1,721,149 research outputs found
Local velocity-adapted motion events for spatio-temporal recognition
No full textIn this paper, we address the problem of motion recognition using event-based local motion representations. We assume that similar patterns of motion contain similar events with consistent motion across image sequences. Using this assumption, we formulate the problem of motion recognition as a matching of corresponding events in image sequences. To enable the matching, we present and evaluate a set of motion descriptors that exploit the spatial and the temporal coherence of motion measurements between corresponding events in image sequences. As the motion measurements may depend on the relative motion of the camera, we also present a mechanism for local velocity adaptation of events and evaluate its influence when recognizing image sequences subjected to different camera motions. When recognizing motion patterns, we compare the performance of a nearest neighbor (NN) classifier with the performance of a support vector machine (SVM). We also compare event-based motion representations to motion representations in terms of global histograms. A systematic experimental evaluation on a large video database with human actions demonstrates that (i) local spatio-temporal image descriptors can be defined to carry important information of space-time events for subsequent recognition, and that (ii) local velocity adaptation is an important mechanism in situations when the relative motion between the camera and the interesting events in the scene is unknown. The particular advantage of event-based representations and velocity adaptation is further emphasized when recognizing human actions in unconstrained scenes with complex and non-stationary backgrounds.QC 20100525 QC 20111115</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Modeling and visual recognition of human actions and interactions
No full textThis work addresses the problem of recognizing actions and interactions in realistic video settings such as movies and consumer videos. The first contribution of this thesis (Chapters 2 and 4) is concerned with new video representations for action recognition. We introduce local space-time descriptors and demonstrate their potential to classify and localize actions in complex settings while circumventing the difficult intermediate steps of person detection, tracking and human pose estimation. The material on bag-of-features action recognition in Chapter 2 is based on publications [L14, L22, L23] and is related to other work by the author [L6, L7, L8, L11, L12, L13, L16, L21]. The work on object and action localization in Chapter 4 is based on [L9, L10, L13, L15] and relates to [L1, L17, L19, L20]. The second contribution of this thesis is concerned with weakly-supervised action learning. Chap- ter 3 introduces methods for automatic annotation of action samples in video using readily-available video scripts. It addresses the ambiguity of action expressions in text and the uncertainty of tem- poral action localization provided by scripts. The material presented in Chapter 3 is based on publications [L4, L14, L18]. Finally Chapter 5 addresses interactions of people with objects and concerns modeling and recognition of object function. We exploit relations between objects and co-occurring human poses and demonstrate object recognition improvements using automatic pose estimation in challenging videos from YouTube. This part of the thesis is based on the publica- tion [L2] and relates to other work by the author [L3, L5]
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Face recognition in unconstrained video streams
Full text linkDas Thema Gesichtserkennung ist gibt es schon lange in der Mustererkennung. Die jüngsten Fortschritte versprechen erfolgreiche Anwendungen für Videoüberwachung in großem Maßstab. Das Erkennen von Millionen von Menschen aufgenommen von tausenden Kameras birgt aber auch signifikante Kosten bezüglich Rechenleistung und Datenkommunikation. Diese Arbeit versucht die Komplexität von Gesichtserkennung in Videos zu reduzieren und verfolgt zwei komplementäre Strategien. Einerseits untersuchen Gesichtserkennung im "whitened"Deskriptorraum und verbessern die Komplexität der state-of-the-art Matched Background Similarity (MBGS) Methode. Andererseits untersuchen wir den Effekt den das Auswählen einer Teilmenge von Videobildern auf die Erkennungsgenauigkeit hat und zeigen, dass es möglich ist mit einem Bruchteil von Videobildern robuste Resultate zu erzielen. Für ein besseres Verständnis von Tiefen Faltenden Neuralen Netzwerken geben wir eine Einführung in das Thema und fassen aktuelle Entwicklungen zusammen, speziell im Bezug auf den weitbekannten ImageNet Wettbewerb. Wir evaluieren unsere Methoden und präsentieren konkurrenzfähige Resultate für den YTF und TSFT Benchmark mit signifikant niedrigerer Komplexität im Vergleich zu anderen Methoden.Face recognition is a long-standing topic in machine learning. Recent advances promise successful applications in large-scale video surveillance. Recognizing millions of people in thousands of streaming cameras, however, implies significant costs in terms of computations and communication bandwidth. This work aims to reduce the complexity of face recognition in video and pursues two complementary strategies. We first investigate face recognition in the whitened descriptor space and improve the complexity of the state-of-the-art Matched Background Similarity (MBGS) approach. Second, we study frame selection and demonstrate robust recognition performance from a small fraction of video frames. For a better understanding of the advancing Deep Convolution Neural Networks field, we give a thorough introduction to the topic and briefly summarize the recent development with a focus on the ImageNet classification challenge. We evaluate our methods and present competitive results for the YTF and TSFT benchmarks while significantly reducing the complexity of previous methods
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