Question: Investigating romantic relationships through social networking platforms with the self-expansion model.

This study investigates the dichotomy of information disclosure and control on Facebook, a leading social networking platform, and its relationship with personality traits. Despite widespread media coverage highlighting the risks associated with sharing personal information on social media, particularly on Facebook, young adults, including undergraduate students, often appear indifferent to the potential downsides. The research aimed to understand how these students manage their personal information on Facebook and the psychological factors that influence their behavior. A total of 343 Facebook-active undergraduate students participated in an online survey. Findings revealed that participants acknowledged disclosing more personal details on Facebook than they would in other settings, yet they also expressed a strong concern for information control and privacy. Most respondents had shared basic personal information like their birthday and email address and had joined online networks. Additionally, they frequently posted pictures, including profile images, group photos, and even candid shots from social events. Contrary to the initial hypothesis, the study found that information disclosure and control were not inversely related; instead, they were distinct processes influenced by separate personality facets. Specifically, the need for popularity was a significant predictor of disclosure, whereas trust and self-esteem were key factors in information control. These results suggest that online behavior on platforms like Facebook is nuanced, with disclosure and control being driven by different psychological dynamics. The study's implications and recommendations for future research are discussed, underscoring the need for a deeper understanding of how personality shapes online information management.

There is a general consensus that Internet addiction (IA) is mainly related to social aspects of the Web, especially among adolescents. The empirical link between poor social skills and IA is well documented; however, theoretical explanations for this relationship are less developed. One possibility is that people with poor social skills are especially prone to develop a preference for online social interaction (POSI), which, in turn, predicts problematic usage. This hypothesis has been tested for loneliness and social anxiety, but not for self-esteem (SE; one of the main antecedents of IA). Furthermore, the mediating role of the perceived relevance of some Internet features (e.g., anonymity) in the relationship between SE and POSI has never been investigated. A cross-sectional study was conducted with 257 adolescents. Using mediation analyses, we found evidence among females for the mediating role of (a) POSI in the relationship between SE and IA, and (b) the subjective relevance of some Internet features in the association between SE and POSI. No significant effects were found for males.

The model of problematic Internet use advanced and tested in the current study proposes that individuals' psychosocial well-being, along with their beliefs about interpersonal communication (both face-to-face and online) are important cognitive predictors of negative outcomes arising from Internet use. The study examined the extent to which social anxiety explains results previously attributed to loneliness as a predictor of preference for online social interaction and problematic Internet use. The results support the hypothesis that the relationship between loneliness and preference for online social interaction is spurious, and that social anxiety is the confounding variable.

Social networks form the backbone of social and economic life. Until recently, however, data have not been available to study the social impact of a national network structure. To that end, we combined the most complete record of a national communication network with national census data on the socioeconomic well-being of communities. These data make possible a population-level investigation of the relation between the structure of social networks and access to socioeconomic opportunity. We find that the diversity of individuals' relationships is strongly correlated with the economic development of communities.

Policy makers are calling for new socio-economic measures that reflect subjective well-being, to complement traditional measures of material welfare as the Gross Domestic Product (GDP). Self-reporting has been found to be reasonably accurate in measuring one's well-being and conveniently tallies with sentiment expressed on social media (e.g., those satisfied with life use more positive than negative words in their Facebook status updates). Social media content can thus be used to track well-being of individuals. A question left unexplored is whether such content can be used to track well-being of entire physical communities as well. To this end, we consider Twitter users based in a variety of London census communities, and study the relationship between sentiment expressed in tweets and community socio-economic well-being. We find that the two are highly correlated: the higher the normalized sentiment score of a community's tweets, the higher the community's socio-economic well-being. This suggests that monitoring tweets is an effective way of tracking community well-being too.

This article explores the relationship between Internet use and the individual-level production of social capital. To do so, the authors adopt a motivational perspective to distinguish among types of Internet use when examining the factors predicting civic engagement, interpersonal trust, and life contentment. The predictive power of new media use is then analyzed relative to key demographic, contextual, and traditional media use variables using the 1999 DDB Life Style Study. Although the size of associations is generally small, the data suggest that informational uses of the Internet are positively related to individual differences in the production of social capital, whereas social-recreational uses are negatively related to these civic indicators. Analyses within subsamples defined by generational age breaks further suggest that social capital production is related to Internet use among Generation X, while it is tied to television use among Baby Boomers and newspaper use among members of the Civic Generation. The possibility of life cycle and cohort effects is discussed.

People have long sought answers to questions online, typically using either anonymous or pseudonymous forums or social network platforms that primarily use real names. Systems that allow anonymous communication afford freedom to explore identity and discuss taboo topics, but can result in negative disinhibited behavior such as cyberbullying. Identifiable communication systems allows one to reach a known audience and avoid negative disinhibition, but can constrain behavior with concerns about privacy and reputation. One persistent design issue is understanding how to leverage the benefits of anonymity without suffering its drawbacks. This paper presents a case study analysis of question asking on Facebook confession boards (FCBs), a tool popular on some college campuses. FCBs present a unique configuration in which members of an offline community (e.g., a university) anonymously submit content to a moderator who posts it to a Facebook page where others in the community can view it and respond. Response is via identifiable Facebook comments and likes. Our results show users asking about taboo and stigmatized topics with local others, and receiving relevant responses with little cyberbullying or negativity.

Our vision is to achieve faster and near real time detection and prediction of the emergence and spread of an influenza epidemic, through sophisticated data collection and analysis of Online Social Networks (OSNs) such as Facebook, MySpace, and Twitter. In particular, we present the design of a system called SNEFT (Social Network Enabled Flu Trends), which will be developed in a 12-month SBIR (Small Business Innovation Research) project funded by the National Institutes of Health (NIH). We describe the innovative technologies that will be developed in this project for collecting and aggregating OSN data, extracting information from it, and integrating it with mathematical models of influenza. One of the monitoring tools used by the Centers for Disease Control and Prevention (CDC) is reports of Influenza-Like Illness (ILI) cases; these reports are authoritative but typically have a delay of one to two weeks due to the largely manual process. We describe the SNEFT prototype in the context of predicting ILI cases well in advance of the CDC reports. We observe that OSN data is individually noisy but collectively revealing, and speculate on other applications that can potentially be enabled by OSN data collection and analysis.

The present study used adult attachment as a theoretical framework to investigate individual differences in young adults’ romantic relationships. Emphasis was put on studying the relational aspects of the self and coping strategies in an attempt to better understand how romantic partners feel about themselves and how they subsequently behave in periods of conflict. The sample comprised undergraduate university students (N=377) who responded to a self report questionnaire in English. Most findings were consistent with the proposed hypotheses. Results indicated that anxious individuals tend to report an ambivalent coping style and that fearful individuals were most vulnerable to health risks such as depression. Gender differences were also explored and results showed for instance that women generally tend to seek more support, and men might be more dismissing than women.

Social networking communities have become an important communications platform, but the popularity of these communities has also made them targets for a new breed of social spammers. Unfortunately, little is known about these social spammers, their level of sophistication, or their strategies and tactics. Thus, in this paper, we provide the first characterization of social spammers and their behaviors. Concretely, we make two contributions: (1) we introduce social honeypots for tracking and monitoring social spam, and (2) we report the results of an analysis performed on spam data that was harvested by our social honeypots. Based on our analysis, we find that the behaviors of social spammers exhibit recognizable temporal and geographic patterns and that social spam content contains various distinguishing characteristics. These results are quite promising and suggest that our analysis techniques may be used to automatically identify social spam.

Integrating social media into the innovation process can open up the potential for organizations to utilize the collective creativity of consumers from all over the world. The research in this paper sets out to identify how social media can facilitate innovation. By taking a Design Science Research approach this research presents the Social Media Innovation Method for matching innovation tasks with social media characteristics. This supports the selection of best suitable social media and can help organizations to achieve their innovation goals. At the core of the method is the honeycomb model which describes seven social media characteristics on three dimensions: audience, content and time. The method has been evaluated by using an approach called scenario walkthrough that is applied in a real-life spatial planning project. This research concludes that there is no one-size-fits-all answer to the question how social media can be of value for the innovation process. However, organizations that want to know how it can benefit their own innovation process can use the Social Media Innovation Method presented in this research as a way to provide an answer to that question, uniquely tailored to each innovation task for which social media is to be used.


Question: Investigating romantic relationships on social media platforms through the lens of the self-expansion model.

Beyond Bowling Together: SocioTechnical Capital and the Enhancement of Social Connections
In the past, social capital—the trust and shared identity that facilitate collaborative endeavors—was often cultivated through participation in community groups and social events, such as bowling leagues. However, contemporary society sees a significant decline in these traditional avenues for building social capital (Putnam, 2000). The challenge for Human-Computer Interaction (HCI) researchers and professionals is to identify innovative methods for fostering social capital that surpass the levels achieved through bowling leagues and similar activities. To address this, the concept of SocioTechnical Capital emerges as a theoretical framework. This framework not only assesses but also promotes the generation of social capital through technology-mediated interactions. By examining how technology can enhance social relations, SocioTechnical Capital provides a blueprint for designing and evaluating digital platforms that aim to strengthen social bonds in a modern context.

This research draws on longitudinal network data from an online community to examine patterns of users’ behavior and social interaction, and infer the processes underpinning dynamics of system use. The online community represents a prototypical example of a complex evolving social network in which connections between users are established over time by online messages. We study the evolution of a variety of properties since the inception of the system, including how users create, reciprocate, and deepen relationships with one another, variations in users’ gregariousness and popularity, reachability and typical distances among users, and the degree of local redundancy in the system. Results indicate that the system is a “small world”characterized by the emergence, in its early stages,of a hub-dominated structure with heterogeneity in users’ behavior. We investigate whether hubs are responsible for holding the system together and facilitating information flow, examine first-mover advantages underpinning users’ability to rise to system prominence, and uncover gender differences in users’ gregariousness, popularity, and local redundancy. We discuss the implications of the results for research on system use and evolving social networks, and for a host of applications, including information diffusion, communities of practice, and the security and robustness of information systems.

How does online technology affect sociability? Emerging evidence-much of it inconclusive-suggests a nuanced relationship between use of online technology (the Internet, social media, and virtual reality) and sociability (emotion recognition, empathy, perspective taking, and emotional intelligence). Although online technology can facilitate purely positive behavior (e.g., charitable giving) or purely negative behavior (e.g., cyberbullying), it appears to affect sociability in three ways, depending on whether it allows a deeper understanding of people's thoughts and feelings: (a) It benefits sociability when it complements already-deep offline engagement with others, (b) it impairs sociability when it supplants deeper offline engagement for superficial online engagement, and (c) it enhances sociability when deep offline engagement is otherwise difficult to attain. We suggest potential implications and moderators of technology's effects on sociability and call for additional causal research.

Follower count is important to Twitter users: it can indicate popularity and prestige. Yet, holistically, little is understood about what factors -- like social behavior, message content, and network structure - lead to more followers. Such information could help technologists design and build tools that help users grow their audiences. In this paper, we study 507 Twitter users and a half-million of their tweets over 15 months. Marrying a longitudinal approach with a negative binomial auto-regression model, we find that variables for message content, social behavior, and network structure should be given equal consideration when predicting link formations on Twitter. To our knowledge, this is the first longitudinal study of follow predictors, and the first to show that the relative contributions of social behavior and mes-sage content are just as impactful as factors related to social network structure for predicting growth of online social networks. We conclude with practical and theoretical implications for designing social media technologies.

With social media datasets being increasingly shared by researchers, it also presents the caveat that those datasets are not always completely replicable. Having to adhere to requirements of platforms like Twitter, researchers cannot release the raw data and instead have to release a list of unique identifiers, which others can then use to recollect the data from the platform themselves. This leads to the problem that subsets of the data may no longer be available, as content can be deleted or user accounts deactivated. To quantify the impact of content deletion in the replicability of datasets in a long term, we perform a longitudinal analysis of the persistence of 30 Twitter datasets, which include over 147 million tweets. Having the original datasets collected between 2012 and 2016, and recollecting them later by using the tweet IDs, we look at four different factors that quantify the extent to which recollected datasets resemble original ones: completeness, representativity, similarity and changingness. Even though the ratio of available tweets keeps decreasing as the dataset gets older, we find that the textual content of the recollected subset is still largely representative of the whole dataset that was originally collected. The representativity of the metadata, however, keeps decreasing over time, both because the dataset shrinks and because certain metadata, such as the users’ number of followers, keeps changing. Our study has important implications for researchers sharing and using publicly shared Twitter datasets in their research.

Retailers and brands are just starting to utilize online social media to support their businesses. Simultaneously, public displays are becoming ubiquitous in public places, raising the question about how these two technologies could be used together to attract new and existing customers as well as strengthen the relationship toward a focal brand. Accordingly, in a field experiment we displayed brandand product-related comments from the social network Facebook as pervasive advertising in small-space retail stores, known as kiosks. From interviews conducted with real customers during the experiment and the corresponding analysis of sales data we could conclude three findings. Showing social media comments resulted in (1) customers perceiving brands as more innovative and attractive, (2) a measurable, positive effect on sales on both the brand and the product in question and (3) customers wanting to see the comments of others, but not their own, creating a give-andtake paradox for using public displays to show social media comments.

Using social networking services is becoming more popular day by day. Social network analysis views relationships in terms of nodes (people) and edges (links or connections—the relationship between the people). The websites of the social networks such as Facebook currently are among the most popular internet services just after giant portals such as Yahoo, MSN and search engines such as Google. One of the main problems in analyzing these networks is the prediction of relationships between people in the network. It is hard to find one method that can identify relation between people in the social network. The purpose of this paper is to forecast the friendship relation between individuals among a social network, especially the likelihood of a relation between an existing member with a new member. For this purpose, we used a few hypotheses to make the graph of relationships between members of social network, and we used the method of logistic regression to complete the graph. Test data from Flickr website are used to evaluate the proposed method. The results show that the method has achieved 99 % accuracy in prediction of friendship relationships.

Introduction: Online social networking sites are being used all around the world. However, only recently researchers have started to investigate their relationship with mental health. Evidence coming from literature suggests that they have both advantages and disadvantages for individuals. The aim of this study is to critically review the existent research conducted on the relationship between online social networking and social anxiety. Method: According to PRISMA guidelines, comprehensive systematic searches of electronic databases were conducted (PsychInfo, Cochrane, PubMed, Scopus, Web of Science). Terms related to online social networking were combined with social anxiety terms. After identifying relevant papers, the relationship between social media networking and social anxiety as well as limitations of each study were presented. All of the papers included were cross-sectional, relying on self-report assessments. Conclusion: There are several papers published on the relationship between social media and social anxiety; however, more research needs to be done in order to clarify the influence of one variable over the other. Rigorous experimental studies need to be conducted with different time-point assessments, and bidirectionality should be investigated. Important mediators and moderators have to be considered in order to explain the relationship between social media networking and social anxiety.

Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Language provides a natural lens for studying mental health – much existing work and therapy have strong linguistic components, so the creation of a large, varied, language-centric dataset could provide significant grist for the field of mental health research. We examine a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis. We systematically explore language differences between ten conditions with respect to the general population, and to each other. Our aim is to provide guidance and a roadmap for where deeper exploration is likely to be fruitful.

Social media platforms have become a major gateway to receive and analyze public opinions. Understanding users can provide invaluable context information of their social media posts and significantly improve traditional opinion analysis models. Demographic attributes, such as ethnicity, gender, age, among others, have been extensively applied to characterize social media users. While studies have shown that user groups formed by demographic attributes can have coherent opinions towards political issues, these attributes are often not explicitly coded by users through their profiles. Previous work has demonstrated the effectiveness of different user signals such as users’ posts and names in determining demographic attributes. Yet, these efforts mostly evaluate linguistic signals from users’ posts and train models from artificially balanced datasets. In this paper, we propose a comprehensive list of user signals: self-descriptions and posts aggregated from users’ friends and followers, users’ profile images, and users’ names. We provide a comparative study of these signals side-by-side in the tasks on inferring three major demographic attributes, namely ethnicity, gender, and age. We utilize a realistic unbalanced datasets that share similar demographic makeups in Twitter for training models and evaluation experiments. Our experiments indicate that self-descriptions provide the strongest signal for ethnicity and age inference and clearly improve the overall performance when combined with tweets. Profile images for gender inference have the highest precision score with overall score close to the best result in our setting. This suggests that signals in self-descriptions and profile images have potentials to facilitate demographic attribute inferences in Twitter, and are promising for future investigation.

As of January 2009, the online social networking application Facebook registered more than 175 million active users. To put that number in perspective, this is only slightly less than the population of Brazil (190 million) and over twice the population of Germany (80 million)! At the same time, every minute, 10 hours of content were uploaded to the video sharing platform YouTube. And, the image hosting site Flickr provided access to over 3 billion photographs, making the world-famous Louvre Museum’s collection of 300,000 objects seem tiny in comparison. According to Forrester Research, 75% of Internet surfers used ‘‘Social Media’’ in the second quarter of 2008 by joining social networks, reading blogs, or contributing reviews to shopping sites; this represents a significant rise from 56% in 2007. The growth is not limited to teenagers, either; members of Generation X, now 35—44 years old, increasingly populate the ranks of joiners, spectators, and critics. It is therefore reasonable to say that Social Media represent a revolutionary new trend that should be of interest to companies operating in online space–—or any space, for that matter. Yet, not overly many firms seem to act comfortably in a world where consumers can speak so freely Business Horizons (2010) 53, 59—68


Question: Investigating romantic connections through social media platforms via the self-expansion model.

This study delves into the dynamics of user engagement within the Facebook social network, focusing on the evolution of interaction patterns over time. While online social platforms facilitate extensive connectivity among users, the distinction between mere connections and meaningful interactions is crucial. Previous research has highlighted the significance of the activity network—a network defined by actual user interactions, as opposed to mere friendships. Building upon this, our investigation aims to understand how these interaction-based links evolve, recognizing that their strength can fluctuate over time. Our findings reveal that activity network connections are highly transient, with a notable decline in interaction intensity as social links age. For instance, only a third of Facebook user pairs maintain consistent interaction from one month to the next. Notably, despite the rapid changes in individual link strength, several key graph-theoretic properties of the activity network remain stable over time, showcasing the network's inherent resilience and adaptability. This nuanced perspective on the evolution of user interaction within Facebook underscores the complex interplay between social ties and online behavior.

Social media are becoming ubiquitous and need to be managed like all other forms of media that organizations employ to meet their goals. However, social media are fundamentally different from any traditional or other online media because of their social network structure and egalitarian nature. These differences require a distinct measurement approach as a prerequisite for proper analysis and subsequent management. To develop the right social media metrics and subsequently construct appropriate dashboards, we provide a tool kit consisting of three novel components. First, we theoretically derive and propose a holistic framework that covers the major elements of social media, drawing on theories from marketing, psychology, and sociology. We continue to support and detail these elements — namely ‘motives,’ ‘content,’ ‘network structure,’ and ‘social roles & interactions’ — with recent research studies. Second, based on our theoretical framework, the literature review, and practical experience, we suggest nine guidelines that may prove valuable for designing appropriate social media metrics and constructing a sensible social media dashboard. Third, based on the framework and the guidelines we derive managerial implications and suggest an agenda for future research. © 2013 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved.

This essay reviews theory and research regarding the “Michelangelo phenomenon,” which describes the manner in which close partners shape one another’s dispositions, values, and behavioral tendencies. Individuals are more likely to exhibit movement toward their ideal selves to the degree that their partners exhibit affirming perception and behavior, exhibiting confidence in the self ’s capacity and enacting behaviors that elicit key features of the self ’s ideal. In turn, movement towards the ideal self yields enhanced personal well-being and couple well-being. We review empirical evidence regarding this phenomenon and discuss self and partner variables that contribute to the process.

As a novel opportunity to acquire capital from the masses, crowdfunding has attracted great attention in academia and practice. So far, little is known about the factors that promote the success of crowdfunding projects, however. In this paper, we examine in how far the social presence perceived on a project’s website influences the success of the respective crowdfunding project. Based on a data-driven analysis of 2.000 project websites from the largest crowdfunding platforms in the U.S. and Germany, we show that the perceived social presence has a significant influence on the success of crowdfunding projects. The obtained results indicate that using socially rich pictures and a socially rich description in the project presentation positively affects the success of a crowdfunding project. A socially rich profile page of the founder(s) in contrast appears to have a rather limited effect. The success of crowdfunding projects seems to be dependent on the participation behavior of the founder, however. Our results indicate that having backed other projects positively influences the success of one’s own initiative. The number of answered comments might have a negative effect on the success of the initiative, though.

Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.

In this study, we examine the identity exploration possibilities presented by online multiplayer games in which players use graphics tools and character-creation software to construct an avatar, or character. We predicted World of Warcraft players would create their main character more similar to their ideal self than the players themselves were. Our results support this idea; a sample of players rated their character as having more favorable attributes that were more favorable than their own self-rated attributes. This trend was stronger among those with lower psychological well-being, who rated themselves comparatively lower than they rated their character. Our results suggest that the game world allows players the freedom to create successful virtual selves regardless of the constraints of their actual situation.

Despite an increasing interest in understanding human perception in social media through the automatic analysis of users' personality, existing attempts have explored user profiles and text blog data only. We approach the study of personality impressions in social media from the novel perspective of crowdsourced impressions, social attention, and audiovisual behavioral analysis on slices of conversational vlogs extracted from YouTube. Conversational vlogs are a unique case study to understand users in social media, as vloggers implicitly or explicitly share information about themselves that words, either written or spoken cannot convey. In addition, research in vlogs may become a fertile ground for the study of video interactions, as conversational video expands to innovative applications. In this work, we first investigate the feasibility of crowdsourcing personality impressions from vlogging as a way to obtain judgements from a variate audience that consumes social media video. Then, we explore how these personality impressions mediate the online video watching experience and relate to measures of attention in YouTube. Finally, we investigate on the use of automatic nonverbal cues as a suitable lens through which impressions are made, and we address the task of automatic prediction of vloggers' personality impressions using nonverbal cues and machine learning techniques. Our study, conducted on a dataset of 442 YouTube vlogs and 2210 annotations collected in Amazon's Mechanical Turk, provides new findings regarding the suitability of collecting personality impressions from crowdsourcing, the types of personality impressions that emerge through vlogging, their association with social attention, and the level of utilization of nonverbal cues in this particular setting. In addition, it constitutes a first attempt to address the task of automatic vlogger personality impression prediction using nonverbal cues, with promising results.

This study aims to examine the causal priority in the observed empirical relationships between Internet addiction and other psychological problems. A cross-lagged panel survey of 361 college students in Hong Kong was conducted. Results show that excessive and unhealthy Internet use would increase feelings of loneliness over time. Although depression had a moderate and positive bivariate relationship with Internet addiction at each time point, such a relationship was not significant in the cross-lagged analyses. This study also found that online social contacts with friends and family were not an effective alternative for offline social interactions in reducing feelings of loneliness. Furthermore, while an increase in face-to-face contacts could help to reduce symptoms of Internet addiction, this effect may be neutralized by the increase in online social contacts as a result of excessive Internet use. Taken as a whole, findings from the study show a worrisome vicious cycle between loneliness and Internet addiction. 2013 Elsevier Ltd. All rights reserved.

Advances in biosensing make it possible to include heart rate monitoring in applications and several studies have suggested that heart rate communication has potential for improving social connectedness. However, it is not known how people understand heart rate feedback, or what issues need to be taken into account when designing technologies including heart rate feedback. To explore this, we created a heart rate communication probe that was used in two qualitative in-lab studies and a two-week field trial in participants' homes. Results show that heart rate feedback is a strong connectedness cue that affects the interaction in various ways, depending on a number of interrelated factors. In particular, we found two distinct categories of effects: heart rate as information and heart rate as connection. We propose two mechanisms that could explain these observations and draw out the implications they have for future use of heartbeat communication to support social connectedness or other aspects of social interaction.

Geography and social relationships are inextricably intertwined; the people we interact with on a daily basis almost always live near us. As people spend more time online, data regarding these two dimensions -- geography and social relationships -- are becoming increasingly precise, allowing us to build reliable models to describe their interaction. These models have important implications in the design of location-based services, security intrusion detection, and social media supporting local communities.
 Using user-supplied address data and the network of associations between members of the Facebook social network, we can directly observe and measure the relationship between geography and friendship. Using these measurements, we introduce an algorithm that predicts the location of an individual from a sparse set of located users with performance that exceeds IP-based geolocation. This algorithm is efficient and scalable, and could be run on a network containing hundreds of millions of users.

The present study investigated the role of 24 character strengths in 87 adolescent romantic relationships focusing on their role in partner selection and their role in mates' life satisfaction. Measures included the Values in Action Inventory of Strengths for Youth, the Students' Life Satisfaction Scale, and an Ideal Partner Profiler for the composition of an ideal partner. Honesty, humor, and love were the most preferred character strengths in an ideal partner. Hope, religiousness, honesty, and fairness showed the most substantial assortment coefficients. Hierarchical regression analyses revealed targets' character strengths as explaining variance in targets' life satisfaction. Furthermore, to a lesser degree, specific character strengths of partners and couples' similarity in certain character strengths explained variance in targets' life satisfaction beyond targets' character strengths. This first research on this topic showed that character strengths play a significant role in adolescent romantic relationships.


Question: Utilizing a model-assisted bundle adjustment framework for mapping underwater ship hulls.

This document introduces the semiglobal matching (SGM) stereo processing technique, which employs mutual information (MI) as a pixelwise matching cost to accommodate radiometric variations in input images. SGM distinguishes itself by integrating a smoothness constraint into a global cost function and achieving a swift approximation through pathwise optimizations in multiple directions. The text delves into the intricacies of occlusion detection, subpixel accuracy enhancement, and multibaseline matching. Moreover, it outlines postprocessing measures to eliminate outliers, rectify issues specific to structured environments, and fill gaps through interpolation. The document also proposes strategies for handling images of virtually any size and fusing disparity images using orthographic projection. Comparative analysis on benchmark stereo images reveals that SGM ranks among the leading algorithms, particularly excelling in subpixel accuracy. The algorithm's runtime on standard test images is a mere 1-2 seconds, showcasing its efficiency. An extensive evaluation of the MI-based matching cost reveals its robustness against a broad spectrum of radiometric transformations. Finally, the document presents successful reconstructions from extensive aerial frame and pushbroom images, underscoring the practical applicability of the proposed concepts.

Road information is useful in many fields. In this paper, a real-time mapping system is presented to acquire the image and determine the geometry of the road. These include designs of platform and instruments, data transmission, processing and archiving. Compare with the traditional platforms, the UAV (Unmanned Aerial Vehicle) platforms offer greater flexibility, shorter response time and is able to generate very high resolution data. It is inexpensive to operate, and its operation is not limited by air traffic constraints. With autonomous flight control system, it is easy to navigate the aircraft along the planned lines strictly. In this system, an unmanned fixed-wing aircraft with double engine and double generator is used, which helps to improve the reliability and capability of the platform. A digital three-axis stabilized platform is developed, which performs an important role in camera attitude controlling. To ensure there is no coverage hole, a data manage system is adopted: after an instantaneous inspection of data integrity, the data will be archived as they are received. If any anomaly is detected, the mission planning will be adapted to re-image the concerned area. Vehicle information extraction from image sequence is also described in this paper. The experiment showed the presented system worked well in real-time road mapping and vehicle information extraction.

We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists. To avoid the caveats of current image-search algorithms in case of severe viewpoint changes between the query and the database images, we propose to generate virtual views of the scene, which exploit the air-ground geometry of the system. To limit the computational complexity of the algorithm, we rely on a histogram-voting scheme to select the best putative image correspondences. The proposed approach is tested on a 2 km image dataset captured with a small quadroctopter flying in the streets of Zurich. The success of our approach shows that our new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches.

This paper describes a hierarchical estimation framework for the computation of diverse representations of motion information. The key features of the resulting framework (or family of algorithms) a,re a global model that constrains the overall structure of the motion estimated, a local rnodel that is used in the estimation process, and a coa,rse-fine refinement strategy. Four specific motion models: affine flow, planar surface flow, rigid body motion, and general optical flow, are described along with their application to specific examPles.

There have been increasing demands for developing robotic system combining camera and inertial measurement unit in navigation task, due to their low-cost, lightweight and complementary properties. In this paper, we present a Visual Inertial Odometry (VIO) system which can utilize sparse depth to estimate 6D pose in GPS-denied and unstructured environments. The system is based on Multi-State Constraint Kalman Filter (MSCKF), which benefits from low computation load when compared to optimization-based method, especially on resource-constrained platform. Features are enhanced with depth information forming 3D landmark position measurements in space, which reduces uncertainty of position estimate. And we derivate measurement model to access compatibility with both 2D and 3D measurements. In experiments, we evaluate the performance of the system in different in-flight scenarios, both cluttered room and industry environment. The results suggest that the estimator is consistent, substantially improves the accuracy compared with original monocular-based MSKCF and achieves competitive accuracy with other research.

Both simultaneous localization and mapping (SLAM) and detection and tracking of moving objects (DTMO) play key roles in robotics and automation. For certain constrained environments, SLAM and DTMO are becoming solved problems. But for robots working outdoors, and at high speeds, SLAM and DTMO are still incomplete. In earlier works, SLAM and DTMO are treated as two separate problems. In fact, they can be complementary to one another. In this paper, we present a new method to integrate SLAM and DTMO to solve both problems simultaneously for both indoor and outdoor applications. The results of experiments carried out with CMU Navlab8 and Navlab11 vehicles with the maximum speed of 45 mph in crowded urban and suburban areas verify the described work.

We present the first dense SLAM system capable of reconstructing non-rigidly deforming scenes in real-time, by fusing together RGBD scans captured from commodity sensors. Our DynamicFusion approach reconstructs scene geometry whilst simultaneously estimating a dense volumetric 6D motion field that warps the estimated geometry into a live frame. Like KinectFusion, our system produces increasingly denoised, detailed, and complete reconstructions as more measurements are fused, and displays the updated model in real time. Because we do not require a template or other prior scene model, the approach is applicable to a wide range of moving objects and scenes.

In this work, we examine the problem of constructing and maintaining a map of an autonomous vehicle’s environment for the purpose of navigation, using evidential reasoning. The inherent uncertainty in the origin of measurements of sensors demands a probabilistic approach to processing, or fusing, the new sensory information to build an accurate map. In this paper, the map is based on a two-dimensional (2-D) occupancy grid. The sensor readings are “fused” into the map using the Dempster–Shafer inference rule. This evidential approach with its multivalued hypotheses allows quantitative analysis of the quality of the data. The map building system is experimentally evaluated using sonar data from real environments.

We present the design and implementation of a new inexact Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Conjugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient preconditioners. We show that the common Schur complement trick is not limited to factorization-based methods and that it can be interpreted as a form of preconditioning. Using photos from a street-side dataset and several community photo collections, we generate a variety of bundle adjustment problems and use them to evaluate the performance of six different bundle adjustment algorithms. Our experiments show that truncated Newton methods, when paired with relatively simple preconditioners, offer state of the art performance for large-scale bundle adjustment. The code, test problems and detailed performance data are available at http://grail.cs.washington.edu/projects/bal.

In this paper, we present an efficient 3D object recognition and pose estimation approach for grasping procedures in cluttered and occluded environments. In contrast to common appearance-based approaches, we rely solely on 3D geometry information. Our method is based on a robust geometric descriptor, a hashing technique and an efficient, localized RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method simultaneously recognizes multiple model instances and estimates their pose in the scene. A variety of tests shows that the proposed method performs well on noisy, cluttered and unsegmented range scans in which only small parts of the objects are visible. The main procedure of the algorithm has a linear time complexity resulting in a high recognition speed which allows a direct integration of the method into a continuous manipulation task. The experimental validation with a 7-degrees-of-freedom Cartesian impedance controlled robot shows how the method can be used for grasping objects from a complex random stack. This application demonstrates how the integration of computer vision and softrobotics leads to a robotic system capable of acting in unstructured and occluded environments.

Robust, accurate pose estimation and mapping at real-time in six dimensions is a primary need of mobile robots, in particular flying Micro Aerial Vehicles (MAVs), which still perform their impressive maneuvers mostly in controlled environments. This work presents a visual-inertial sensor unit aimed at effortless deployment on robots in order to equip them with robust real-time Simultaneous Localization and Mapping (SLAM) capabilities, and to facilitate research on this important topic at a low entry barrier. Up to four cameras are interfaced through a modern ARM-FPGA system, along with an Inertial Measurement Unit (IMU) providing high-quality rate gyro and accelerometer measurements, calibrated and hardware-synchronized with the images. This facilitates a tight fusion of visual and inertial cues that leads to a level of robustness and accuracy which is difficult to achieve with purely visual SLAM systems. In addition to raw data, the sensor head provides FPGA-pre-processed data such as visual keypoints, reducing the computational complexity of SLAM algorithms significantly and enabling employment on resource-constrained platforms. Sensor selection, hardware and firmware design, as well as intrinsic and extrinsic calibration are addressed in this work. Results from a tightly coupled reference visual-inertial motion estimation framework demonstrate the capabilities of the presented system.


Question: Utilizing a model-assisted bundle adjustment framework for mapping underwater ship hulls.

This document outlines a method for the least-squares fitting of two three-dimensional point sets, {pi} and {p'i}; i = 1, 2,..., N, which are related through a rotation matrix R, a translation vector T, and a noise vector Ni, such that p'i = Rpi + T + Ni. The proposed algorithm aims to determine the optimal R and T that minimize the least-squares error, using the singular value decomposition (SVD) of a 3 × 3 matrix. This novel technique is evaluated against two previously established algorithms in terms of computational efficiency, demonstrating its effectiveness in processing time requirements.

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

**Documents**: An efficient evolutionary algorithm for economic dispatch with valve-point effect
The economic dispatch problem with valve-point effect is a challenging optimization problem that needs to be solved frequently in power system operations. This paper presents an efficient evolutionary algorithm to solve this problem. The proposed algorithm is a hybrid of differential evolution (DE) and particle swarm optimization (PSO). The hybridization is achieved by incorporating the mutation and crossover operations of DE into the PSO framework. The proposed algorithm is tested on a number of standard test systems and compared with other evolutionary algorithms. The results show that the proposed algorithm is more efficient and robust than other algorithms.

**Similar Documents**: An innovative evolutionary approach is introduced in this paper to tackle the economic dispatch problem with valve-point effect, a significant challenge in power system operations. The proposed algorithm is a fusion of differential evolution (DE) and particle swarm optimization (PSO), integrating the mutation and crossover operations characteristic of DE within the PSO framework. This hybridization results in an algorithm that is both efficient and robust. The algorithm's effectiveness is validated through testing on a variety of established test systems and comparison with alternative evolutionary algorithms, demonstrating its superiority in terms of efficiency and reliability.

We present a novel approach for model-based 6D pose refinement in color data. Building on the established idea of contour-based pose tracking, we teach a deep neural network to predict a translational and rotational update. At the core, we propose a new visual loss that drives the pose update by aligning object contours, thus avoiding the definition of any explicit appearance model. In contrast to previous work our method is correspondence-free, segmentation-free, can handle occlusion and is agnostic to geometrical symmetry as well as visual ambiguities. Additionally, we observe a strong robustness towards rough initialization. The approach can run in real-time and produces pose accuracies that come close to 3D ICP without the need for depth data. Furthermore, our networks are trained from purely synthetic data and will be published together with the refinement code at http://campar.in.tum. de/Main/FabianManhardt to ensure reproducibility.

This paper presents a visual-inertial odometry framework which tightly fuses inertial measurements with visual data from one or more cameras, by means of an iterated extended Kalman filter (IEKF). By employing image patches as landmark descriptors, a photometric error is derived, which is directly integrated as an innovation term in the filter update step. Consequently, the data association is an inherent part of the estimation process and no additional feature extraction or matching processes are required. Furthermore, it enables the tracking of non-corner shaped features, such as lines, and thereby increases the set of possible landmarks. The filter state is formulated in a fully robocentric fashion, which reduces errors related to nonlinearities. This also includes partitioning of a landmark’s location estimate into a bearing vector and distance and thereby allows an undelayed initialization of landmarks. Overall, this results in a compact approach which exhibits a high level of robustness with respect to low scene texture and motion blur. Furthermore, there is no time-consuming initialization procedure and pose estimates are available starting at the second image frame. We test the filter on different real datasets and compare it to other state-of-the-art visual-inertial frameworks. The experimental results show that robust localization with high accuracy can be achieved with this filterbased framework.

This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon's navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.

Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance in industrial plants. Key prerequisites for the fully autonomous operation of micro aerial vehicles in restricted environments are 3D mapping, real-time pose tracking, obstacle detection, and planning of collision-free trajectories. In this article, we propose a complete navigation system with a multimodal sensor setup for omnidirectional environment perception. Measurements of a 3D laser scanner are aggregated in egocentric local multiresolution grid maps. Local maps are registered and merged to allocentric maps in which the MAV localizes. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnidirectional perception and quick navigation reactions.

Photometric Stereo in murky water is subject to light attenuation and near-field illumination, and the resulting image formation model is complex. Apart from the scene normals and albedo, the incident illumination varies per-pixel and it depends on the scene depth and the attenuation coefficient of the medium. When these are unknown, e.g. in a realistic scenario where a robotic platform explores an underwater scene (unknown shape and distance) within the dynamic subsea environment (unknown scattering level), Photometric Stereo becomes ambiguous. Previous approaches have tackled the problem by assuming distant-lighting and resorting to external hardware for estimating the unknown model variables. In our work, we show that the Photometric Stereo problem can be determined as soon as some additional constraints regarding the scene albedo and the presence of pixels with local intensity maxima within the image are incorporated into the optimization framework. Our proposed solution leads to effective Photometric Stereo and yields detailed 3D reconstruction of objects in murky water when the scene distance and the medium attenuation are unknown. We evaluate our work using both numerical simulations and real experiments in the controlled environment of a water tank and real port water using a remotely operated vehicle. © 2016 Elsevier B.V. All rights reserved.

We propose a voting-based pose estimation algorithm applicable to 3D sensors, which are fast replacing their 2D counterparts in many robotics, computer vision, and gaming applications. It was recently shown that a pair of oriented 3D points, which are points on the object surface with normals, in a voting framework enables fast and robust pose estimation. Although oriented surface points are discriminative for objects with sufficient curvature changes, they are not compact and discriminative enough for many industrial and real-world objects that are mostly planar. As edges play the key role in 2D registration, depth discontinuities are crucial in 3D. In this paper, we investigate and develop a family of pose estimation algorithms that better exploit this boundary information. In addition to oriented surface points, we use two other primitives: boundary points with directions and boundary line segments. Our experiments show that these carefully chosen primitives encode more information compactly and thereby provide higher accuracy for a wide class of industrial parts and enable faster computation. We demonstrate a practical robotic bin-picking system using the proposed algorithm and a 3D sensor.

This work presents a Multi-View Stereo system for robust and efficient dense modeling from unstructured image collections. Our core contributions are the joint estimation of depth and normal information, pixelwise view selection using photometric and geometric priors, and a multi-view geometric consistency term for the simultaneous refinement and image-based depth and normal fusion. Experiments on benchmarks and large-scale Internet photo collections demonstrate stateof-the-art performance in terms of accuracy, completeness, and efficiency. Fig. 1. Reconstructions for Louvre, Todai-ji, Paris Opera, and Astronomical Clock.

Markerless Augmented Reality (AR) registration using the standard Homography matrix is unstable, and for image-based registration it has very low accuracy. In this paper, we present a new method to improve the stability and the accuracy of marker-less registration in AR. Based on the Visual Simultaneous Localization and Mapping (V-SLAM) framework, our method adds a three-dimensional dense cloud processing step to the state-of-the-art ORB-SLAM in order to deal with mainly the point cloud fusion and the object recognition. Our algorithm for the object recognition process acts as a stabilizer to improve the registration accuracy during the model to the scene transformation process. This has been achieved by integrating the Hough voting algorithm with the Iterative Closest Points(ICP) method. Our proposed AR framework also further increases the registration accuracy with the use of integrated camera poses on the registration of virtual objects. Our experiments show that the proposed method not only accelerates the speed of camera tracking with a standard SLAM system, but also effectively identifies objects and improves the stability of markerless augmented reality applications.

Applications that provide location related services need to understand the environment in which humans live such that verbal references and human interaction are possible. We formulate this semantic labelling task as the problem of learning the semantic labels from the perceived 3D structure. In this contribution we propose a batch approach and a novel multi-view frame fusion technique to exploit multiple views for improving the semantic labelling results. The batch approach works offline and is the direct application of an existing single-view method to scene reconstructions with multiple views. The multi-view frame fusion works in an incremental fashion accumulating the single-view results, hence allowing the online multi-view semantic segmentation of single frames and the offline reconstruction of semantic maps. Our experiments show the superiority of the approaches based on our fusion scheme, which leads to a more accurate semantic labelling.


Question: Utilizing a model-assisted bundle adjustment framework for mapping underwater ship hull structures.

This paper introduces a real-time system designed to estimate the motion of a stereo camera or a single moving camera for ground vehicle applications, utilizing video input. The system is characterized by its real-time operation with minimal delay, making it suitable for navigational purposes. The system's front end features a sophisticated feature tracker that matches point features between frame pairs and integrates them into continuous image trajectories at video rate. To produce reliable estimates of camera motion, a geometric hypothesize-and-test architecture is employed, which generates motion estimates solely from visual input, eliminating the need for prior knowledge about the scene or motion. The visual estimates can be integrated with data from various sources, including GPS, inertia sensors, and wheel encoders, to enhance accuracy. The pose estimation technique has been successfully applied to videos captured from aerial, automotive, and handheld platforms. The paper focuses on outcomes achieved using a stereo-head mounted on an autonomous ground vehicle, presenting examples of real-time camera trajectories estimated from images over unprecedented distances (600 meters) and time periods.

In a related context, a novel approach is proposed for mapping underwater ship hulls using a model-assisted bundle adjustment framework. This method combines visual and acoustic data to create detailed 3D reconstructions of ship hulls. The system processes a sequence of images and acoustic measurements from an underwater vehicle, tracking features and estimating the vehicle's motion relative to the ship hull. By integrating a pre-existing 3D model of the hull, the system can refine the position estimates and improve the accuracy of the reconstruction. The model-assisted bundle adjustment framework allows for the simultaneous optimization of feature positions, camera poses, and hull model parameters, resulting in a more precise and detailed 3D model of the ship hull. This approach is particularly useful in scenarios where high-resolution mapping is required for maintenance, inspection, or archaeological purposes. The paper provides examples of successful reconstructions of various ship hulls, demonstrating the effectiveness of the proposed method in generating accurate and detailed 3D models.

Today, autonomous underwater vehicles (AUVs) are mostly used for survey missions, but many existing applications require manipulation capabilities, such as the maintenance of permanent observatories, submerged oil wells, cabled sensor networks, and pipes; the deployment and recovery of benthic stations; or the search and recovery of black boxes. Currently, these tasks require the use of work-class remotely operated vehicles (ROVs) deployed from vessels equipped with dynamic positioning, leaving such solutions expensive to adopt. To face these challenges during the last 25 years, scientists have researched the idea of increasing the autonomy of underwater intervention systems.

In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular timesampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.

A large number of applications use motion capture systems to track the location and the body posture of people. For instance, the movie industry captures actors to animate virtual characters that perform stunts. Today's tracking systems either operate with statically mounted cameras and thus can be used in confined areas only or rely on inertial sensors that allow for free and large-scale motion but suffer from drift in the pose estimate. This paper presents a novel tracking approach that aims to provide globally aligned full body posture estimates by combining a mobile robot and an inertial motion capture system. In our approach, a mobile robot equipped with a laser scanner is used to anchor the pose estimates of a person given a map of the environment. It uses a particle filter to globally localize a person wearing a motion capture suit and to robustly track the person's position. To obtain a smooth and globally aligned trajectory of the person, we solve a least squares optimization problem formulated from the motion capture suite and tracking data. Our approach has been implemented on a real robot and exhaustively tested. As the experimental evaluation shows, our system is able to provide locally precise and globally aligned estimates of the person's full body posture.

We describea markerlesscamera tracking systemfor augmentedreality thatoperatesin environmentswhich contain oneor more planes. This is a commonspecialcase, which we showsignificantlysimplifiestracking. The result is a practical, reliable, vision-basedtracker. Furthermore, thetrackedplaneimposesa natural referenceframe, sothat thealignmentof thereal andvirtual coordinatesystemsis rather simplerthan would be the casewith a general structure-and-motionsystem.Multiple planescan be tracked, and additional data such as 2D point tracks are easilyincorporated.

Precise reconstruction of 3D volumes from X-ray projections requires precisely pre-calibrated systems where accurate knowledge of the systems geometric parameters is known ahead. However, when dealing with mobile X-ray devices such calibration parameters are unknown. Joint estimation of the systems calibration parameters and 3d reconstruction is a heavily unconstrained problem, especially when the projections are arbitrary. In industrial applications, that we target here, nominal CAD models of the object to be reconstructed are usually available. We rely on this prior information and employ Deep Learning to learn the mapping between simulated X-ray projections and its pose. Moreover, we introduce the reconstruction loss in addition to the pose loss to further improve the reconstruction quality. Finally, we demonstrate the generalization capabilities of our method in case where poses can be learned on instances of the objects belonging to the same class, allowing pose estimation of unseen objects from the same category, thus eliminating the need for the actual CAD model. We performed exhaustive evaluation demonstrating the quality of our results on both synthetic and real data.

In this paper a novel reconstruction method is presented that uses the topological relationship of detected image features to create a highly abstract but semantically rich 3D model of the reconstructed scenes. In the first step, a combined image-based reconstruction of points and lines is performed based on the current state of art structure from motion methods. Subsequently, connected planar threedimensional structures are reconstructed by a novel method that uses the topological relationships between the detected image features. The reconstructed 3D models enable a simple extraction of geometric shapes, such as rectangles, in the scene.

There have been increasing demands for developing robotic system combining camera and inertial measurement unit in navigation task, due to their low-cost, lightweight and complementary properties. In this paper, we present a Visual Inertial Odometry (VIO) system which can utilize sparse depth to estimate 6D pose in GPS-denied and unstructured environments. The system is based on Multi-State Constraint Kalman Filter (MSCKF), which benefits from low computation load when compared to optimization-based method, especially on resource-constrained platform. Features are enhanced with depth information forming 3D landmark position measurements in space, which reduces uncertainty of position estimate. And we derivate measurement model to access compatibility with both 2D and 3D measurements. In experiments, we evaluate the performance of the system in different in-flight scenarios, both cluttered room and industry environment. The results suggest that the estimator is consistent, substantially improves the accuracy compared with original monocular-based MSKCF and achieves competitive accuracy with other research.

LiDAR (Light detection and ranging) sensors are widely used in research and development. As such, they build the base for the evaluation of newly developed ADAS (Advanced Driver Assistance Systems) functions in the automotive field where they are used for ground truth establishment. However, the factory calibration provided for the sensors is not able to satisfy the high accuracy requirements by such applications. In this paper we propose a concept to easily improve the existing calibration of a Velodyne LiDAR sensor without the need for special calibration setups which can even be used to enhance already recorded data.

We present a novel approach for model-based 6D pose refinement in color data. Building on the established idea of contour-based pose tracking, we teach a deep neural network to predict a translational and rotational update. At the core, we propose a new visual loss that drives the pose update by aligning object contours, thus avoiding the definition of any explicit appearance model. In contrast to previous work our method is correspondence-free, segmentation-free, can handle occlusion and is agnostic to geometrical symmetry as well as visual ambiguities. Additionally, we observe a strong robustness towards rough initialization. The approach can run in real-time and produces pose accuracies that come close to 3D ICP without the need for depth data. Furthermore, our networks are trained from purely synthetic data and will be published together with the refinement code at http://campar.in.tum. de/Main/FabianManhardt to ensure reproducibility.

To facilitate scene understanding and robot navigation in a modern urban area, we design a multilayer feature graph (MFG) based on two views from an on-board camera. The nodes of an MFG are features such as scale invariant feature transformation (SIFT) feature points, line segments, lines, and planes while edges of the MFG represent different geometric relationships such as adjacency, parallelism, collinearity, and coplanarity. MFG also connects the features in two views and the corresponding 3D coordinate system. Building on SIFT feature points and line segments, MFG is constructed using feature fusion which incrementally, iteratively, and extensively verifies the aforementioned geometric relationships using random sample consensus (RANSAC) framework. Physical experiments show that MFG can be successfully constructed in urban area and the construction method is demonstrated to be very robust in identifying feature correspondence.


Question: Utilizing a model-assisted bundle adjustment framework for mapping underwater ship hulls.

This paper introduces an innovative method for creating realistic 3D face models, either through automatic generation from photographic inputs or through a user-friendly interface for direct modeling. The technique addresses two primary challenges in computer-aided face modeling: the automatic registration of new face images or 3D models to an internal template through dense correspondence computation, and the regulation of face naturalness to prevent the creation of unrealistic appearances. Starting with a collection of 3D face models, the authors develop a morphable face model by converting the shape and texture of these examples into a vector space representation. New faces and expressions can be created by combining the prototypes in linear fashion. Statistical constraints derived from the example set guide the manual modeling process and enhance the performance of automated matching algorithms. The paper showcases the method's capability in 3D face reconstruction from single images and its potential for photo-realistic image manipulation, as well as the manipulation of complex facial parameters such as gender, facial fullness, and distinctiveness.

**Question**: A new method for the synthesis of 3D facial expressions

**Documents**: A Neural Network Approach to Facial Expression Synthesis
In this paper, we propose a novel method for facial expression synthesis using neural networks. Our approach is based on the observation that facial expressions can be represented as a linear combination of a set of basic expressions. We train a neural network to learn the mapping between the coefficients of the linear combination and the corresponding facial expression. The neural network is trained using a dataset of facial expressions captured from a 3D face scanner. The training data consists of a set of basic expressions and their corresponding coefficients. The neural network is then used to synthesize new facial expressions by taking as input the coefficients of the linear combination and generating the corresponding facial expression. We evaluate the performance of our method using a dataset of facial expressions captured from a 3D face scanner. The results show that our method is able to synthesize facial expressions with high accuracy and realism.

**Similar Documents**: This paper presents an innovative technique for synthesizing facial expressions using a neural network framework. The proposed method is grounded in the principle that facial expressions can be effectively represented as linear combinations of a set of fundamental expressions. To achieve this, we train a neural network to learn the relationship between the coefficients of these linear combinations and the corresponding facial expressions. The training process utilizes a dataset of facial expressions obtained through a 3D face scanner, which provides a comprehensive set of basic expressions along with their associated coefficients. Once trained, the neural network can generate new facial expressions by

We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates motion estimates from visual input alone. No prior knowledge of the scene nor the motion is necessary. The visual estimates can also be used in conjunction with information from other sources such as GPS, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive and handheld platforms. We focus on results obtained with a stereo-head mounted on an autonomous ground vehicle. We give examples of camera trajectories estimated in real-time purely from images over previously unseen distances (600 meters) and periods of time .

This paper presents an integrated approach to exploration, mapping, and localization. Our algorithm uses a highly efficient Rao-Blackwellized particle filter to represent the posterior about maps and poses. It applies a decision-theoretic framework which simultaneously considers the uncertainty in the map and in the pose of the vehicle to evaluate potential actions. Thereby, it trades off the cost of executing an action with the expected information gain and takes into account possible sensor measurements gathered along the path taken by the robot. We furthermore describe how to utilize the properties of the Rao-Blackwellization to efficiently compute the expected information gain. We present experimental results obtained in the real world and in simulation to demonstrate the effectiveness of our approach.

Flat refractive geometry corresponds to a perspective camera looking through single/multiple parallel flat refractive mediums. We show that the underlying geometry of rays corresponds to an axial camera. This realization, while missing from previous works, leads us to develop a general theory of calibrating such systems using 2D-3D correspondences. The pose of 3D points is assumed to be unknown and is also recovered. Calibration can be done even using a single image of a plane. We show that the unknown orientation of the refracting layers corresponds to the underlying axis, and can be obtained independently of the number of layers, their distances from the camera and their refractive indices. Interestingly, the axis estimation can be mapped to the classical essential matrix computation and 5-point algorithm [15] can be used. After computing the axis, the thicknesses of layers can be obtained linearly when refractive indices are known, and we derive analytical solutions when they are unknown. We also derive the analytical forward projection (AFP) equations to compute the projection of a 3D point via multiple flat refractions, which allows non-linear refinement by minimizing the reprojection error. For two refractions, AFP is either 4th or 12th degree equation depending on the refractive indices. We analyze ambiguities due to small field of view, stability under noise, and show how a two layer system can be well approximated as a single layer system. Real experiments using a water tank validate our theory.

Image-based 6-DoF pose estimation of arbitrarily shaped 3D objects based on their shape is a rarely studied problem. Most existing image-based methods for pose estimation either exploit textural information in form of local features or, if shape-based, rely on the extraction of straight line segments or other primitives. Straight-forward extensions of 2D approaches are potentially more general, but in practice assume a limited range of possible view angles. The general problem is that a 3D object can potentially produce completely different 2D projections depending on its relative pose to the observing camera. One way to reduce the solution space is to exploit temporal information, i.e. perform tracking. Again, existing model-based tracking approaches rely on relatively simple object geometries. In this paper, we propose a particle filter based tracking approach that can deal with arbitrary shapes and arbitrary or even no texture, i.e. it offers a general solution to the rigid object tracking problem. As our approach can deal with occlusions, it is in particular of interest in the context of goal-directed imitation learning involving the observation of object manipulations. Results of simulation experiments as well as real-world experiments with different object types prove the practical applicability of our approach.

We provide a large dataset containing RGB-D image sequences and the ground-truth camera trajectories with the goal to establish a benchmark for the evaluation of visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor and the groundtruth trajectory of camera poses. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). Further, we provide the accelerometer data from the Kinect. Finally, we propose an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems.

The sheer volume of data generated by depth cameras provides a challenge to process in real time, in particular when used for indoor mobile robot localization and navigation. We introduce the Fast Sampling Plane Filtering (FSPF) algorithm to reduce the volume of the 3D point cloud by sampling points from the depth image, and classifying local grouped sets of points as belonging to planes in 3D (the “plane filtered” points) or points that do not correspond to planes within a specified error margin (the “outlier” points). We then introduce a localization algorithm based on an observation model that down-projects the plane filtered points on to 2D, and assigns correspondences for each point to lines in the 2D map. The full sampled point cloud (consisting of both plane filtered as well as outlier points) is processed for obstacle avoidance for autonomous navigation. All our algorithms process only the depth information, and do not require additional RGB data. The FSPF, localization and obstacle avoidance algorithms run in real time at full camera frame rates (30Hz) with low CPU requirements (16%). We provide experimental results demonstrating the effectiveness of our approach for indoor mobile robot localization and navigation. We further compare the accuracy and robustness in localization using depth cameras with FSPF vs. alternative approaches that simulate laser rangefinder scans from the 3D data.

The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU’s to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.

Road information is useful in many fields. In this paper, a real-time mapping system is presented to acquire the image and determine the geometry of the road. These include designs of platform and instruments, data transmission, processing and archiving. Compare with the traditional platforms, the UAV (Unmanned Aerial Vehicle) platforms offer greater flexibility, shorter response time and is able to generate very high resolution data. It is inexpensive to operate, and its operation is not limited by air traffic constraints. With autonomous flight control system, it is easy to navigate the aircraft along the planned lines strictly. In this system, an unmanned fixed-wing aircraft with double engine and double generator is used, which helps to improve the reliability and capability of the platform. A digital three-axis stabilized platform is developed, which performs an important role in camera attitude controlling. To ensure there is no coverage hole, a data manage system is adopted: after an instantaneous inspection of data integrity, the data will be archived as they are received. If any anomaly is detected, the mission planning will be adapted to re-image the concerned area. Vehicle information extraction from image sequence is also described in this paper. The experiment showed the presented system worked well in real-time road mapping and vehicle information extraction.

We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists. To avoid the caveats of current image-search algorithms in case of severe viewpoint changes between the query and the database images, we propose to generate virtual views of the scene, which exploit the air-ground geometry of the system. To limit the computational complexity of the algorithm, we rely on a histogram-voting scheme to select the best putative image correspondences. The proposed approach is tested on a 2 km image dataset captured with a small quadroctopter flying in the streets of Zurich. The success of our approach shows that our new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches.

This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover, lidar and image sensor positions generally differ during acquisition, such that depth values referring to objects that are hidden from the image view point might appear with a naive projection. The proposed algorithm estimates the complete depth map, while simultaneously detecting and excluding those hidden points. It consists in a primal-dual optimization method, where a coupled total variation regularization term is included to match the depth and image gradients and a visibility indicator handles the selection of visible points. Tests with real data prove the effectiveness of the proposed strategy.