1,720,980 research outputs found
Skeleton-Based Gait Recognition via Robust Frame-Level Matching
No full textGait is a useful biometric feature for human identification in video surveillance applications since it can be obtained without subject cooperation. In recent years, model-based gait recognition using a 3D skeleton has been widely studied through view-invariant modeling and kinematic gait analysis. However, existing methods integrate all frame-level feature vectors using the same criterion, even though skeleton information is highly sensitive to changes in covariate conditions such as clothing, carrying, and occlusion. The scheme inevitably reduces the frame-level discriminative power and eventually degrades performance. Instead, we propose a robust framelevel matching method for gait recognition that minimizes the influence of noisy patterns as well as secures the frame-level discriminative power. To this end, we measure the skeleton quality in terms of body symmetry for each frame. Based on the quality, we construct a quality-adjusted cost matrix between input frames and registered frames to prevent matching with noisy patterns. Our two-stage linear matching is then applied to the cost matrix to compute a frame-level discriminative score including similarity and margin. In the end, the identity of a probe is determined by a weighted majority voting scheme via frame-level scores. It enhances the robustness against inaccurate skeleton estimation results by assigning different weights for each frame based on the score. Our approach outperforms the state-of-the-art methods on three public datasets (UPCVgait, UPCVgaitK2, and SDUgait) and a new gait dataset which we create with consideration of unpredictable behaviors while walking. In addition, we demonstrate that our method is robust to skeleton estimation error, partial occlusion, and data loss. The CILgait dataset and MATLAB code are available at https://sites.google.com/site/seokeonchoi/gait-recognition.
Real-time 3D Human Pose Recognition from Reconstructed Volume via Voxel Classifiers
No full textThis paper presents a human pose recognition method which simultaneously reconstructs a human volume based on
ensemble of voxel classifiers from a single depth image in real-time. The human pose recognition is a difficult task since
a single depth camera can capture only visible surfaces of a human body. In order to recognize invisible (self-occluded)
surfaces of a human body, the proposed algorithm employs voxel classifiers trained with multi-layered synthetic voxels.
Specifically, ray-casting onto a volumetric human model generates a synthetic voxel, where voxel consists of a 3D
position and ID corresponding to the body part. The synthesized volumetric data which contain both visible and invisible
body voxels are utilized to train the voxel classifiers. As a result, the voxel classifiers not only identify the visible voxels
but also reconstruct the 3D positions and the IDs of the invisible voxels. The experimental results show improved
performance on estimating the human poses due to the capability of inferring the invisible human body voxels. It is
expected that the proposed algorithm can be applied to many fields such as telepresence, gaming, virtual fitting, wellness
business, and real 3D contents control on real 3D displays
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
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
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