel object recognition system has been devised, employing a distinctive set of local image features designed to be invariant to various transformations. These features exhibit robustness against changes in image scale, position, orientation, and are partially tolerant to alterations in illumination and projection, similar to the neuronal responses observed in the inferior temporal cortex of primates during object recognition tasks. The detection of these features is achieved efficiently through a multi-stage filtering process, which accurately pinpoints stable points within the scale space of the image. To accommodate local geometric distortions, image keys are constructed using blurred gradients in multiple orientation planes at varying scales. These keys serve as inputs to a nearest-neighbor indexing technique, facilitating the identification of potential object matches. The final step involves the verification of these matches by calculating a low-residual least-squares solution for the unknown parameters of the model. Demonstrated through experimental outcomes, this system can reliably recognize objects in cluttered scenes with partial occlusions, all within a processing time of less than 2 seconds.

This is the first in a pair of papers in which we present image-processing-based procedures for the measurement of fluvial gravels. The spatial and temporal resolution of surface grain-size characterization is constrained by the time-consuming and costly nature of traditional measurement techniques. Several groups have developed image-processing-based procedures, but none have demonstrated the transferability of these techniques between sites with different lithological, clast form and textural characteristics. Here we focus on image-processing procedures for identifying and measuring image objects (i.e. grains); the second paper examines the application of such procedures to the measurement of fluvially deposited gravels. Four image-segmentation procedures are developed, each having several internal parameters, giving a total of 416 permutations. These are executed on 39 images from three field sites at which the clasts have contrasting physical properties. The performance of each procedure is evaluated against a sample of manually digitized grains in the same images, by comparing three derived statistics. The results demonstrate that it is relatively straightforward to develop procedures that satisfactorily identify objects in any single image or a set of images with similar sedimentary characteristics. However, the optimal procedure is that which gives consistently good results across sites with dissimilar sedimentary characteristics. We show that neighborhood-based operations are the most powerful, and a morphological bottom-hat transform with a double threshold is optimal. It is demonstrated that its performance approaches that of the procedures giving the best results for individual sites. Overall, it out-performs previously published, or improvements to previously published, methods.

We describe a model based recognition system, called LEWIS, for the identification of planar objects based on a projectively invariant representation of shape. The advantages of this shape description include simple model acquisition (direct from images), no need for camera calibration or object pose computation, and the use of index functions. We describe the feature construction and recognition algorithms in detail and provide an analysis of the combinatorial advantages of using index functions. Index functions are used to select models from a model base and are constructed from projective invariants based on algebraic curves and a canonical projective coordinate frame. Examples are given of object recognition from images of real scenes, with extensive object libraries. Successful recognition is demonstrated despite partial occlusion by unmodelled objects, and realistic lighting conditions.

The automatic detection of objects within complex volumetric imagery is becoming of increased interest due to the use of dual energy Computed Tomography (CT) scanners as an aviation security deterrent. These devices produce a volumetric image akin to that encountered in prior medical CT work but in this case we are dealing with a complex multi-object volumetric environment including significant noise artefacts. In this work we look at the application of the recent extension to the seminal SIFT approach to the 3D volumetric recognition of rigid objects within this complex volumetric environment. A detailed overview of the approach and results when applied to a set of exemplar CT volumetric imagery is presented.

We propose a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity, based primarily on a Bayesian (MAP) analysis of image differences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was demonstrated using results from DARPA’s 1996 “FERET” face recognition competition, in which this Bayesian matching algorithm was found to be the top performer. In addition, we derive a simple method of replacing costly computation of nonlinear (on-line) Bayesian similarity measures by inexpensive linear (off-line) subspace projections and simple Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large databases. Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c Mitsubishi Electric Research Laboratories, Inc., 2002 201 Broadway, Cambridge, Massachusetts 02139 Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000.

Multi-character recognition in arbitrary photographs on mobile platform is difficult, in terms of both accuracy and real-time performance. In this paper, we focus on the domain of hand-written multi-digit recognition. Convolutional neural network (CNN) is the state-of-the-art solution for object recognition, and presents a workload that is both compute and data intensive. To reduce the workload, we train a shallow CNN offline, achieving 99.07% top-1 accuracy. And we utilize preprocessing and segmentation to reduce input image size fed into CNN. For CNN implementation on the mobile platform, we adopt and modify DeepBeliefSDK to support batching fully-connected layers. On NVIDIA SHIELD tablet, the application processes a frame and extracts 32 digits in approximately 60ms, and batching the fully-connected layers reduces the CNN runtime by another 12%.

A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled accordingly. With 11 static features including color distributions, texture parameters and shape roundness, the static SVM classifier is trained and filters the segmented results. Using defined overlapping degree and varying degree, the remained candidate regions are matched among consecutive frames. Subsequently the variations of color, texture, roundness, area, contour are computed, then the average and the mean square deviation of them are obtained. Together with the flickering frequency from temporal wavelet based Fourier descriptors analysis of flame contour, 27 dynamic features are used to train the dynamic SVM classifier, which is applied for final decision. Our approach has been tested with dozens of video clips, and it can detect forest fire while recognize the fire like objects, such as red house, bright light and flying flag. Except for the acceptable accuracy, our detection algorithm performs in real time, which proves its value for computer vision based forest fire surveillance.

The task of gesture recognition is highly challenging due to complex background, presence of nongesture hand motions, and different illumination environments. In this paper, a new technique is proposed which begins by detecting the hand and determining its canter, tracking the hands trajectory and analyzing the variations in the hand locations, and finally recognizing the gesture. The proposed technique overcomes background complexity and distance from camera which is up to 2.5 meters. Experimental results show that the proposed technique can recognize 12 gestures with rate above 94%. Keywords— Hand gesture recognition, skin detection, Hand tracking.

─ This article summarizes the most important research projects developed in thearea of recognitions y stemsusing facial images. It presents the description of the principal lines of study about people identification systems using face recognition. Furthermore, it contains a synthesis of recent mathematical techniques for pattern sextraction in these systems.

Some state-of-the-art video stabilization methods can achieve quite good visual effect, but they always cost a lot of time. On the other hand, current real-time video stabilization methods cannot generate satisfactory results. In this paper, we propose a novel trajectory-based video stabilization method which can generate high-quality results in real time. Our method runs very fast, because many techniques are proposed for acceleration. In the trajectory smoothing step, trajectories are extracted, pre-processed, and smoothed. A video splitting algorithm is proposed for pre-processing, and binomial filtering is used for smoothing. Both of them are simple and fast. In the frame warping step, we calculate a spatially varying warp that is directed by a grid mesh for each frame. Instead of solving time consuming global optimization problems, the transformation matrix of each grid is calculated using nearby trajectories in our method, leading to very high speed. We implement our method and run it on a variety of videos. Experiments show that while the stabilization effect is comparable with state-of-the-art methods, our algorithm can run in real time.

MOVING OBJECT DETECTION, TRACKING AND CLASSIFICATION FOR SMART VIDEO SURVEILLANCE Yiğithan Dedeoğlu M.S. in Computer Engineering Supervisor: Assist. Prof. Dr. Uğur Güdükbay August, 2004 Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-capacity storage devices and high speed network infrastructure paved the way for cheaper, multi sensor video surveillance systems. Traditionally, the video outputs are processed online by human operators and are usually saved to tapes for later use only after a forensic event. The increase in the number of cameras in ordinary surveillance systems overloaded both the human operators and the storage devices with high volumes of data and made it infeasible to ensure proper monitoring of sensitive areas for long times. In order to filter out redundant information generated by an array of cameras, and increase the response time to forensic events, assisting the human operators with identification of important events in video by the use of “smart” video surveillance systems has become a critical requirement. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. In this thesis, a smart visual surveillance system with real-time moving object detection, classification and tracking capabilities is presented. The system operates on both color and gray scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. The classification algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group and vehicle. The system is also able to detect the natural phenomenon fire in various scenes reliably. The proposed tracking algorithm successfully tracks video objects even in full occlusion cases. In addition to these, some important needs of a robust iii


Question: A MapReduce-driven deep learning technique illustrated with a handwritten digit recognition case study.

This paper explores the utilization of Hadoop, an open-source variant of Google's MapReduce framework and distributed file system, for executing machine learning algorithms on clusters of computers. Through a series of benchmarks on searching and sorting tasks, we analyze how different system configurations impact performance. Our findings highlight the suitability of the MapReduce paradigm for executing specific categories of machine learning problems. We also propose enhancements to Hadoop's implementation to better accommodate machine learning tasks. Our conclusion supports the use of MapReduce for fundamental data processing operations on large datasets, acknowledging that more intricate machine learning challenges require additional considerations.

Received Feb 10, 2017 Revised Apr 14, 2017 Accepted May 23, 2017 Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement the machine learning or to design intelligent machines. In this paper brief introduction to all machine learning paradigm and application area of deep machine learning and different types of neural networks with applications is discussed. Keyword:

Many problems in real-world applications involve predicting several random variables that are statistically related. Markov random fields (MRFs) are a great mathematical tool to encode such dependencies. The goal of this paper is to combine MRFs with deep learning to estimate complex representations while taking into account the dependencies between the output random variables. Towards this goal, we propose a training algorithm that is able to learn structured models jointly with deep features that form the MRF potentials. Our approach is efficient as it blends learning and inference and makes use of GPU acceleration. We demonstrate the effectiveness of our algorithm in the tasks of predicting words from noisy images, as well as tagging of Flickr photographs. We show that joint learning of the deep features and the MRF parameters results in significant performance gains.

We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

Deep Learning is an artificial intelligence function that imitates the mechanisms of the human mind in processing records and developing shapes to be used in selection construction. The objective of the paper is to improve the performance of the deep learning using a proposed algorithm called RFHTMC. This proposed algorithm is a merged version from Random Forest and HTM Cortical Learning Algorithm. The methodology for improving the performance of Deep Learning depends on the concept of minimizing the mean absolute percentage error which is an indication of the high performance of the forecast procedure. In addition to the overlap duty cycle which its high percentage is an indication of the speed of the processing operation of the classifier. The outcomes depict that the proposed set of rules reduces the absolute percent errors by using half of the value. And increase the percentage of the overlap duty cycle with 15%.

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce “deep compression”, a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35× to 49× without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9× to 13×; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35×, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49× from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3× to 4× layerwise speedup and 3× to 7× better energy efficiency.

ÐHandwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the on-line case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered. Index TermsÐHandwriting recognition, on-line, off-line, written language, signature verification, cursive script, handwriting learning tools, writer authentification.

This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of bodyworn inertial sensors and outputs are predefined human activities. In this problem, extracting effective features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a systematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.

The track 2 and track 3 of ChaLearn 2016 can be considered as Multi-Label Classification problems. We present a framework of learning deep binary encoding (DeepBE) to deal with multi-label problems by transforming multi-labels to single labels. The transformation of DeepBE is in a hidden pattern, which can be well addressed by deep convolutions neural networks (CNNs). Furthermore, we adopt an ensemble strategy to enhance the learning robustness. This strategy is inspired by its effectiveness in fine-grained image recognition (FGIR) problem, while most of face related tasks such as track 2 and track 3 are also FGIR problems. By DeepBE, we got 5.45% and 10.84% mean square error for track 2 and track 3 respectively. Additionally, we proposed an algorithm adaption method to treat the multiple labels of track 2 directly and got 6.84% mean square error.

Deep learning (DL) has been successfully deployed in various application domains ranging from computer vision, speech recognition, to natural language processing. As the network models and the datasets used to train these models scale, system architects are faced with new challenges in designing a scalable and energy-efficient high-performance computing (HPC) system for training DL algorithms. One of the key obstacles that DL researchers are facing is the memory capacity bottleneck, where the limited physical memory size of the PCIe-attached DL accelerator (whether it be a discrete GPU or ASIC accelerator like Google's Tensor Processing Unit) constrains the algorithm that can be studied. In this paper, and the associated invited special session talk, we first discuss recent research literature geared towards designing scalable HPC systems for DL. In this context, we then discuss the memory capacity wall problem and introduce the work on virtualized deep neural networks, a memory virtualization solution that systematically reduces the memory consumption of DNN training. We conclude by providing projections on future challenges DNN memory virtualization will encounter upon and suggest accelerator-centric DL system as a promising research direction for the development of a scalable and energy-efficient deep learning system architecture.


Question: A MapReduce-driven deep learning technique, demonstrated through a case study on handwritten digit recognition.

This paper presents a novel deep learning strategy for domain adaptation in large-scale sentiment classification tasks, leveraging the vast amount of online reviews and recommendations. Given the diversity of domains and the challenge of obtaining annotated training data for each, the focus is on developing a system that can be trained on labeled reviews from one domain and effectively applied to another. The proposed method utilizes a deep learning framework to automatically extract a rich, meaningful representation for each review without supervision. Sentiment classifiers trained using these high-level features significantly outperform existing state-of-the-art techniques on a benchmark dataset that includes reviews from four distinct Amazon product categories. Moreover, this approach is highly scalable, as demonstrated by its successful application to a more extensive, industry-level dataset encompassing 22 different domains. This showcases the robustness and adaptability of the proposed deep learning method in addressing domain adaptation challenges in sentiment classification across various domains.

**Question**: A comparative study of machine learning algorithms for stock market prediction

**Documents**: An empirical comparison of machine learning models for time series forecasting
Time series forecasting has become an increasingly important research topic due to the vast amount of sequential data being generated in various fields such as finance, economics, and healthcare. This paper compares the performance of several popular machine learning models for time series forecasting, including Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and classical models like Autoregressive Integrated Moving Average (ARIMA). We conduct extensive experiments on multiple datasets from different domains and evaluate the models using various performance metrics. Our results indicate that LSTM and GRU models outperform classical models in most cases, especially when the data exhibit complex patterns and long-term dependencies. However, the classical models are still competitive in some scenarios, particularly when the time series is relatively short or has a simple structure. The findings of this study provide valuable insights for choosing the right model for time series forecasting tasks.

**Similar Documents**: This study offers a comprehensive evaluation of various machine learning models for time series forecasting, reflecting the growing importance of sequential data analysis in domains such as finance, economics, and healthcare. We compare the efficacy of advanced models like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) against traditional methods, including Autoregressive Integrated Moving Average (ARIMA), across diverse datasets. Through rigorous experimentation, we assess model performance using multiple criteria. Our analysis reveals that LSTM and GRU models generally outperform classical methods, particularly in scenarios involving intricate patterns and long-term dependencies. Yet, traditional models remain

The use of large-scale data mining and machine learning has proliferated through the adoption of technologies such as Hadoop, with its simple programming semantics and rich and active ecosystem. This paper presents LinkedIn's Hadoop-based analytics stack, which allows data scientists and machine learning researchers to extract insights and build product features from massive amounts of data. In particular, we present our solutions to the ``last mile'' issues in providing a rich developer ecosystem. This includes easy ingress from and egress to online systems, and managing workflows as production processes. A key characteristic of our solution is that these distributed system concerns are completely abstracted away from researchers. For example, deploying data back into the online system is simply a 1-line Pig command that a data scientist can add to the end of their script. We also present case studies on how this ecosystem is used to solve problems ranging from recommendations to news feed updates to email digesting to descriptive analytical dashboards for our members.

Deep learning is currently popular in the field of computer vision and pattern recognition. Deep learning models such as Convolutional Neural Networks (CNNs) have been shown to be extremely effective at solving computer vision problems with state-of-the-art results. This work uses the CNN model to achieve a high degree of accuracy on the problem of fingerprint image classification. It will also be shown that effective image preprocessing can greatly reduce the dimensionality of the problem, allowing fast training times without compromising classification accuracy, even in networks of moderate depth. The proposed approach has achieved 95.9% classification accuracy on the NIST-DB4 dataset with zero sample rejection.

One of the most widely used frameworks for programming MapReduce-based applications is Apache Hadoop. Despite its popularity, however, application developers face numerous challenges in using the Hadoop framework, which stem from them having to effectively manage the resources of a MapReduce cluster, and configuring the framework in a way that will optimize the performance and reliability of MapReduce applications running on it. This paper addresses these problems by presenting the Profiling and Performance Analysis-based System (PPABS) framework, which automates the tuning of Hadoop configuration settings based on deduced application performance requirements. The PPABS framework comprises two distinct phases called the Analyzer, which trains PPABS to form a set of equivalence classes of MapReduce applications for which the most appropriate Hadoop config- uration parameters that maximally improve performance for that class are determined, and the Recognizer, which classifies an incoming unknown job to one of these equivalence classes so that its Hadoop configuration parameters can be self-tuned. The key research contributions in the Analyzer phase includes modifications to the well-known k - means + + clustering and Simulated Annealing algorithms, which were required to adapt them to the MapReduce paradigm. The key contributions in the Recognizer phase includes an approach to classify an unknown, incoming job to one of the equivalence classes and a control strategy to self-tune the Hadoop cluster configuration parameters for that job. Experimental results comparing the performance improvements for three different classes of applications running on Hadoop clusters deployed on Amazon EC2 show promising results.

As applications such as Apple Siri, Google Now, Microsoft Cortana, and Amazon Echo continue to gain traction, web-service companies are adopting large deep neural networks (DNN) for machine learning challenges such as image processing, speech recognition, natural language processing, among others. A number of open questions arise as to the design of a server platform specialized for DNN and how modern warehouse scale computers (WSCs) should be outfitted to provide DNN as a service for these applications.
 In this paper, we present DjiNN, an open infrastructure for DNN as a service in WSCs, and Tonic Suite, a suite of 7 end-to-end applications that span image, speech, and language processing. We use DjiNN to design a high throughput DNN system based on massive GPU server designs and provide insights as to the varying characteristics across applications. After studying the throughput, bandwidth, and power properties of DjiNN and Tonic Suite, we investigate several design points for future WSC architectures. We investigate the total cost of ownership implications of having a WSC with a disaggregated GPU pool versus a WSC composed of homogeneous integrated GPU servers. We improve DNN throughput by over 120x for all but one application (40x for Facial Recognition) on an NVIDIA K40 GPU. On a GPU server composed of 8 NVIDIA K40s, we achieve near-linear scaling (around 1000x throughput improvement) for 3 of the 7 applications. Through our analysis, we also find that GPU-enabled WSCs improve total cost of ownership over CPU-only designs by 4-20x, depending on the composition of the workload

C onvolutional neural networks (CNNs) have interesting and widely varied architectures catering to the requirement of learning features from three dimensional and structured data volumes (with a particular example being images, whether single or multi-channel). However, there are certain design considerations common to all CNN architectures that enable us to craft correct implementations of the architectures suited to our objectives. It is more than just mathematical calculations—it's an art in itself! Thus, in the present tutorial, we'll put our knowledge of CNNs and their distinct layers in practice by making use of Google's open-source machine learning (ML) platform, TensorFlow. The tutorial is fairly simple and follows the steps detailed online [1] in light of added insights, which can be assimilated from the basics of CNN architectural concepts. The TensorFlow platform was released in November 2015, in a climate of stiff competition from well-established deep learning and ML packages such as Torch and Theano. (The performance comparisons between the packages goes beyond the scope of this piece). The platform has been undergoing constant improvements since its release, thanks in part to a nascent community of contributors. TensorFlow packs quite a punch owing to its significant positive characteristics. Firstly, the package's open source nature makes it transparent, customizable, and extensible by the end user. Secondly, the documentation support is rich and elaborate, which is a critical factor in accelerating user adoption. Lastly, the fact that the package has been backed by Google, with some of the best ML and AI researchers being among its active contributors, is the cherry on top. Additionally it's compatibility with newer versions of Python (version 3.3 and above), optional GPU utilization, parallelizable execution, and visualization support via TensorBoard further enrich its impressive repertoire. Its Achilles heel—which has prevented widespread adoption—is its unavailability on the Windows OS (TensorFlow is only available on Mac OSX and Linux platforms), and its non-operability in distributed environments, as of now (it's bound to change soon). The question as to why the MNIST dataset [2] was chosen for this tutorial is a very pertinent one. The answer is the handwritten digits dataset has been ubiquitous since its release in 1998 by Yann LeCunn [3]. Having since become the textbook dataset for neural network classification tasks, it is a good choice in our context of getting acquainted with CNNs. The dataset consists of 60,000 training examples and 10,000 test examples, each being a grayscale …

Many algorithms for image processing and pattern recognition have recently been implemented on GPU (graphic processing unit) for faster computational times. However, the implementation using GPU encounters two problems. First, the programmer should master the fundamentals of the graphics shading languages that require the prior knowledge on computer graphics. Second, in a job which needs much cooperation between CPU and GPU, which is usual in image processings and pattern recognitions contrary to the graphics area, CPU should generate raw feature data for GPU processing as much as possible to effectively utilize GPU performance. This paper proposes more quick and efficient implementation of neural networks on both GPU and multi-core CPU. We use CUDA (compute unified device architecture) that can be easily programmed due to its simple C language-like style instead of GPU to solve the first problem. Moreover, OpenMP (Open Multi-Processing) is used to concurrently process multiple data with single instruction on multi-core CPU, which results ineffectively utilizing the memories of GPU. In the experiments, we implemented neural networks-based text detection system using the proposed architecture, and the computational times showed about 15 times faster than implementation using CPU and about 4 times faster than implementation on only GPU without OpenMP.

Deep neural networks (DNNs) are widely used in machine translation (MT). This article gives an overview of DNN applications in various aspects of MT.

In this thesis, the deep learning method convolutional neural networks (CNNs) has been used in an attempt to solve two classification problems, namely traffic sign recognition and Alzheimer’s disease detection. The two datasets used are from the German Traffic Sign Recognition Benchmark (GTSRB) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The final test results on the traffic sign dataset generated a classification accuracy of 98.81 %, almost as high as human performance on the same dataset, 98.84 %. Different parameter settings of the selected CNN structure have also been tested in order to see their impact on the classification accuracy. Trying to distinguish between MRI images of healthy brains and brains afflicted with Alzheimer’s disease gained only about 65 % classification accuracy. These results show that the convolutional neural network approach is very promising for classifying traffic signs, but more work needs to be done when working with the more complex problem of detecting Alzheimer’s disease.

Matrix operations are a fundamental building block of many computational tasks in fields as diverse as scientific computing, machine learning, and data mining. Matrix inversion is an important matrix operation, but it is difficult to implement in today's popular parallel dataflow programming systems, such as MapReduce. The reason is that each element in the inverse of a matrix depends on multiple elements in the input matrix, so the computation is not easily partitionable. In this paper, we present a scalable and efficient technique for matrix inversion in MapReduce. Our technique relies on computing the LU decomposition of the input matrix and using that decomposition to compute the required matrix inverse. We present a technique for computing the LU decomposition and the matrix inverse using a pipeline of MapReduce jobs. We also present optimizations of this technique in the context of Hadoop. To the best of our knowledge, our technique is the first matrix inversion technique using MapReduce. We show experimentally that our technique has good scalability, enabling us to invert a 10^5 x 10^5 matrix in 5 hours on Amazon EC2. We also show that our technique outperforms ScaLAPACK, a state-of-the-art linear algebra package that uses MPI.

The management and analysis of big data has been identified as one of the most important emerging needs in recent years. This is because of the sheer volume and increasing complexity of data being created or collected. Current clustering algorithms can not handle big data, and therefore, scalable solutions are necessary. Since fuzzy clustering algorithms have shown to outperform hard clustering approaches in terms of accuracy, this paper investigates the parallelization and scalability of a common and effective fuzzy clustering algorithm named Fuzzy C-Means (FCM) algorithm. The algorithm is parallelized using the MapReduce paradigm outlining how the Map and Reduce primitives are implemented. A validity analysis is conducted in order to show that the implementation works correctly achieving competitive purity results compared to state-of-the art clustering algorithms. Furthermore, a scalability analysis is conducted to demonstrate the performance of the parallel FCM implementation with increasing number of computing nodes used.