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Author Oriol Pujol; Petia Radeva; Jordi Vitria
Title Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes Type Journal
Year 2006 Publication (up) IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6): 1007–1012 Abbreviated Journal
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Notes OR;MILAB;HuPBA;MV Approved no
Call Number BCNPCL @ bcnpcl @ PRV2006a Serial 646
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Author Jaume Amores; N. Sebe; Petia Radeva
Title Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms Type Journal
Year 2007 Publication (up) IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29(10):1818–1833, (ISI 3,81) Abbreviated Journal
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Notes ADAS;MILAB Approved no
Call Number ADAS @ adas @ ASR2007b Serial 922
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Author Juan Andrade; A. Sanfeliu
Title The effects of partial observability when building fully correlated maps Type Journal
Year 2005 Publication (up) IEEE Transactions on Robotics, 21(4):771–777 (IF: 1.486) Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ AnS2005 Serial 594
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Author Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera
Title A Gesture Recognition System for Detecting Behavioral Patterns of ADHD Type Journal Article
Year 2016 Publication (up) IEEE Transactions on System, Man and Cybernetics, Part B Abbreviated Journal TSMCB
Volume 46 Issue 1 Pages 136-147
Keywords Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data
Abstract We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context.
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Notes HuPBA; MILAB; Approved no
Call Number Admin @ si @ BHE2016 Serial 2566
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Author Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez
Title Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments Type Journal Article
Year 2014 Publication (up) IEEE Transactions on Systems, Man and Cybernetics (Part B) Abbreviated Journal TSMCB
Volume 44 Issue 12 Pages 2379-2390
Keywords
Abstract The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios.
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ISSN 2168-2267 ISBN Medium
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Notes HuPBA; ISE; 600.078;MILAB Approved no
Call Number Admin @ si @ LRE2014 Serial 2442
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Author Fadi Dornaika; Bogdan Raducanu
Title 3D Face Pose Detection and Tracking Using Monocular Videos: Tool and Application Type Journal
Year 2008 Publication (up) IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) Abbreviated Journal
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ DoR2008d Serial 1109
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Author Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva
Title Circular Blurred Shape Model for Multiclass Symbol Recognition Type Journal Article
Year 2011 Publication (up) IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) Abbreviated Journal TSMCB
Volume 41 Issue 2 Pages 497-506
Keywords
Abstract In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations.
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ISSN 1083-4419 ISBN Medium
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Notes MILAB; DAG;HuPBA Approved no
Call Number Admin @ si @ EFP2011 Serial 1784
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Author Jordi Vitria; M. Bressan; Petia Radeva
Title Bayesian classification of cork stoppers using class-conditional independent component analysis Type Journal
Year 2006 Publication (up) IEEE Transactions on Systems, Man and Cybernetics (Part C), 36(6) Abbreviated Journal
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ VBR2006 Serial 723
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Author Jordi Vitria; M. Bressan; Petia Radeva
Title Bayesian classification of cork stoppers using class-conditional independent component analysis Type Journal
Year 2007 Publication (up) IEEE Transactions on Systems, Man and Cybernetics (Part C), 37(1): 32–38 (ISI 0,482) Abbreviated Journal
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ VBR2007 Serial 795
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Author David Masip; Agata Lapedriza; Jordi Vitria
Title Boosted Online Learning for Face Recognition Type Journal Article
Year 2009 Publication (up) IEEE Transactions on Systems, Man and Cybernetics part B Abbreviated Journal TSMCB
Volume 39 Issue 2 Pages 530–538
Keywords
Abstract Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight.
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ISSN 1083–4419 ISBN Medium
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MLV2009 Serial 1155
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Author Fadi Dornaika; Bogdan Raducanu
Title Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application Type Journal Article
Year 2009 Publication (up) IEEE Transactions on Systems, Man and Cybernetics part B Abbreviated Journal TSMCB
Volume 39 Issue 4 Pages 935–944
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Abstract Recently, we have proposed a real-time tracker that simultaneously tracks the 3-D head pose and facial actions in monocular video sequences that can be provided by low quality cameras. This paper has two main contributions. First, we propose an automatic 3-D face pose initialization scheme for the real-time tracker by adopting a 2-D face detector and an eigenface system. Second, we use the proposed methods-the initialization and tracking-for enhancing the human-machine interaction functionality of an AIBO robot. More precisely, we show how the orientation of the robot's camera (or any active vision system) can be controlled through the estimation of the user's head pose. Applications based on head-pose imitation such as telepresence, virtual reality, and video games can directly exploit the proposed techniques. Experiments on real videos confirm the robustness and usefulness of the proposed methods.
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ DoR2009a Serial 1218
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Author Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva
Title Occlusion handling via random subspace classifiers for human detection Type Journal Article
Year 2014 Publication (up) IEEE Transactions on Systems, Man, and Cybernetics (Part B) Abbreviated Journal TSMCB
Volume 44 Issue 3 Pages 342-354
Keywords Pedestriand Detection; occlusion handling
Abstract This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes
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ISSN 2168-2267 ISBN Medium
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Notes ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076 Approved no
Call Number ADAS @ adas @ MVL2014 Serial 2213
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Author Aura Hernandez-Sabate; Debora Gil; Jaume Garcia; Enric Marti
Title Image-based Cardiac Phase Retrieval in Intravascular Ultrasound Sequences Type Journal Article
Year 2011 Publication (up) IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control Abbreviated Journal T-UFFC
Volume 58 Issue 1 Pages 60-72
Keywords 3-D exploring; ECG; band-pass filter; cardiac motion; cardiac phase retrieval; coronary arteries; electrocardiogram signal; image intensity local mean evolution; image-based cardiac phase retrieval; in vivo pullbacks acquisition; intravascular ultrasound sequences; longitudinal motion; signal extrema; time 36 ms; band-pass filters; biomedical ultrasonics; cardiovascular system; electrocardiography; image motion analysis; image retrieval; image sequences; medical image processing; ultrasonic imaging
Abstract Longitudinal motion during in vivo pullbacks acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a band-pass filter centered at the main cardiac frequency. Phase is retrieved by computing signal extrema. The average frame processing time using our setup is 36 ms. Comparison to manually sampled sequences encourages a deeper study comparing them to ECG signals.
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ISSN 0885-3010 ISBN Medium
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Notes IAM;ADAS Approved no
Call Number IAM @ iam @ HGG2011 Serial 1546
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Author Mohammad N. S. Jahromi; Morten Bojesen Bonderup; Maryam Asadi-Aghbolaghi; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Shohreh Kasaei; Thomas B. Moeslund; Gholamreza Anbarjafari
Title Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context Type Conference Article
Year 2018 Publication (up) IEEE Winter Applications of Computer Vision Workshops Abbreviated Journal
Volume Issue Pages 28-36
Keywords IEEE Winter Applications of Computer Vision Workshops
Abstract Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users.
Address Lake Tahoe; USA; March 2018
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Area Expedition Conference WACVW
Notes HUPBA; 602.133 Approved no
Call Number Admin @ si @ JBA2018 Serial 3121
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Author German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez
Title Vision-based Offline-Online Perception Paradigm for Autonomous Driving Type Conference Article
Year 2015 Publication (up) IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 231 - 238
Keywords Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation
Abstract Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community.
Address Hawaii; January 2015
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Area ACDC Expedition Conference WACV
Notes ADAS; 600.076 Approved no
Call Number ADAS @ adas @ RRG2015 Serial 2499
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