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Author Anthony Cioppa; Silvio Giancola; Vladimir Somers; Floriane Magera; Xin Zhou; Hassan Mkhallati; Adrien Deliège; Jan Held; Carlos Hinojosa; Amir M. Mansourian; Pierre Miralles; Olivier Barnich; Christophe De Vleeschouwer; Alexandre Alahi; Bernard Ghanem; Marc Van Droogenbroeck; Abdullah Kamal; Adrien Maglo; Albert Clapes; Amr Abdelaziz; Artur Xarles; Astrid Orcesi; Atom Scott; Bin Liu; Byoungkwon Lim; Chen Chen; Fabian Deuser; Feng Yan; Fufu Yu; Gal Shitrit; Guanshuo Wang; Gyusik Choi; Hankyul Kim; Hao Guo; Hasby Fahrudin; Hidenari Koguchi; Håkan Ardo; Ibrahim Salah; Ido Yerushalmy; Iftikar Muhammad; Ikuma Uchida; Ishay Beery; Jaonary Rabarisoa; Jeongae Lee; Jiajun Fu; Jianqin Yin; Jinghang Xu; Jongho Nang; Julien Denize; Junjie Li; Junpei Zhang; Juntae Kim; Kamil Synowiec; Kenji Kobayashi; Kexin Zhang; Konrad Habel; Kota Nakajima; Licheng Jiao; Lin Ma; Lizhi Wang; Luping Wang; Menglong Li; Mengying Zhou; Mohamed Nasr; Mohamed Abdelwahed; Mykola Liashuha; Nikolay Falaleev; Norbert Oswald; Qiong Jia; Quoc-Cuong Pham; Ran Song; Romain Herault; Rui Peng; Ruilong Chen; Ruixuan Liu; Ruslan Baikulov; Ryuto Fukushima; Sergio Escalera; Seungcheon Lee; Shimin Chen; Shouhong Ding; Taiga Someya; Thomas B. Moeslund; Tianjiao Li; Wei Shen; Wei Zhang; Wei Li; Wei Dai; Weixin Luo; Wending Zhao; Wenjie Zhang; Xinquan Yang; Yanbiao Ma; Yeeun Joo; Yingsen Zeng; Yiyang Gan; Yongqiang Zhu; Yujie Zhong; Zheng Ruan; Zhiheng Li; Zhijian Huang; Ziyu Meng
Title (up) SoccerNet 2023 Challenges Results Type Miscellaneous
Year 2023 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on this https URL. Baselines and development kits can be found on this https URL.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA Approved no
Call Number Admin @ si @ CGS2023 Serial 3991
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Author Pierluigi Casale
Title (up) Social Environment Description from Data Collected with a Wearable Device Type Miscellaneous
Year 2008 Publication CVC Technical Report #124 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona, Spain
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number Admin @ si @ Cas2008 Serial 1151
Permanent link to this record
 
 
Author Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva; Bogdan Raducanu
Title (up) Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction Type Journal Article
Year 2012 Publication Sensors Abbreviated Journal SENS
Volume 12 Issue 2 Pages 1702-1719
Keywords
Abstract IF=1.77 (2010)
Social interactions are a very important component in peopleís lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Timesí Blogging Heads opinion blog.
The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The linksí weights are a measure of the ìinfluenceî a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network.
Address
Corporate Author Thesis
Publisher Molecular Diversity Preservation International Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; OR;HuPBA;MV Approved no
Call Number Admin @ si @ EBV2012 Serial 1885
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Author Emanuel Sanchez Aimar; Petia Radeva; Mariella Dimiccoli
Title (up) Social Relation Recognition in Egocentric Photostreams Type Conference Article
Year 2019 Publication 26th International Conference on Image Processing Abbreviated Journal
Volume Issue Pages 3227-3231
Keywords
Abstract This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental's social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal.
Address Taipei; Taiwan; September 2019
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICIP
Notes MILAB; no menciona Approved no
Call Number Admin @ si @ SRD2019 Serial 3370
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Author Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov
Title (up) Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images Type Conference Article
Year 2018 Publication International Workshop on Egocentric Perception, Interaction and Computing at ECCV Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Word spotting in natural scene images has many applications in scene understanding and visual assistance. We propose Soft-PHOC, an intermediate representation of images based on character probability maps. Our representation extends the concept of the Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We show how to use our descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera.
Address Munich; Alemanya; September 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCVW
Notes DAG; 600.129; 600.121; Approved no
Call Number Admin @ si @ BKB2018b Serial 3174
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Author Dimosthenis Karatzas;Ch. Lioutas
Title (up) Software Package Development for Electron Diffraction Image Analysis Type Conference Article
Year 1998 Publication Proceedings of the XIV Solid State Physics National Conference Abbreviated Journal
Volume Issue Pages
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Abstract
Address Ioannina, Greece
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number IAM @ iam @ KaL1998 Serial 2045
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Author Oriol Pujol; Petia Radeva
Title (up) Solving Particularization with Supervised Clustering Competition Scheme Type Book Chapter
Year 2005 Publication Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 11–18 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Estoril (Portugal)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ PuR2005b Serial 557
Permanent link to this record
 
 
Author D. Smith
Title (up) Solving the mean string problem for 2D shapes Type Report
Year 1999 Publication CVC Technical Report #36 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address CVC (UAB)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number Admin @ si @ Smi1999 Serial 195
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Author Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez
Title (up) Solving the Multi Object Occlusion Problem in a Multiple Camera Tracking System Type Journal
Year 2009 Publication Pattern Recognition and Image Analysis Abbreviated Journal
Volume 19 Issue 1 Pages 165-171
Keywords
Abstract An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1054-6618 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number ISE @ ise @ MAR2009a Serial 1160
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Author Naveen Onkarappa; Angel Sappa
Title (up) Space Variant Representations for Mobile Platform Vision Applications Type Conference Article
Year 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal
Volume 6855 Issue II Pages 146-154
Keywords
Abstract The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow.
Address Seville, Spain
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-23677-8 Medium
Area Expedition Conference CAIP
Notes ADAS Approved no
Call Number NaS2011; ADAS @ adas @ Serial 1686
Permanent link to this record
 
 
Author J. Stöttinger; A. Hanbury; N. Sebe; Theo Gevers
Title (up) Spars Color Interest Points for Image Retrieval and Object Categorization Type Journal Article
Year 2012 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 21 Issue 5 Pages 2681-2692
Keywords
Abstract Impact factor 2010: 2.92
IF 2011/2012?: 3.32
Interest point detection is an important research area in the field of image processing and computer vision. In particular, image retrieval and object categorization heavily rely on interest point detection from which local image descriptors are computed for image matching. In general, interest points are based on luminance, and color has been largely ignored. However, the use of color increases the distinctiveness of interest points. The use of color may therefore provide selective search reducing the total number of interest points used for image matching. This paper proposes color interest points for sparse image representation. To reduce the sensitivity to varying imaging conditions, light-invariant interest points are introduced. Color statistics based on occurrence probability lead to color boosted points, which are obtained through saliency-based feature selection. Furthermore, a principal component analysis-based scale selection method is proposed, which gives a robust scale estimation per interest point. From large-scale experiments, it is shown that the proposed color interest point detector has higher repeatability than a luminance-based one. Furthermore, in the context of image retrieval, a reduced and predictable number of color features show an increase in performance compared to state-of-the-art interest points. Finally, in the context of object recognition, for the Pascal VOC 2007 challenge, our method gives comparable performance to state-of-the-art methods using only a small fraction of the features, reducing the computing time considerably.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1057-7149 ISBN Medium
Area Expedition Conference
Notes ALTRES;ISE Approved no
Call Number Admin @ si @ SHS2012 Serial 1847
Permanent link to this record
 
 
Author Laura Igual; Santiago Segui; Jordi Vitria; Fernando Azpiroz; Petia Radeva
Title (up) Sparse Bayesian Feature Selection Applied to Intestinal Motility Analysis Type Conference Article
Year 2007 Publication XVI Congreso Argentino de Bioingenieria Abbreviated Journal
Volume Issue Pages 467–470
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Abstract
Address San Juan (Argentina)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference SABI
Notes MILAB;OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ ISV2007b Serial 896
Permanent link to this record
 
 
Author Mikhail Mozerov; Fei Yang; Joost Van de Weijer
Title (up) Sparse Data Interpolation Using the Geodesic Distance Affinity Space Type Journal Article
Year 2019 Publication IEEE Signal Processing Letters Abbreviated Journal SPL
Volume 26 Issue 6 Pages 943 - 947
Keywords
Abstract In this letter, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate its superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the paper on EpicFlow optical flow, which is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ MYW2019 Serial 3261
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Author Anguelos Nicolaou; Andrew Bagdanov; Marcus Liwicki; Dimosthenis Karatzas
Title (up) Sparse Radial Sampling LBP for Writer Identification Type Conference Article
Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal
Volume Issue Pages 716-720
Keywords
Abstract In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.
Address Nancy; France; August 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ NBL2015 Serial 2692
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Author Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades
Title (up) Sparse representation over learned dictionary for symbol recognition Type Journal Article
Year 2016 Publication Signal Processing Abbreviated Journal SP
Volume 125 Issue Pages 36-47
Keywords Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points
Abstract In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.061; 600.077 Approved no
Call Number Admin @ si @ DTR2016 Serial 2946
Permanent link to this record