Question: Standardized enhancements to the High Efficiency Video Coding (HEVC) method.

This paper offers a comprehensive review of the High Efficiency Video Coding (HEVC) standard, currently under development by the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. The primary objective of the HEVC standard is to achieve a substantial enhancement in compression performance compared to current standards, aiming for a 50% reduction in bit-rate while maintaining equivalent perceptual video quality. Key technical attributes and distinctive features of the HEVC standard are elaborated upon, underscoring its potential for significant advancements in video coding efficiency.

This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.

We propose a new fast algorithm for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an l2 data-fidelity term and a nonsmooth regularizer. This formulation allows both wavelet-based (with orthogonal or frame-based representations) regularization or total-variation regularization. Our approach is based on a variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The proposed algorithm is an instance of the so-called alternating direction method of multipliers, for which convergence has been proved. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is faster than the current state of the art methods.

In the wide-angle (WA) images embedded in an around-view monitor system, the subject(s) in the peripheral region is normally small and has little information. Furthermore, since the outer region suffers from the non-uniform blur phenomenon and artifact caused by the inherent optical characteristic of WA lenses, its visual quality tends to deteriorate. According to our experiments, conventional image enhancement techniques rarely improve the degraded visual quality of the outer region of WA images. In order to solve the above-mentioned problem, this paper proposes a joint sharpness enhancement (SE) and super-resolution (SR) algorithm which can improve the sharpness and resolution of WA images together. The proposed SE algorithm improves the sharpness of the deteriorated WA images by exploiting self-similarity. Also, the proposed SR algorithm generates super-resolved images by using high-resolution information which is classified according to the extended local binary pattern-based classifier and learned on a pattern basis. Experimental results show that the proposed scheme effectively improves the sharpness and resolution of the input deteriorated WA images. Even in terms of quantitative metrics such as just noticeable blur, structural similarity, and peak signal-to-noise ratio. Finally, the proposed scheme guarantees real-time processing such that it achieves 720p video at 29 Hz on a low cost GPU platform.

Fog and haze degrade the quality of preview and captured image by reducing the contrast and saturation. As a result the visibility of scene or object degrades. The objective of the present work is to enhance the visibility, saturation, contrast and reduce the noise in a foggy image. We propose a method that uses single frame for enhancing foggy images using multilevel transmission map. The method is fast and free from noise or artifacts that generally arise in such enhancement techniques. A comparison with existing methods shows that the proposed method performs better in terms of both processing time and quality. The proposed method works in real time for VGA resolution. The proposed work also presents a scheme to remove fog, rain and snow in real-time.

Human observers can easily assess the quality of a distorted image without examining the original image as a reference. By contrast, designing objective No-Reference (NR) quality measurement algorithms is a very difficult task. Currently, NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This research aims to develop NR quality measurement algorithms for JPEG compressed images. First, we established a JPEG image database and subjective experiments were conducted on the database. We show that Peak Signalto-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality. Therefore, tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics. Furthermore, we propose a computational and memory efficient NR quality assessment model for JPEG images. Subjective test results are used to train the model, which achieves good quality prediction performance. A Matlab implementation of the proposed method is available at http://anchovy.ece.ute xas.edu/ ̃zwang/research/nr jpeg quality/index.html .

A novel filter is proposed for edge-preserving decomposition of an image. It is different from previous filters in its locally adaptive property. The filtered image contains local means everywhere and preserves local salient edges. Comparisons are made between our filtered result and the results of three other methods. A detailed analysis is also made on the behavior of the filter. A multiscale decomposition with this filter is proposed for manipulating a high dynamic range image, which has three detail layers and one base layer. The multiscale decomposition with the filter addresses three assumptions: 1) the base layer preserves local means everywhere; 2) every scale's salient edges are relatively large gradients in a local window; and 3) all of the nonzero gradient information belongs to the detail layer. An effective function is also proposed for compressing the detail layers. The reproduced image gives a good visualization. Experimental results on real images demonstrate that our algorithm is especially effective at preserving or enhancing local details.

With the advent of perceptual loss functions, new possibilities in super-resolution have emerged, and we currently have models that successfully generate near-photorealistic high-resolution images from their low-resolution observations. Up to now, however, such approaches have been exclusively limited to single image super-resolution. The application of perceptual loss functions on video processing still entails several challenges, mostly related to the lack of temporal consistency of the generated images, i.e., flickering artifacts. In this work, we present a novel adversarial recurrent network for video upscaling that is able to produce realistic textures in a temporally consistent way. The proposed architecture naturally leverages information from previous frames due to its recurrent architecture, i.e. the input to the generator is composed of the low-resolution image and, additionally, the warped output of the network at the previous step. We also propose an additional loss function to further reinforce temporal consistency in the generated sequences. The experimental validation of our algorithm shows the effectiveness of our approach which obtains competitive samples in terms of perceptual quality with improved temporal consistency.

This paper presents an image super-resolution method that enhances spatial resolution of MRI images in the slice-select direction. The algorithm employs Tikhonov regularization, using a standard model of general imaging process and then reformulating the reconstruction as a regularized minimization problem. Our experimental result shows improvements in both signal-to-noise ratio and visual quality.

The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on the Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.

An image compression is necessary for the image processing applications such as data storing, image classification, image recognition etc. Then, several research articles have been proposed to reserve for these topics. However, the image compression with auto encoder has been found for a small number of the improvements. Therefore, this paper presents a detailed study to demonstrate the image compression algorithm using the deep neural network (DNN). The proposed algorithm consists of 1) compressing image with auto encoder, and 2) decoding image. The proposed compressing image with auto encoder algorithm uses non-recurrent three-layer neural networks (NRTNNs) which use an extended Kalman filter (EKF) to update the weights of the networks. To evaluate the proposed algorithm performances, the Matlab program is used for implementations of the overall testing algorithm. From our simulation results, it shows that the proposed image compression algorithm is able to reduce the image dimensionality and is able to recall the compressed image with low loss.


Question: Standardized enhancements for High Efficiency Video Coding (HEVC) systems.

This article focuses on the implementation of H.264/AVC video compression technology over Internet Protocol (IP) networks. H.264, an advanced video compression standard from the International Telecommunication Union (ITU-T), offers superior performance compared to its predecessors. The standard features a Video Coding Layer (VCL) responsible for traditional signal processing and macroblock coding, and a Network Adaptation Layer (NAL) that optimizes these bit strings for network transmission. The paper explores the deployment of H.264-encoded video over best-effort IP networks, utilizing the Real-time Transport Protocol (RTP) for real-time data transfer. It begins by outlining the system environment, followed by an in-depth look at H.264's error-resilience mechanisms and the draft specification for the RTP payload format. Subsequently, the effectiveness of various error-resilience tools, both VCL and NAL-based, is evaluated through simulations.

**Question**: The Application of Convolutional Neural Networks in Speech Recognition

**Documents**: A Convolutional Recurrent Neural Network for Polyphonic Music Modeling
We present a novel deep learning model for polyphonic music modeling and generation, which combines convolutional and recurrent neural networks. The convolutional layers learn to extract features from raw music data, while the recurrent layers learn to model long-term temporal dependencies. We compare our model with a purely recurrent baseline on two music datasets, and show that our model outperforms the baseline in terms of log-likelihood and generated music quality. We also show that our model can generate coherent and diverse music samples, which demonstrates its potential for music generation.

**Similar Documents**: This paper introduces a groundbreaking deep learning model designed for polyphonic music modeling and creation, integrating convolutional neural networks (CNNs) with recurrent neural networks (RNNs). The CNN layers are tasked with extracting intricate features from raw music data, while the RNN components excel at capturing long-term temporal relationships within the music. Through comparative analysis on two distinct music datasets, the proposed model surpasses a purely recurrent baseline model in both log-likelihood and the quality of generated music. Additionally, the model's capability to produce coherent and varied musical compositions is showcased, highlighting its promising potential in the field of music generation.

An image compression is necessary for the image processing applications such as data storing, image classification, image recognition etc. Then, several research articles have been proposed to reserve for these topics. However, the image compression with auto encoder has been found for a small number of the improvements. Therefore, this paper presents a detailed study to demonstrate the image compression algorithm using the deep neural network (DNN). The proposed algorithm consists of 1) compressing image with auto encoder, and 2) decoding image. The proposed compressing image with auto encoder algorithm uses non-recurrent three-layer neural networks (NRTNNs) which use an extended Kalman filter (EKF) to update the weights of the networks. To evaluate the proposed algorithm performances, the Matlab program is used for implementations of the overall testing algorithm. From our simulation results, it shows that the proposed image compression algorithm is able to reduce the image dimensionality and is able to recall the compressed image with low loss.

Image processing is one of the most important areas of signal processing. Image processing is applicable for the improvement of pictorial improvement for human perception and autonomous machine application also the efficient storage and transmission. In this paper guided filter is proposed which can be applicable to both images as well as to video. This guided filter has many advantages over the previous version of filter like mean filter, median filter, sobel filter, bilateral filter, etc. Among of those bilateral filter is more popular. But this bilateral filter has certain limitations like gradient reversal effect this introduces false edges in the image. Proposed guided filter can smooth image/video and also preserved the edge that is information at the edges of image/video but this filter has better behaviors near edges. Also the fast implementation of guided filter is possible. This filter also has algorithm implementation regardless of kernel size. Guided filter gives better output than that of bilateral filter after filtering this bilateral filter gives output like cartoon this also a problem with this. The guided filter is both effective and efficient in many applications of computer vision and computer graphics, etc. Filtering techniques are used in image and video processing applications for various technologies.

The OpenCL standard offers a common API for program execution on systems composed of different types of computational devices such as multicore CPUs, GPUs, or other accelerators.

This paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K.

This article presents a high-level overview of the IEEE 802.22 standard for cognitive wireless regional area networks (WRANs) that is under development in the IEEE 802 LAN/MAN Standards Committee.

Contrast enhancement of radiographies based on a multiscale decomposition of the images recently has proven to be a far more versatile and efficient method than regular unsharp-masking techniques, while containing these as a subset. In this paper, we compare the performance of two multiscale-methods, namely the Laplacian Pyramid and the fast wavelet transform (FWT). We find that enhancement based on the FWT suffers from one serious drawback-the introduction of visible artifacts when large structures are enhanced strongly. By contrast, the Laplacian Pyramid allows a smooth enhancement of large structures, such that visible artifacts can be avoided. Only for the enhancement of very small details, for denoising applications or compression of images, the FWT may have some advantages over the Laplacian Pyramid.

Given a short video we create a representation that captures a spectrum of looping videos with varying levels of dynamism, ranging from a static image to a highly animated loop. In such a progressively dynamic video, scene liveliness can be adjusted interactively using a slider control. Applications include background images and slideshows, where the desired level of activity may depend on personal taste or mood. The representation also provides a segmentation of the scene into independently looping regions, enabling interactive local adjustment over dynamism. For a landscape scene, this control might correspond to selective animation and deanimation of grass motion, water ripples, and swaying trees. Converting arbitrary video to looping content is a challenging research problem. Unlike prior work, we explore an optimization in which each pixel automatically determines its own looping period. The resulting nested segmentation of static and dynamic scene regions forms an extremely compact representation.

Cloud-based multimedia systems are becoming increasingly common. These systems offer not only storage facility, but also high-end computing infrastructure which can be used to process data for various analysis tasks ranging from low-level data quality enhancement to high-level activity and behavior identification operations. However, cloud data centers, being third party servers, are often prone to information leakage, raising security and privacy concerns. In this article, we present a Shamir's secret sharing based method to enhance the quality of encrypted image data over cloud. Using the proposed method we show that several image enhancement operations such as noise removal, antialiasing, edge and contrast enhancement, and dehazing can be performed in encrypted domain with near-zero loss in accuracy and minimal computation and data overhead. Moreover, the proposed method is proven to be information theoretically secure.

A 352 288 pixel CMOS image sensor chip with perpixel single-slope ADC and dynamic memory in a standard digital 0.18m CMOS process is described. The chip performs “snapshot” image acquisition, parallel 8-bit A/D conversion, and digital readout at continuous rate of 10 000 frames/s or 1 Gpixels/s with power consumption of 50 mW. Each pixel consists of a photogate circuit, a three-stage comparator, and an 8-bit 3T dynamic memory comprising a total of 37 transistors in 9.4 9.4 m with a fill factor of 15%. The photogate quantum efficiency is 13.6%, and the sensor conversion gain is 13.1 V/e . At 1000 frames/s, measured integral nonlinearity is 0.22% over a 1-V range, rms temporal noise with digital CDS is 0.15%, and rms FPN with digital CDS is 0.027%. When operated at low frame rates, on-chip power management circuits permit complete powerdown between each frame conversion and readout. The digitized pixel data is read out over a 64-bit (8-pixel) wide bus operating at 167 MHz, i.e., over 1.33 GB/s. The chip is suitable for general high-speed imaging applications as well as for the implementation of several still and standard video rate applications that benefit from high-speed capture, such as dynamic range enhancement, motion estimation and compensation, and image stabilization.

SIMD vectorization has received significant attention in the past decade as an important method to accelerate scientific applications, media and embedded applications on SIMD architectures such as Intel® SSE, AVX, and IBM* AltiVec. However, most of the focus has been directed at loops, effectively executing their iterations on multiple SIMD lanes concurrently relying upon program hints and compiler analysis. This paper presents a set of new C/C++ high-level vector extensions for SIMD programming, and the Intel® C++ product compiler that is extended to translate these vector extensions and produce optimized SIMD instruction sequences of vectorized functions and loops. For a function, our main idea is to vectorize the entire function for callers instead of just vectorizing loops (if any) inside the function. It poses the challenge of dealing with complicated control-flow in the function body, and matching caller and callee for SIMD vector calls while vectorizing caller functions (or loops) and callee functions. Our compilation methods for automatically compiling vector extensions are described. We present performance results of several non-trivial visual computing, computational, and simulation workloads, utilizing SIMD units through the vector extensions on Intel® Multicore 128-bit SIMD processors, and we show that significant SIMD speedups (3.07x to 4.69x) are achieved over the serial execution.


Question: Standardized enhancements of High Efficiency Video Coding (HEVC) technology.

This paper presents a comprehensive analysis of the compression performance of various video coding standards, encompassing different generations, through the lens of peak signal-to-noise ratio (PSNR) and subjective testing outcomes. The study adopts a consistent methodology to evaluate the designs of H.262/MPEG-2 Video, H.263, MPEG-4 Visual, H.264/MPEG-4 Advanced Video Coding (AVC), and High Efficiency Video Coding (HEVC). Subjective evaluations conducted on WVGA and HD video sequences reveal that HEVC encoders are capable of achieving comparable subjective quality to that of H.264/MPEG-4 AVC encoders, but with a remarkable 50% reduction in average bit rate. The HEVC coding standard is particularly advantageous for scenarios requiring low bit rates, high-resolution video content handling, and real-time, low-latency communication applications. The subjective quality enhancement observed in HEVC surpasses the gains quantified by the PSNR metric, highlighting the standard's superiority in delivering enhanced visual experiences.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A novel direct search method for solving the economic dispatch problem with valve-point effect
In this paper, a new direct search method (DSM) is proposed for solving the economic dispatch (ED) problem with valve-point effect. The DSM is a derivative-free optimization algorithm that can handle nonlinear and non-convex functions. It searches for the optimal solution by exploring the feasible solution space in a systematic manner. The proposed method is applied to the ED problem, which is a non-convex optimization problem due to the valve-point effect. The DSM is able to find the global optimal solution by avoiding getting trapped in local minima. The effectiveness of the proposed method is demonstrated by comparing its performance with other optimization algorithms, such as the genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). The results show that the DSM outperforms these algorithms in terms of solution quality and computational efficiency.

**Similar Documents**: A pioneering direct search method (DSM) is introduced in this study to address the economic dispatch (ED) problem in the presence of the valve-point effect. The DSM, as a derivative-free optimization technique, is uniquely suited for tackling complex functions with nonlinear and non-convex characteristics. By methodically traversing the solution space, the DSM identifies the optimal solution for the ED problem, which is inherently non-convex due to the valve

Cloud-based multimedia systems are becoming increasingly common. These systems offer not only storage facility, but also high-end computing infrastructure which can be used to process data for various analysis tasks ranging from low-level data quality enhancement to high-level activity and behavior identification operations. However, cloud data centers, being third party servers, are often prone to information leakage, raising security and privacy concerns. In this article, we present a Shamir's secret sharing based method to enhance the quality of encrypted image data over cloud. Using the proposed method we show that several image enhancement operations such as noise removal, antialiasing, edge and contrast enhancement, and dehazing can be performed in encrypted domain with near-zero loss in accuracy and minimal computation and data overhead. Moreover, the proposed method is proven to be information theoretically secure.

We propose a deep learning method for single image superresolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the lowresolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.

The OpenCL standard offers a common API for program execution on systems composed of different types of computational devices such as multicore CPUs, GPUs, or other accelerators.

a Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China b Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada c Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, PR China d System Research Institute, Polish Academy of Sciences, Warsaw, Poland e School of Software, Jiangxi Agricultural University, Nanchang 330013, PR China

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (approx 2 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments.
 Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.

In this paper a new sign coding approximation method for the wavelet coefficients in a 2D image codec based on a simulated annealing metaheuristic is presented. The efficiency of the proposed algorithm versus a genetic algorithm using benchmarks of Kodak is compared and showing that the proposed sign prediction algorithm is efficient and provides a significant reduction of wavelet coefficients sign information in the final bit-stream. The results show that, by including sign coding capabilities to a nonembedded encoder, the sign compression gain is up to 17.35%, being the rate-distortion (R/D) performance improvement up to 0.25 dB.

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented using vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided.

Growing interest in super-resolution (SR) restoration of video sequences and the closely related problem of construction of SR still images from image sequences has led to the emergence of several competing methodologies. We review the state of the art of SR techniques using a taxonomy of existing techniques. We critique these methods and identify areas which promise performance improvements.

Sports video processing is an interesting topic for research, since the clearly defined game rules in sports provide the rich domain knowledge for analysis. Moreover, it is interesting because many specialized applications for sports video processing are emerging. This paper gives an overview of sports video research, where we describe both basic algorithmic techniques and applications.

Nonlinear image reconstruction based upon sparse representations of images has recently received widespread attention with the emerging framework of compressed sensing (CS). This theory indicates that, when feasible, judicious selection of the type of distortion induced by measurement systems may dramatically improve our ability to perform image reconstruction. However, applying compressed sensing theory to practical imaging systems poses a key challenge: physical constraints typically make it infeasible to actually measure many of the random projections described in the literature, and therefore, innovative and sophisticated imaging systems must be carefully designed to effectively exploit CS theory. In video settings, the performance of an imaging system is characterized by both pixel resolution and field of view. In this work, we propose compressive imaging techniques for improving the performance of video imaging systems in the presence of constraints on the focal plane array size. In particular, we describe a novel yet practical approach that combines coded aperture imaging to enhance pixel resolution with superimposing subframes of a scene onto a single focal plane array to increase field of view. Specifically, the proposed method superimposes coded observations and uses wavelet-based sparsity recovery algorithms to reconstruct the original subframes. We demonstrate the effectiveness of this approach by reconstructing with high resolution the constituent images of a video sequence.


Question: Standardized enhancements of High Efficiency Video Coding (HEVC) technology.

This paper offers an in-depth exploration of the quadtree and associated hierarchical data structures, focusing on their foundational principle of recursive decomposition. The primary spotlight is on the application of these structures in various domains, including image processing, computer graphics, geographic information systems, and robotics. The survey places a significant emphasis on region data, or two-dimensional shapes, while also discussing point, curvilinear, and three-dimensional data to a lesser degree. The document delves into several operations where these data structures prove particularly useful, providing detailed insights into their practical applications and highlighting their versatility across multiple fields.

**Question**: The Application of a Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO) in the Design of Recurrent Neural/Fuzzy Networks

**Documents**: A hybrid approach for optimizing the design of recurrent neural/fuzzy networks using a novel evolutionary learning algorithm, HGAPSO, is presented in this paper. HGAPSO combines the principles of genetic algorithms (GA) and particle swarm optimization (PSO) to create a new generation of individuals. The algorithm introduces the concept of elite strategy, where the best-performing individuals, termed elites, are not directly replicated in the next generation but are first enhanced through PSO. This enhancement process mimics natural maturation, refining the elites before they constitute half of the population in the subsequent generation. The other half is produced through crossover and mutation operations on these enhanced elites. HGAPSO is applied to the design of recurrent neural and fuzzy networks, showcasing its effectiveness in temporal sequence production and dynamic plant control problems. Comparative studies with GA and PSO highlight the superior performance of HGAPSO in recurrent network design.

**Similar Documents**: This paper introduces a groundbreaking hybrid evolutionary learning algorithm, HGAPSO, which optimizes the design of recurrent neural/fuzzy networks. HGAPSO innovatively merges genetic algorithms (GA) with particle swarm optimization (PSO), enabling the creation of a new generation of individuals that are not only the product of traditional crossover and mutation but also benefit from PSO's enhancement capabilities. A key feature of HGAPSO is its elite strategy, which involves the selection and enhancement of the top-performing individuals, known as elites, before they contribute to the next generation. This process of refinement mirrors natural maturation, ensuring that enhanced elites form half of the subsequent population, while the remaining individuals are generated through genetic operations on these elites. The algorithm's application in recurrent neural and fuzzy network design is demonstrated through its successful handling of temporal sequence production and dynamic plant control challenges. Comparative analyses with GA and PS

DVB-NGH (Digital Video Broadcasting - Next Generation Handheld) is the next generation technology for mobile TV broadcasting, which has been developed by the DVB project with the most advanced transmission technologies. DVB-NGH is the first broadcasting standard to incorporate multiple-input multiple-output (MIMO) as the key technology to overcome the Shannon limit of single antenna communications. MIMO techniques can be used to improve the robustness of the transmitted signal by exploiting the spatial diversity of the MIMO channel, but also to achieve increased data rates through spatial multiplexing. This article describes the benefits of MIMO that motivated its incorporation in DVB-NGH, reviews the MIMO schemes adopted, and discusses some aspects related to the deployment of MIMO networks in DVB-NGH. The article also provides a feature comparison with the multi-antenna techniques for 3GGP's LTE/LTE-Advanced for cellular networks. Finally, physical layer simulation results calibrated within the DVB-NGH standardization process are provided to illustrate the gain of MIMO for the next generation of mobile TV broadcasting.

Video and images acquired by a visual system are seriously degraded under hazy and foggy weather, which will affect the detection, tracking, and recognition of targets. Thus, restoring the true scene from such a foggy video or image is of significance. The main goal of this paper was to summarize current video and image defogging algorithms. We first presented a review of the detection and classification method of a foggy image. Then, we summarized existing image defogging algorithms, including image restoration algorithms, image contrast enhancement algorithms, and fusion-based defogging algorithms. We also presented current video defogging algorithms. We summarized objective image quality assessment methods that have been widely used for the comparison of different defogging algorithms, followed by an experimental comparison of various classical image defogging algorithms. Finally, we presented the problems of video and image defogging which need to be further studied. The code of all algorithms will be available at <;uri xlink:href="http://www.yongxu.org/lunwen.html" xlink:type="simple">http://www.yongxu.org/lunwen.html<;/uri>.

This paper introduces a new algorithm for enhancing image resolution from an image sequence. The approach we propose herein uses the integrating resampler for warping. The method is a direct computation, which is fundamentally different from the iterative back-projection approaches proposed in previous work. This paper shows that image-warping techniques may have a strong impact on the quality of image resolution enhancement. By coupling the degradation model of the imaging system directly into the integrating resampler, we can better approximate the warping characteristics of real sensors, which also significantly improve the quality of super-resolution images. Examples of super-resolutions are given for gray-scale images. Evaluations are made visuallyby comparing the resulting images and those using bi-linear resampling and back-projection and quantitativelyusing OCR as a fundamental measure. The paper shows that even when the images are qualitatively similar, quantitative differences appear in machine processing. q 2000 Elsevier Science B.V. All rights reserved. Keywords: Super-resolution; Imaging-consistent restoration/reconstruction; Integrating resampling; Integrating resampler; Quantitative evaluatio n; OCR; Bilinear resampling; Image reconstruction; Image restoration; Image warping 1. Background and introduction The basic premise of super-resolution is to combine images (and in some work scene/data models) to produce an image that has higher “resolution” than the original image. It is not sufficient to just resample the image—size is not the same as resolution. Increasing resolution could be viewed as either increasing the signal-to-noise ratio while keeping the size fixed and/or approximating the image at a larger size with reasonable approximations for frequencies higher than those representable at the original size. In either case, it requires additional information and assumptions. There are, of course, some fundamental limits on what can be gained; lens and photo-sites cause blur, and the discrete sampling causes frequencies above the Nyquist rate to alias. While in general the aliasing is theoretically impossible to separate, the problem occurs mostly along sharp “intensity edges”, so multiple images with slightly different imaging positions can be used to reduce its impact. There are two different groups of research that use the term super-resolution—one is based on multiple images, which will be considered within this paper; the other uses “scene/object” models. Early research on image-based super-resolution dates back to the work by Huang and Tsai [8]. They solved the problem in the frequency domain, but they disregarded sensor blurring. Furthermore, they assumed only inter-frame translations. Gross [7] solved the problem by first merging the low resolution images by interpolation and then obtaining the high-resolution image by deblurring the merged image. Again, only inter-frame translations were considered. Peleg and Keren [11,14] solved the problem by first estimating an initial guess of the high-resolution image, then simulating the imaging process and minimizing the difference between the observed and simulated low-resolution images. Irani and Peleg [9,10] used a back-projection method similar to that used in tomography to minimize the same difference. Bascle et al. [1] used the same back-projection method to minimize the difference between the predicted and actual images except that a simple motion blur model was included. All previous work ignores the impact of image warping techniques. In this paper, we show that warping techniques may have a strong impact on the quality of image resolution enhancement. Image warping has been around almost as long as image processing, allowing users to reshape the image geometry. Image warping requires the underlying image to be “resampled” at non-integer locations; hence, it requires image reconstruction. When the goal of warping is to produce output for human viewing, only moderately accurate image intensities are needed. In these cases, techniques Image and Vision Computing 18 (2000) 761–771 0262-8856/00/$ see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S0262-8856(99)00044-X www.elsevier.com/locate/imavis * Corresponding author. Tel.: 11-610-7584061. E-mail address:tboult@eecs.lehigh.edu (T.E. Boult). using bi-linear interpolation have been found sufficient. However, as a step for applications such as super-resolution, the precision of the warped intensity values is often important. For these problems, bi-linear image reconstruction may not be sufficient. This paper shows how the integrating resampler can be used to enhance image resolution. This builds upon the ideas of imaging-consistent restoration/reconstruction algorithms proposed in Ref. [2] and the integrating resampler proposed in Ref. [5]. A final contribution of this paper is a quantitative comparison and analysis of our techniques. In this paper, we use a character recognition rate based on a commercial OCR program. We chose this because it is easy to make it quantitative, and we consider this to be a good measure for other applications such as license-plate reading and other pattern recognition problems. In what follows in this paper, the image formation process and the relationship between restoration, reconstruction, and super-resolution is briefly reviewed in Section 2. The integrating resampler—an efficient method for warping using imaging-consistent reconstruction/restoration algorithms—is given in Section 3. This algorithm is well suited for today’s pipelined micro-processors. In addition, the integrating resampler can allow for modifications of the intensity values to better approximate the warping characteristics of real sensors. The super-resolution algorithm where we compare the integrating resampler with bi-linear resampling is presented in Section 4. Quantitative measurement of super-resolution imaging using OCR is shown in Section 5. 2. Image formation and image restoration In this section, we briefly review the image formation process as well as the sensor model proposed in Ref. [2]. Image formation is generally described as a sequence of filtering operations. See Fig. 1. Let f x; y be the intensity distribution of a scene at the front aperture of a lens. That distribution is acted upon by h1x; y; the blurring component of the lens, yielding f1x; y: A geometric distortion functionh2x; y is then applied to produce imagef2u; v: At this point, f2 strikes the image sensor where it undergoes additional blurring by a point spread functionh3u; v to generate image f3u; v: This blurring reflects the limitation of the sensor to accurately resolve each point without the influence of neighboring points. We choose to use a simple model wherein this blurring takes place within one pixel because for CCD and CID cameras, the physical boundaries between photo-sites generally allow only insignificant charge transfer between pixels. Imagef3 undergoes spatial sampling as it hits the discrete photo-sites in a CCD or CID camera. The combination of h3 with sampling is known as areas sampling. It reflects the finite size of the sampling area. If h3 is taken to be an impulse, then we have point sampling. This ideal, often assumed for theoretical considerations, is generally not true in practice. In either case, intensities in the sampled image Is are now defined only for integer values of u and v. The digital imageI u; v is obtained via an analog-todigital converter that quantizes the samples of Is. This completes the image formation process, leaving I u; v as the starting point for subsequent processing, including image reconstruction and restoration. Reconstruction and restoration start with I (and models for one or morehis), and seek to solve for one or more of the fjs. Recoveringf is known as image restoration and is of considerable interest in image processing. Note that recovering a functional form off2 is an intra-pixel restoration. Reconstruction limits itself to the problem of deriving the continuous functionf3. Restoration attempts to estimate the original input functionf2 (or f1). The goal of super-resolution is to recover a discrete approximation, with resolution higher than Is, to either f1 (plain super-resolution) orf (super-resolution with deblurring). Because super-resolution increases the signal-to-noise ratio, it significantly aids in the ill-condition problem of deblurring. 3. Imaging-consistency and the integrating resampler An algorithm is called imaging-consistent if it is the exact solution for some input function, which, according to the imaging model, would have generated the measured input. For image reconstruction, we achieve this by first restoring the image to yieldf2, then blurring it again by the pixel’s PSF. Details can be found in Ref. [2]. Only one-dimensional image models are presented herein; higher dimensions are treated separately. M.-C. Chiang, T.E. Boult / Image and Vision Computing 18 (2000) 761–771 762 Fig. 1. The image formation process and the relationship between restoration, reconstruction, and super-resolution. The first imaging-consistent method to consider is based on a piecewise quadratic model for the image. If we assume a Rect PSF filter (1 inside and 0 outside the pixel), the imaging consistent algorithm is easy to derive. To ensure that the function is continuous and that the method is local, we define the value of the reconstruction at the pixel boundarieski andki11 to be equal toEi andEi11: Any method of approximation could be used to compute Ei, though our examples will only include linear interpolation and cubic convolution. See [3, Section 2.3] for more details. Given the valuesEi at the pixel edges, an additional constraint, that the integral across the pixel must equal Vi, results in exactly three constraints: qi21=2  Ei ; qi1=2  Ei11; Z1=2 2 1=2 qix dx Vi : 1 From Eq. (1), one can det

A novel filter is proposed for edge-preserving decomposition of an image. It is different from previous filters in its locally adaptive property. The filtered image contains local means everywhere and preserves local salient edges. Comparisons are made between our filtered result and the results of three other methods. A detailed analysis is also made on the behavior of the filter. A multiscale decomposition with this filter is proposed for manipulating a high dynamic range image, which has three detail layers and one base layer. The multiscale decomposition with the filter addresses three assumptions: 1) the base layer preserves local means everywhere; 2) every scale's salient edges are relatively large gradients in a local window; and 3) all of the nonzero gradient information belongs to the detail layer. An effective function is also proposed for compressing the detail layers. The reproduced image gives a good visualization. Experimental results on real images demonstrate that our algorithm is especially effective at preserving or enhancing local details.

This paper represents a twofold approach. On the one hand, we introduce a simple method that encompasses an ensemble of image processing algorithms with a multilevel color transfer approach at its core. On the other hand, the method is applied for providing an artistic look to standard images. The approach proposes a multilevel color transfer in a chromatic channel of the CIELAB color space. Once converted from red, green and blue, a specific channel on both images, input and target (reference), is thresholded in a number of levels. Later, the color transfer is performed between regions from corresponding levels using a classical color transfer method. In the application phase, the color palette of a recognized artwork of the Fauve movement is mapped to the input image, emulating the sight of the artist, characterized by the use of vivid colors. Filtering techniques are applied to the outcome, in order to emulate the basic brushstrokes of the artist. Experimental results are shown, visualizing and comparing the input images with the outcomes.

Image captured under various environment conditions with low contrast results in visual deterioration. To enhance the contrast of images a number of techniques have been proposed over the years. Histogram equalization is one of those methods which could be used for this purpose. For the contrast enhancement purpose histogram equalization (HE) attempts reduction in the number of gray levels. Contrast enhancement by histogram equalization method involve mapping of gray levels on the basis of changeover function that is derived from the probability distribution of gray levels embedded in input image. For this, the neighbouring gray levels in image having low probability density values are combined into single gray level value. Whereas for neighbouring gray levels having high probability density value the gap between them is increased, HE often makes over-enhancement of images with frequent gray level region resulting in loss of contrast for less frequent area of the images. Thus, the HE technique is not able to preserve image brightness. So wherever the preservation of image brightness is required this method is not preferred. This paper review various histogram equalization techniques used in preserving image brightness as well as the contrast enhancement is presented. The key approach for this aim is to use image segmentation. The criteria used are decomposition of images and the presentation of the basic difference between these techniques. For comparative study of performance of these techniques, absolute mean brightness error (AMBE) is used to determine degree of brightness preservation while peak signal to noise ratio (PSNR) have been used to fine\ degree of contrast enhancement.

Due to the development of economy and technology, the people's demands on cars are growing and so are the problems, such as finding stolen car, banning violation and parking lot management. It will be time-consuming and low-efficiency if we do those jobs only by human because of the limitation of human being's concentration. Therefore, it has been a popular topic to develop intelligent monitoring system under new video technology within a decade. There are still many rooms for future development and application in the car license detection and recognition fields. For finding stolen car, we can integrate car license detection system and road monitoring system to analyze the videos and trace the objects, so we can gain high-efficiency and low-cost results. For parking lot management system, we can have the result of access management and automatic charge to reduce the human resource through car license recognition system. Automated toll of highway can be done through car license recognition system as well. Because the car license recognition system is mostly applied to security monitoring field and business purposes, the demand of the accuracy is quite strict. There are causes which make the inaccuracy of the license recognition, such as the lack of video resolution, too small license due to the distance and the light and shadow. We will discuss the license recognition system and how to use apply the super-resolution to overcome those above problems.

Given a short video we create a representation that captures a spectrum of looping videos with varying levels of dynamism, ranging from a static image to a highly animated loop. In such a progressively dynamic video, scene liveliness can be adjusted interactively using a slider control. Applications include background images and slideshows, where the desired level of activity may depend on personal taste or mood. The representation also provides a segmentation of the scene into independently looping regions, enabling interactive local adjustment over dynamism. For a landscape scene, this control might correspond to selective animation and deanimation of grass motion, water ripples, and swaying trees. Converting arbitrary video to looping content is a challenging research problem. Unlike prior work, we explore an optimization in which each pixel automatically determines its own looping period. The resulting nested segmentation of static and dynamic scene regions forms an extremely compact representation.

We present a new method for compact representation of large image datasets. Our method is based on treating small patches from a 2-D image as matrices as opposed to the conventional vectorial representation, and encoding these patches as sparse projections onto a set of exemplar orthonormal bases, which are learned a priori from a training set. The end result is a low-error, highly compact image/patch representation that has significant theoretical merits and compares favorably with existing techniques (including JPEG) on experiments involving the compression of ORL and Yale face databases, as well as a database of miscellaneous natural images. In the context of learning multiple orthonormal bases, we show the easy tunability of our method to efficiently represent patches of different complexities. Furthermore, we show that our method is extensible in a theoretically sound manner to higher-order matrices (¿tensors¿). We demonstrate applications of this theory to compression of well-known color image datasets such as the GaTech and CMU-PIE face databases and show performance competitive with JPEG. Lastly, we also analyze the effect of image noise on the performance of our compression schemes.

Common method in image enhancement that's often use is histogram equalization, due to this method is simple and has low computation load. In this research, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the color retinal image. To reduce this noise effect in color retinal image due to the acquisition process, we need to enhance this image. Color retinal image has unique characteristic than other image, that is, this image has important in green (G) channel. Image enhancement has important contribution in ophthalmology. In this paper, we propose new enhancement method using CLAHE in G channel to improve the color retinal image quality. The enhancement process conduct in G channel is appropriate to enhance the color retinal image quality.


Question: Automated fruit identification and tallying from various images.

This paper explores the development of an advanced feature set for robust visual recognition of objects, with a focus on human detection using linear SVM. We critically evaluate various edge and gradient-based descriptors, demonstrating that a grid of histograms of oriented gradients (HOG) descriptors outperforms traditional feature sets in human detection tasks. Through a series of experiments, we identify key factors contributing to optimal performance: the use of fine-scale gradients, precise orientation binning, coarser spatial binning, and the application of high-quality local contrast normalization within overlapping descriptor blocks. These enhancements result in near-perfect separation on the MIT pedestrian database. To further test the robustness of our approach, we introduce a more challenging dataset consisting of over 1800 annotated human images that encompass a wide range of poses and diverse backgrounds. This work not only advances the state of the art in human detection but also lays a solid foundation for the application of HOG descriptors in various object recognition tasks, including automatic fruit recognition and counting from multiple images.

This paper describes the UJI Librarian Robot, a mobile manipulator that is able to autonomously locate a book in an ordinary library, and grasp it from a bookshelf, by using eye-in-hand stereo vision and force sensing. The robot is only provided with the book code, a library map and some knowledge about its logical structure and takes advantage of the spatio-temporal constraints and regularities of the environment by applying disparate techniques such as stereo vision, visual tracking, probabilistic matching, motion estimation, multisensor-based grasping, visual servoing and hybrid control, in such a way that it exhibits a robust and dependable performance. The system has been tested, and experimental results show how it is able to robustly locate and grasp a book in a reasonable time without human intervention.

Recently, a new ICT approach to agriculture called “Smart Agriculture” has been received great attention to support farmers' decision-making for good final yield on various kinds of field conditions. For this purpose, this paper presents two image sensing methods that enable an automatic observation to capture flowers and seedpods of soybeans in real fields. The developed image sensing methods are considered as sensors in an agricultural cyber-physical system in which big data on the growth status of agricultural plants and environmental information (e.g., weather, temperature, humidity, solar radiation, soil condition, etc.) are analyzed to mine useful rules for appropriate cultivation. The proposed image sensing methods are constructed by combining several image processing and machine learning techniques. The flower detection is realized based on a coarse-to-fine approach where candidate areas of flowers are first detected by SLIC and hue information, and the acceptance of flowers is decided by CNN. In the seedpod detection, candidates of seedpod regions are first detected by the Viola-Jones object detection method, and we also use CNN to make a final decision on the acceptance of detected seedpods. The performance of the proposed image sensing methods is evaluated for a data set of soybean images that were taken from a crowd of soybeans in real agricultural fields in Hokkaido, Japan.

I ncreasingly powerful integrated circuits are making an entire range of new applications possible. Complementary metal-oxide semiconductor (CMOS) sensors, for example, have made the digital camera a commonplace consumer item. These light-sensitive chips, positioned where film would normally be, capture images as reusable digital files that users can upload to their computer, manipulate with software, and distribute electronically. Recent technological advances are enabling a new generation of smart cameras that represent a quantum leap in sophistication. While today's digital cameras capture images, smart cameras capture high-level descriptions of the scene and analyze what they see. These devices could support a wide variety of applications including human and animal detection, surveillance, motion analysis, and facial identification. Video processing has an insatiable demand for real-time performance. Fortunately, Moore's law provides an increasing pool of available computing power to apply to real-time analysis. Smart cameras leverage very large-scale integration (VLSI) to provide such analysis in a low-cost, low-power system with substantial memory. Moving well beyond pixel processing and compression, these systems run a wide range of algorithms to extract meaning from streaming video. Because they push the design space in so many dimensions, smart cameras are a leading-edge application for embedded system research. has developed a first-generation smart camera system that can detect people and analyze their movement in real time. Although there are many approaches to real-time video analysis, we chose to focus initially on human gesture recognition—identifying whether a subject is walking, standing, waving his arms, and so on. Because much work remains to be done on this problem, we sought to design an embedded system that can incorporate future algorithms as well as use those we created exclusively for this application. As Figure 1 shows, our algorithms use both low-level and high-level processing. The low-level component identifies different body parts and categorizes their movement in simple terms. The high-level component, which is application-dependent, uses this information to recognize each body part's action and the person's overall activity based on scenario parameters. The system captures images from the video input, which can be either uncompressed or compressed (MPEG and motion JPEG), and applies four different algorithms to detect and identify human body parts. Region extraction. The first algorithm transforms the pixels of an image, like that shown in Figure 2a, into an M × N bitmap and eliminates the background. It then detects the body part's skin area using a YUV …

We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sharing of such annotations. Using this annotation tool, we have collected a large dataset that spans many object categories, often containing multiple instances over a wide variety of images. We quantify the contents of the dataset and compare against existing state of the art datasets used for object recognition and detection. Also, we show how to extend the dataset to automatically enhance object labels with WordNet, discover object parts, recover a depth ordering of objects in a scene, and increase the number of labels using minimal user supervision and images from the web.

• Lighting. When an object is in bright light, it looks brighter than when it’s in shadow, so a program can’t just look at image intensity values. • Within-class variation. Different instances of the same kind of object can look quite different to one another. For example, a green station wagon and a red convertible are both cars, so a program can’t simply compare a picture to one example. • Aspect. The same object can look very different when viewed at from different directions—pick up a book and compare its cover and its spine to see this effect. Again, this means that a program might need to have many examples of each type of object. • Deformation. Many objects can change their appearance significantly without their identity changing. For example, you can move your limbs around, change clothes, paint your face, or have your hair cut. You will look very different indeed, but you will still be a person.

Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable and accurate weed detection to minimize damage to surrounding plants. In this letter, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder–decoder cascaded convolutional neural network, SegNet, that infers dense semantic classes while allowing any number of input image channels and class balancing with our sugar beet and weed datasets. To obtain training datasets, we established an experimental field with varying herbicide levels resulting in field plots containing only either crop or weed, enabling us to use the normalized difference vegetation index as a distinguishable feature for automatic ground truth generation. We train six models with different numbers of input channels and condition (fine tune) it to achieve  $\sim$0.8 F1-score and 0.78 area under the curve classification metrics. For the model deployment, an embedded Graphics Processing Unit (GPU) system (Jetson TX2) is tested for MAV integration. Dataset used in this letter is released to support the community and future work.

Plant disease analysis is one of the critical tasks in the field of agriculture. Automatic identification and classification of plant diseases can be supportive to agriculture yield maximization. In this paper we compare performance of several Machine Learning techniques for identifying and classifying plant disease patterns from leaf images. A three-phase framework has been implemented for this purpose. First, image segmentation is performed to identify the diseased regions. Then, features are extracted from segmented regions using standard feature extraction techniques. These features are then used for classification into disease type. Experimental results indicate that our proposed technique is significantly better than other techniques used for Plant Disease Identification and Support Vector Machines outperforms other techniques for classification of diseases.

Plant diseases are one of the major factors that can negatively impact crop yields. Therefore, we want to create an automated system that can identify plant diseases as easily as possible. Our research goal is to help crop growers who might not be experts in plant pathology be able to identify diseases as soon as they occur in order to minimize the spread of disease and the damage to the crops. We propose a very light-weight convolutional neural network architecture that is trained to classify images of corn leaves into one of the four categories: common rust disease, northern leaf blight disease, gray leaf spot disease, or healthy. The dataset used is the PlantVillage dataset taken from plantvillage.org. The dataset consists of 8506 images of corn leaves from 4 categories. We evaluated our model using 5-fold cross-validation, and we achieves an accuracy of 97.09 % on average. The result of our model is slightly worse than the performance of other well-known CNNs architectures, but the complexity of our architecture is much lower, making it more suitable for real-time inferences on resource-constrained devices such as smartphones.

In this research, an algorithm for disease spot segmentation using image processing techniques in plant leaf is implemented. This is the first and important phase for automatic detection and classification of plant diseases. Disease spots are different in color but not in intensity, in comparison with plant leaf color. So we color transform of RGB image can be used for better segmentation of disease spots. In this paper a comparison of the effect of CIELAB, HSI and YCbCr color space in the process of disease spot detection is done. Median filter is used for image smoothing. Finally threshold can be calculated by applying Otsu method on color component to detect the disease spot. An algorithm which is independent of background noise, plant type and disease spot color was developed and experiments were carried out on different “Monocot” and “Dicot” family plant leaves with both, noise free (white) and noisy background.

A method is described which quantifies the speed and direction of several moving objects in a sequence of digital images. A relationship between the time variation of intensity, the spatial gradient, and velocity has been developed which allows the determination of motion using clustering techniques. This paper describes these relationships, the clustering technique, and provides examples of the technique on real images containing several moving objects.


Question: Automated fruit identification and tallying from a series of images.

This research tackles the intricate challenge of pedestrian detection within densely populated urban environments, where significant occlusions and overlaps occur. We posit that no single model or feature can sufficiently address this complexity. Therefore, we propose an algorithm that iteratively integrates evidence from various sources to enhance detection accuracy. Central to our methodology is the synthesis of local and global cues through a probabilistic top-down segmentation technique. This enables us to meticulously examine and compare object hypotheses at the pixel level, achieving a high degree of precision. Extensive testing on a comprehensive data set validates our method's efficacy in consistently identifying pedestrians amidst crowded scenes, even in scenarios involving overlap and partial occlusion. Moreover, the adaptable nature of our approach facilitates its operation on minimal training data, underscoring its practicality and efficiency.

**Question**: The role of social media in promoting mental health awareness

**Documents**: The effects of social media on mental health
Social media has become an integral part of modern society, influencing various aspects of human life, including mental health. While social media platforms offer numerous benefits, such as connecting people across the globe and providing a platform for self-expression, they also have potential drawbacks. Studies have shown that excessive use of social media can lead to increased feelings of anxiety, depression, and loneliness. However, social media can also serve as a powerful tool for mental health awareness and support. It provides a space for individuals to share their experiences, seek advice, and connect with others who are facing similar challenges. Moreover, social media platforms can be used to disseminate information about mental health resources and services, which can help to reduce stigma and improve access to care.

**Similar Documents**: In the contemporary digital age, social media platforms have permeated every facet of human interaction, significantly impacting mental well-being. While these platforms offer unparalleled connectivity and self-expression opportunities, they also present potential hazards. Research indicates that prolonged social media engagement can exacerbate feelings of anxiety, depression, and social isolation. Yet, the role of social media in mental health advocacy and support cannot be understated. These digital spaces offer a communal ground for individuals to share personal stories, seek guidance, and forge connections with others facing analogous struggles. Additionally, social media serves as a valuable conduit for disseminating mental health resources and information, thereby demystifying mental health issues and enhancing access to care. This dual-edged nature of social media underscores the need for balanced usage and proactive measures to mitigate its negative impacts while capitalizing on its potential for positive mental health outcomes.

With the Agriculture Sector being the backbone of not only a large number of industries but also society as a whole, there is a rising need to grow good quality crops which in turn will give a high yield. For this to happen, it is crucial to monitor the crops throughout their growth period. In this paper, Image processing is used to detect and classify sunflower crop diseases based on the image of their leaf. The images are taken through a high resolution digital camera and after preprocessing, are subjected to k-means clustering to get the diseased part of the leaf. These are then run through the various machine learning algorithms and classified based on their color and texture features. A comparison based on accuracy between various machine learning algorithms is done namely K-Nearest Neighbors, Multi-Class Support Vector Machine, Naive Bayes and Multinomial Logistic Regression to achieve maximum accuracy. The implementation has been done using MATLAB.

Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief (Dean et al., 2012) implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over 96% accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-the-art and achieve 97.84% accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over 90% accuracy. Our evaluations further indicate that at specific operating thresholds, the performance of the proposed system is comparable to that of human operators. To date, our system has helped us extract close to 100 million physical street numbers from Street View imagery worldwide.

Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have progressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator activities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and increases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible robotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which a number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system architecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit the integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated architecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots and advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet, which comprises three ground mobile units based on a commercial tractor chassis.

Depalletizing is a challenging task for manipulation robots. Key to successful application are not only robustness of the approach, but also achievable cycle times in order to keep up with the rest of the process. In this paper, we propose a system for depalletizing and a complete pipeline for detecting and localizing objects as well as verifying that the found object does not deviate from the known object model, e.g., if it is not the object to pick. In order to achieve high robustness (e.g., with respect to different lighting conditions) and generality with respect to the objects to pick, our approach is based on multi-resolution surfel models. All components (both software and hardware) allow operation at high frame rates and, thus, allow for low cycle times. In experiments, we demonstrate depalletizing of automotive and other prefabricated parts with both high reliability (w.r.t. success rates) and efficiency (w.r.t. low cycle times).

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.

Automatic identification and recognition of medicinal plant species in environments such as forests, mountains and dense regions is necessary to know about their existence. In recent years, plant species recognition is carried out based on the shape, geometry and texture of various plant parts such as leaves, stem, flowers etc. Flower based plant species identification systems are widely used. While modern search engines provide methods to visually search for a query image that contains a flower, it lacks in robustness because of the intra-class variation among millions of flower species around the world. Hence in this proposed research work, a Deep learning approach using Convolutional Neural Networks (CNN) is used to recognize flower species with high accuracy. Images of the plant species are acquired using the built-in camera module of a mobile phone. Feature extraction of flower images is performed using a Transfer Learning approach (i.e. extraction of complex features from a pre-trained network). A machine learning classifier such as Logistic Regression or Random Forest is used on top of it to yield a higher accuracy rate. This approach helps in minimizing the hardware requirement needed to perform the computationally intensive task of training a CNN. It is observed that, CNN combined with Transfer Learning approach as feature extractor outperforms all the handcrafted feature extraction methods such as Local Binary Pattern (LBP), Color Channel Statistics, Color Histograms, Haralick Texture, Hu Moments and Zernike Moments. CNN combined with Transfer Learning approach yields impressive Rank-1 accuracies of 73.05%, 93.41% and 90.60% using OverFeat, Inception-v3 and Xception architectures, respectively as Feature Extractors on FLOWERS102 dataset.

Pl@ntNet is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool.

Effectiveness of cutting is measured by the ability to achieve material fracture with smooth knife movements. The work performed by the knife overcomes the material toughness, acts against the blade-material friction, and generates shape deformation. This paper studies how to control a 2-DOF robotic arm equipped with a force/torque sensor to cut through an object in a sequence of three moves: press, push, and slice. For each move, a separate control strategy in the Cartesian space is designed to incorporate contact and/or force constraints while following some prescribed trajectory. Experiments conducted over several types of natural foods have demonstrated smooth motions like would be commanded by a human hand.

In this paper, the recognition of pollen bearing honey bees from videos of the entrance of the hive is presented. This computer vision task is a key component for the automatic monitoring of honeybees in order to obtain large scale data of their foraging behavior and task specialization. Several approaches are considered for this task, including baseline classifiers, shallow Convolutional Neural Networks, and deeper networks from the literature. The experimental comparison is based on a new dataset of images of honeybees that was manually annotated for the presence of pollen. The proposed approach, based on Convolutional Neural Networks is shown to outperform the other approaches in terms of accuracy. Detailed analysis of the results and the influence of the architectural parameters, such as the impact of dedicated color based data augmentation, provide insights into how to apply the approach to the target application.

We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin-images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes. This research was performed at Carnegie Mellon University and was supported by the US Department of Energy under contract DE-AC21-92MC29104. 1