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| Author | Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez; Xavier Roca | ||||
| Title | A Selective Spatio-Temporal Interest Point Detector for Human Action Recognition in Complex Scenes | Type | Conference Article | ||
| Year | 2011 | Publication | 13th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1776-1783 | ||
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| Abstract | Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper we present a new approach for STIP detection by applying surround suppression combined with local and temporal constraints. Our method is significantly different from existing STIP detectors and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-visual words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on existing benchmark datasets, and more challenging datasets of complex scenes, validate our approach and show state-of-the-art performance. | ||||
| Address | Barcelona | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1550-5499 | ISBN | 978-1-4577-1101-5 | Medium | |
| Area | Expedition | Conference | ICCV | ||
| Notes | ISE | Approved | no | ||
Call Number  |
Admin @ si @ CHM2011 | Serial | 1811 | ||
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| Author | Diego Alejandro Cheda | ||||
| Title | Monocular Depth Cues in Computer Vision Applications | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Depth perception is a key aspect of human vision. It is a routine and essential visual task that the human do effortlessly in many daily activities. This has often been associated with stereo vision, but humans have an amazing ability to perceive depth relations even from a single image by using several monocular cues.
In the computer vision field, if image depth information were available, many tasks could be posed from a different perspective for the sake of higher performance and robustness. Nevertheless, given a single image, this possibility is usually discarded, since obtaining depth information has frequently been performed by three-dimensional reconstruction techniques, requiring two or more images of the same scene taken from different viewpoints. Recently, some proposals have shown the feasibility of computing depth information from single images. In essence, the idea is to take advantage of a priori knowledge of the acquisition conditions and the observed scene to estimate depth from monocular pictorial cues. These approaches try to precisely estimate the scene depth maps by employing computationally demanding techniques. However, to assist many computer vision algorithms, it is not really necessary computing a costly and detailed depth map of the image. Indeed, just a rough depth description can be very valuable in many problems. In this thesis, we have demonstrated how coarse depth information can be integrated in different tasks following alternative strategies to obtain more precise and robust results. In that sense, we have proposed a simple, but reliable enough technique, whereby image scene regions are categorized into discrete depth ranges to build a coarse depth map. Based on this representation, we have explored the potential usefulness of our method in three application domains from novel viewpoints: camera rotation parameters estimation, background estimation and pedestrian candidate generation. In the first case, we have computed camera rotation mounted in a moving vehicle applying two novels methods based on distant elements in the image, where the translation component of the image flow vectors is negligible. In background estimation, we have proposed a novel method to reconstruct the background by penalizing close regions in a cost function, which integrates color, motion, and depth terms. Finally, we have benefited of geometric and depth information available on single images for pedestrian candidate generation to significantly reduce the number of generated windows to be further processed by a pedestrian classifier. In all cases, results have shown that our approaches contribute to better performances. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Daniel Ponsa;Antonio Lopez | |
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| Notes | ADAS | Approved | no | ||
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Admin @ si @ Che2012 | Serial | 2210 | ||
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| Author | Diego Alejandro Cheda | ||||
| Title | Monocular egomotion estimation for ADAS application | Type | Report | ||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 148 | Issue | Pages | ||
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| Corporate Author | Computer Vision Center | Thesis | Ph.D. thesis | ||
| Publisher | Place of Publication | Bellaterra, Barcelona | Editor | ||
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| Notes | ADAS | Approved | no | ||
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Admin @ si @ Che2009 | Serial | 2402 | ||
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| Author | Francesco Ciompi; Rui Hua; Simone Balocco; Marina Alberti; Oriol Pujol; Carles Caus; Josepa Mauri; Petia Radeva | ||||
| Title | Learning to Detect Stent Struts in Intravascular Ultrasound | Type | Conference Article | ||
| Year | 2013 | Publication | 6th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 7887 | Issue | Pages | 575-583 | |
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| Abstract | In this paper we tackle the automatic detection of struts elements (metallic braces of a stent device) in Intravascular Ultrasound (IVUS) sequences. The proposed method is based on context-aware classification of IVUS images, where we use Multi-Class Multi-Scale Stacked Sequential Learning (M2SSL). Additionally, we introduce a novel technique to reduce the amount of required contextual features. The comparison with binary and multi-class learning is also performed, using a dataset of IVUS images with struts manually annotated by an expert. The best performing configuration reaches a F-measure F = 63.97% . | ||||
| Address | Madeira; Portugal; June 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-38627-5 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | MILAB; HuPBA; 605.203; 600.046 | Approved | no | ||
| Call Number |
Admin @ si @ CHB2013 | Serial | 2349 | ||
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| Author | Bhaskar Chakraborty | ||||
| Title | Model free approach to human action recognition | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Automatic understanding of human activity and action is very important and challenging research area of Computer Vision with wide applications in video surveillance, motion analysis, virtual reality interfaces, video indexing, content based video retrieval, HCI and health care. This thesis presents a series of techniques to solve the problem of human action recognition in video. First approach towards this goal is based on a probabilistic optimization model of body parts using Hidden Markov Model. This strong model based approach is able to distinguish between similar actions by only considering the body parts having major contributions to the actions. In next approach, we apply a weak model based human detector and actions are represented by Bag-of-key poses model to capture the human pose changes during the actions. To tackle the problem of human action recognition in complex scenes, a selective spatio-temporal interest point (STIP) detector is proposed by using a mechanism similar to that of the non-classical receptive field inhibition that is exhibited by most oriented selective neuron in the primary visual cortex. An extension of the selective STIP detector is applied to multi-view action recognition system by introducing a novel 4D STIPs (3D space + time). Finally, we use our STIP detector on large scale continuous visual event recognition problem and propose a novel generalized max-margin Hough transformation framework for activity detection | ||||
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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| Notes | ISE | Approved | no | ||
| Call Number |
Admin @ si @ Cha2012 | Serial | 2207 | ||
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| Author | Bhaskar Chakraborty | ||||
| Title | View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component Wise HMM of Body Parts | Type | Miscellaneous | ||
| Year | 2008 | Publication | CVC Technical Report #123 | Abbreviated Journal | |
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| Address | Barcelona, Spain | ||||
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| Notes | ISE | Approved | no | ||
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Admin @ si @ Cha2008 | Serial | 1149 | ||
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| Author | Raul Chaves | ||||
| Title | Sistema de identificacion mediante huellas dactilares | Type | Report | ||
| Year | 2004 | Publication | CVC Technical Report #81 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
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Admin @ si @ Cha2004 | Serial | 507 | ||
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| Author | V. Chapaprieta | ||||
| Title | Reconocimiento de caracteres manuscritos mediante modelos de distribucion de puntos (PDM) | Type | Report | ||
| Year | 2000 | Publication | CVC Technical Report #41 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
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Admin @ si @ Cha2000 | Serial | 342 | ||
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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 | SoccerNet 2023 Challenges Results | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| 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. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number |
Admin @ si @ CGS2023 | Serial | 3991 | ||
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| Author | Mariona Caros; Maite Garolera; Petia Radeva; Xavier Giro | ||||
| Title | Automatic Reminiscence Therapy for Dementia | Type | Conference Article | ||
| Year | 2020 | Publication | 10th ACM International Conference on Multimedia Retrieval | Abbreviated Journal | |
| Volume | Issue | Pages | 383-387 | ||
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| Abstract | With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. | ||||
| Address | Virtual; October 2020 | ||||
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| Area | Expedition | Conference | ICRM | ||
| Notes | Approved | no | |||
| Call Number |
Admin @ si @ CGR2020 | Serial | 3529 | ||
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| Author | Bhaskar Chakraborty; Jordi Gonzalez; Xavier Roca | ||||
| Title | Large scale continuous visual event recognition using max-margin Hough transformation framework | Type | Journal Article | ||
| Year | 2013 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 117 | Issue | 10 | Pages | 1356–1368 |
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| Abstract | In this paper we propose a novel method for continuous visual event recognition (CVER) on a large scale video dataset using max-margin Hough transformation framework. Due to high scalability, diverse real environmental state and wide scene variability direct application of action recognition/detection methods such as spatio-temporal interest point (STIP)-local feature based technique, on the whole dataset is practically infeasible. To address this problem, we apply a motion region extraction technique which is based on motion segmentation and region clustering to identify possible candidate “event of interest” as a preprocessing step. On these candidate regions a STIP detector is applied and local motion features are computed. For activity representation we use generalized Hough transform framework where each feature point casts a weighted vote for possible activity class centre. A max-margin frame work is applied to learn the feature codebook weight. For activity detection, peaks in the Hough voting space are taken into account and initial event hypothesis is generated using the spatio-temporal information of the participating STIPs. For event recognition a verification Support Vector Machine is used. An extensive evaluation on benchmark large scale video surveillance dataset (VIRAT) and as well on a small scale benchmark dataset (MSR) shows that the proposed method is applicable on a wide range of continuous visual event recognition applications having extremely challenging conditions. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1077-3142 | ISBN | Medium | ||
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| Notes | ISE | Approved | no | ||
| Call Number |
Admin @ si @ CGR2013 | Serial | 2413 | ||
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| Author | Pau Cano; Debora Gil; Eva Musulen | ||||
| Title | Towards automatic detection of helicobacter pylori in histological samples of gastric tissue | Type | Conference Article | ||
| Year | 2023 | Publication | IEEE International Symposium on Biomedical Imaging | Abbreviated Journal | |
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| Address | Cartagena de Indias; Colombia; April 2023 | ||||
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| Area | Expedition | Conference | ISBI | ||
| Notes | IAM | Approved | no | ||
| Call Number |
Admin @ si @ CGM2023 | Serial | 3953 | ||
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| Author | Victor M. Campello; Polyxeni Gkontra; Cristian Izquierdo; Carlos Martin-Isla; Alireza Sojoudi; Peter M. Full; Klaus Maier-Hein; Yao Zhang; Zhiqiang He; Jun Ma; Mario Parreno; Alberto Albiol; Fanwei Kong; Shawn C. Shadden; Jorge Corral Acero; Vaanathi Sundaresan; Mina Saber; Mustafa Elattar; Hongwei Li; Bjoern Menze; Firas Khader; Christoph Haarburger; Cian M. Scannell; Mitko Veta; Adam Carscadden; Kumaradevan Punithakumar; Xiao Liu; Sotirios A. Tsaftaris; Xiaoqiong Huang; Xin Yang; Lei Li; Xiahai Zhuang; David Vilades; Martin L. Descalzo; Andrea Guala; Lucia La Mura; Matthias G. Friedrich; Ria Garg; Julie Lebel; Filipe Henriques; Mahir Karakas; Ersin Cavus; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Jose F. Rodriguez Palomares; Karim Lekadir | ||||
| Title | Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge | Type | Journal Article | ||
| Year | 2021 | Publication | IEEE Transactions on Medical Imaging | Abbreviated Journal | TMI |
| Volume | 40 | Issue | 12 | Pages | 3543-3554 |
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| Abstract | The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
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Admin @ si @ CGI2021 | Serial | 3653 | ||
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| Author | J. Chazalon; P. Gomez-Kramer; Jean-Christophe Burie; M.Coustaty; S.Eskenazi; Muhammad Muzzamil Luqman; N.Nayef; Marçal Rusiñol; N. Sidere; Jean-Marc Ogier | ||||
| Title | SmartDoc 2017 Video Capture: Mobile Document Acquisition in Video Mode | Type | Conference Article | ||
| Year | 2017 | Publication | 1st International Workshop on Open Services and Tools for Document Analysis | Abbreviated Journal | |
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| Abstract | As mobile document acquisition using smartphones is getting more and more common, along with the continuous improvement of mobile devices (both in terms of computing power and image quality), we can wonder to which extent mobile phones can replace desktop scanners. Modern applications can cope with perspective distortion and normalize the contrast of a document page captured with a smartphone, and in some cases like bottle labels or posters, smartphones even have the advantage of allowing the acquisition of non-flat or large documents. However, several cases remain hard to handle, such as reflective documents (identity cards, badges, glossy magazine cover, etc.) or large documents for which some regions require an important amount of detail. This paper introduces the SmartDoc 2017 benchmark (named “SmartDoc Video Capture”), which aims at
assessing whether capturing documents using the video mode of a smartphone could solve those issues. The task under evaluation is both a stitching and a reconstruction problem, as the user can move the device over different parts of the document to capture details or try to erase highlights. The material released consists of a dataset, an evaluation method and the associated tool, a sample method, and the tools required to extend the dataset. All the components are released publicly under very permissive licenses, and we particularly cared about maximizing the ease of understanding, usage and improvement. |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR-OST | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
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Admin @ si @ CGB2017 | Serial | 2997 | ||
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| Author | Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados | ||||
| Title | A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages | 219-227 | |
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| Abstract | In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks. | ||||
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| Notes | DAG; 600.140; 601.311; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ CFV2020 | Serial | 3451 | ||
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