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Author Xialei Liu; Marc Masana; Luis Herranz; Joost Van de Weijer; Antonio Lopez; Andrew Bagdanov
Title (down) Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting Type Conference Article
Year 2018 Publication 24th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 2262-2268
Keywords
Abstract In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of
a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and
Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting.
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 ICPR
Notes LAMP; ADAS; 601.305; 601.109; 600.124; 600.106; 602.200; 600.120; 600.118 Approved no
Call Number Admin @ si @ LMH2018 Serial 3160
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Author Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva
Title (down) ROC curves and video analysis optimization in intestinal capsule endoscopy Type Journal Article
Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 27 Issue 8 Pages 875–881
Keywords ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy
Abstract Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions.
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 800 Expedition Conference
Notes MILAB;MV;SIAI Approved no
Call Number BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 Serial 647
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Author Arnau Ramisa; Adriana Tapus; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras
Title (down) Robust Vision-Based Localization using Combinations of Local Feature Regions Detectors Type Journal Article
Year 2009 Publication Autonomous Robots Abbreviated Journal AR
Volume 27 Issue 4 Pages 373-385
Keywords
Abstract This paper presents a vision-based approach for mobile robot localization. The model of the environment is topological. The new approach characterizes a place using a signature. This signature consists of a constellation of descriptors computed over different types of local affine covariant regions extracted from an omnidirectional image acquired rotating a standard camera with a pan-tilt unit. This type of representation permits a reliable and distinctive environment modelling. Our objectives were to validate the proposed method in indoor environments and, also, to find out if the combination of complementary local feature region detectors improves the localization versus using a single region detector. Our experimental results show that if false matches are effectively rejected, the combination of different covariant affine region detectors increases notably the performance of the approach by combining the different strengths of the individual detectors. In order to reduce the localization time, two strategies are evaluated: re-ranking the map nodes using a global similarity measure and using standard perspective view field of 45°.
In order to systematically test topological localization methods, another contribution proposed in this work is a novel method to see the degradation in localization performance as the robot moves away from the point where the original signature was acquired. This allows to know the robustness of the proposed signature. In order for this to be effective, it must be done in several, variated, environments that test all the possible situations in which the robot may have to perform localization.
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 0929-5593 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ RTA2009 Serial 1245
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Author Ariel Amato; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez
Title (down) Robust Real-Time Background Subtraction Based on Local Neighborhood Patterns Type Journal Article
Year 2010 Publication EURASIP Journal on Advances in Signal Processing Abbreviated Journal EURASIPJ
Volume Issue Pages 7
Keywords
Abstract Article ID 901205
This paper describes an efficient background subtraction technique for detecting moving objects. The proposed approach is able to overcome difficulties like illumination changes and moving shadows. Our method introduces two discriminative features based on angular and modular patterns, which are formed by similarity measurement between two sets of RGB color vectors: one belonging to the background image and the other to the current image. We show how these patterns are used to improve foreground detection in the presence of moving shadows and in the case when there are strong similarities in color between background and foreground pixels. Experimental results over a collection of public and own datasets of real image sequences demonstrate that the proposed technique achieves a superior performance compared with state-of-the-art methods. Furthermore, both the low computational and space complexities make the presented algorithm feasible for real-time applications.
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 1110-8657 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number ISE @ ise @ AMR2010 Serial 1463
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Author Dani Rowe; Ignasi Rius; Jordi Gonzalez; Juan J. Villanueva
Title (down) Robust Particle Filtering for Object Tracking Type Miscellaneous
Year 2005 Publication 13th International Conference on Image Analysis and Processing (ICIAP’2005), LNCS 3617: 1158–1165, ISBN 3–540–28869–4 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Cagliary (Italy)
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 ISE @ ise @ RRG2005e Serial 577
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Author Pejman Rasti; Salma Samiei; Mary Agoyi; Sergio Escalera; Gholamreza Anbarjafari
Title (down) Robust non-blind color video watermarking using QR decomposition and entropy analysis Type Journal Article
Year 2016 Publication Journal of Visual Communication and Image Representation Abbreviated Journal JVCIR
Volume 38 Issue Pages 838-847
Keywords Video watermarking; QR decomposition; Discrete Wavelet Transformation; Chirp Z-transform; Singular value decomposition; Orthogonal–triangular decomposition
Abstract Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks.
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;MILAB; Approved no
Call Number Admin @ si @RSA2016 Serial 2766
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Author Dani Rowe; Ivan Huerta; Jordi Gonzalez; Juan J. Villanueva
Title (down) Robust Multiple-People Tracking Using Colour-Based Particle Filters Type Book Chapter
Year 2007 Publication 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:113–120 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Girona (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 ISE @ ise @ RHG2007 Serial 782
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Author Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras; T. Graf
Title (down) Robust lane markings detection and road geometry computation Type Journal Article
Year 2010 Publication International Journal of Automotive Technology Abbreviated Journal IJAT
Volume 11 Issue 3 Pages 395–407
Keywords lane markings
Abstract Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known.
Address
Corporate Author Thesis
Publisher The Korean Society of Automotive Engineers Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1229-9138 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ LSC2010 Serial 1300
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Author Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras
Title (down) Robust Lane Lines Detection and Quantitative Assessment Type Conference Article
Year 2007 Publication 3rd Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 4477 Issue Pages 274–281
Keywords lane markings
Abstract
Address Girona (Spain)
Corporate Author Thesis
Publisher Place of Publication Editor J. Marti et al
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference IbPRIA
Notes ADAS Approved no
Call Number ADAS @ adas @ LSC2007 Serial 881
Permanent link to this record
 
 
Author Carlo Gatta; Oriol Pujol; O. Rodriguez-Leor; J. Mauri; Petia Radeva
Title (down) Robust Image-based IVUS Pullbacks Gating Type Book Chapter
Year 2008 Publication Proceedings 11th International ConferenceMedical Image Computing and Computer–Assisted Intervention Abbreviated Journal
Volume 5242 Issue Pages 518–525
Keywords
Abstract
Address NY (USA)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference MICCAI
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ GPR2008a Serial 1037
Permanent link to this record
 
 
Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu
Title (down) Robust Head Gestures Recognition for Assistive Technology Type Book Chapter
Year 2014 Publication Pattern Recognition Abbreviated Journal
Volume 8495 Issue Pages 152-161
Keywords
Abstract This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.
Address
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-319-07490-0 Medium
Area Expedition Conference
Notes OR;MV Approved no
Call Number Admin @ si @ TSR2014b Serial 2505
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Author Lei Kang
Title (down) Robust Handwritten Text Recognition in Scarce Labeling Scenarios: Disentanglement, Adaptation and Generation Type Book Whole
Year 2020 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Handwritten documents are not only preserved in historical archives but also widely used in administrative documents such as cheques and claims. With the rise of the deep learning era, many state-of-the-art approaches have achieved good performance on specific datasets for Handwritten Text Recognition (HTR). However, it is still challenging to solve real use cases because of the varied handwriting styles across different writers and the limited labeled data. Thus, both explorin a more robust handwriting recognition architectures and proposing methods to diminish the gap between the source and target data in an unsupervised way are
demanded.
In this thesis, firstly, we explore novel architectures for HTR, from Sequence-to-Sequence (Seq2Seq) method with attention mechanism to non-recurrent Transformer-based method. Secondly, we focus on diminishing the performance gap between source and target data in an unsupervised way. Finally, we propose a group of generative methods for handwritten text images, which could be utilized to increase the training set to obtain a more robust recognizer. In addition, by simply modifying the generative method and joining it with a recognizer, we end up with an effective disentanglement method to distill textual content from handwriting styles so as to achieve a generalized recognition performance.
We outperform state-of-the-art HTR performances in the experimental results among different scientific and industrial datasets, which prove the effectiveness of the proposed methods. To the best of our knowledge, the non-recurrent recognizer and the disentanglement method are the first contributions in the handwriting recognition field. Furthermore, we have outlined the potential research lines, which would be interesting to explore in the future.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Alicia Fornes;Marçal Rusiñol;Mauricio Villegas
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-122714-0-9 Medium
Area Expedition Conference
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ Kan20 Serial 3482
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Author Laura Igual; Agata Lapedriza; Ricard Borras
Title (down) Robust Gait-Based Gender Classification using Depth Cameras Type Journal Article
Year 2013 Publication EURASIP Journal on Advances in Signal Processing Abbreviated Journal EURASIPJ
Volume 37 Issue 1 Pages 72-80
Keywords
Abstract This article presents a new approach for gait-based gender recognition using depth cameras, that can run in real time. The main contribution of this study is a new fast feature extraction strategy that uses the 3D point cloud obtained from the frames in a gait cycle. For each frame, these points are aligned according to their centroid and grouped. After that, they are projected into their PCA plane, obtaining a representation of the cycle particularly robust against view changes. Then, final discriminative features are computed by first making a histogram of the projected points and then using linear discriminant analysis. To test the method we have used the DGait database, which is currently the only publicly available database for gait analysis that includes depth information. We have performed experiments on manually labeled cycles and over whole video sequences, and the results show that our method improves the accuracy significantly, compared with state-of-the-art systems which do not use depth information. Furthermore, our approach is insensitive to illumination changes, given that it discards the RGB information. That makes the method especially suitable for real applications, as illustrated in the last part of the experiments section.
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 MILAB; OR;MV Approved no
Call Number Admin @ si @ ILB2013 Serial 2144
Permanent link to this record
 
 
Author Mikhail Mozerov; V. Kober; I.A. Ovseyevich
Title (down) Robust Dynamic Programming Algorithm for Motion Detection and Estimation Type Journal
Year 2007 Publication Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
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 ISE Approved no
Call Number ISE @ ise @ MKO2007 Serial 810
Permanent link to this record
 
 
Author Angel Morera; Angel Sanchez; Angel Sappa; Jose F. Velez
Title (down) Robust Detection of Outdoor Urban Advertising Panels in Static Images Type Conference Article
Year 2019 Publication 18th International Conference on Practical Applications of Agents and Multi-Agent Systems Abbreviated Journal
Volume Issue Pages 246-256
Keywords Object detection; Urban ads panels; Deep learning; Single Shot Detector (SSD) architecture; Intersection over Union (IoU) metric; Augmented Reality
Abstract One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising panels. For such a purpose, a previous stage is to accurately detect and locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based on a deep neural network architecture that minimizes the number of false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection over Union (IoU) accuracy metric make this proposal applicable in real complex urban images.
Address Aquila; Italia; June 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 PAAMS
Notes MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ MSS2019 Serial 3270
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