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Author Xim Cerda-Company; C. Alejandro Parraga; Xavier Otazu
Title (down) Which tone-mapping is the best? A comparative study of tone-mapping perceived quality Type Abstract
Year 2014 Publication Perception Abbreviated Journal
Volume 43 Issue Pages 106
Keywords
Abstract Perception 43 ECVP Abstract Supplement
High-dynamic-range (HDR) imaging refers to the methods designed to increase the brightness dynamic range present in standard digital imaging techniques. This increase is achieved by taking the same picture under di erent exposure values and mapping the intensity levels into a single image by way of a tone-mapping operator (TMO). Currently, there is no agreement on how to evaluate the quality
of di erent TMOs. In this work we psychophysically evaluate 15 di erent TMOs obtaining rankings based on the perceived properties of the resulting tone-mapped images. We performed two di erent experiments on a CRT calibrated display using 10 subjects: (1) a study of the internal relationships between grey-levels and (2) a pairwise comparison of the resulting 15 tone-mapped images. In (1) observers internally matched the grey-levels to a reference inside the tone-mapped images and in the real scene. In (2) observers performed a pairwise comparison of the tone-mapped images alongside the real scene. We obtained two rankings of the TMOs according their performance. In (1) the best algorithm
was ICAM by J.Kuang et al (2007) and in (2) the best algorithm was a TMO by Krawczyk et al (2005). Our results also show no correlation between these two rankings.
Address
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECVP
Notes NEUROBIT; 600.074 Approved no
Call Number Admin @ si @ CPO2014 Serial 2527
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Author Javad Zolfaghari Bengar; Bogdan Raducanu; Joost Van de Weijer
Title (down) When Deep Learners Change Their Mind: Learning Dynamics for Active Learning Type Conference Article
Year 2021 Publication 19th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal
Volume 13052 Issue 1 Pages 403-413
Keywords
Abstract Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.
Address September 2021
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 CAIP
Notes LAMP; Approved no
Call Number Admin @ si @ ZRV2021 Serial 3673
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Author Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris
Title (down) Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code Type Conference Article
Year 2015 Publication European Conference on Visual Perception ECVP2015 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Liverpool; uk; 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 ECVP
Notes NEUROBIT; Approved no
Call Number Admin @ si @ POW2015 Serial 2633
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Author A. Auge; Javier Varona; Juan J. Villanueva
Title (down) Tumour Segmentation in Mammographies with Neural Networks. Application to Tumoural Volume Approximation. Type Conference Article
Year 1997 Publication Proceedings of the VII NSPRIA, Vol. II, CVC–UAB 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 Approved no
Call Number ISE @ ise @ AVV1997 Serial 208
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Author C. Alejandro Parraga; Robert Benavente; Maria Vanrell
Title (down) Towards a general model of colour categorization which considers context Type Journal Article
Year 2010 Publication Perception. ECVP Abstract Supplement Abbreviated Journal PER
Volume 39 Issue Pages 86
Keywords
Abstract In two previous experiments [Parraga et al, 2009 J. of Im. Sci. and Tech 53(3) 031106; Benavente et al,2009 Perception 38 ECVP Supplement, 36] the boundaries of basic colour categories were measured.
In the first experiment, samples were presented in isolation (ie on a dark background) and boundaries were measured using a yes/no paradigm. In the second, subjects adjusted the chromaticity of a sample presented on a random Mondrian background to find the boundary between pairs of adjacent colours.
Results from these experiments showed significant di erences but it was not possible to conclude whether this discrepancy was due to the absence/presence of a colourful background or to the di erences in the paradigms used. In this work, we settle this question by repeating the first experiment (ie samples presented on a dark background) using the second paradigm. A comparison of results shows that
although boundary locations are very similar, boundaries measured in context are significantly di erent(more di use) than those measured in isolation (confirmed by a Student’s t-test analysis on the subject’s answers statistical distributions). In addition, we completed the mapping of colour name space by measuring the boundaries between chromatic colours and the achromatic centre. With these results we
completed our parametric fuzzy-sets model of colour naming space.
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Notes CIC Approved no
Call Number CAT @ cat @ PBV2010b Serial 1326
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Author Stefan Ameling; Stephan Wirth; Dietrich Paulus; Gerard Lacey; Fernando Vilariño
Title (down) Texture-based Polyp Detection in Colonoscopy Type Conference Article
Year 2009 Publication Proc. BILDVERARBEITUNG FÜR DIE MEDIZIN Abbreviated Journal
Volume Issue Pages
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Abstract
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 800 Expedition Conference
Notes MV;SIAI Approved no
Call Number fernando @ fernando @ Serial 2428
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Author Josep Llados; Ernest Valveny; Enric Marti
Title (down) Symbol Recognition in Document Image Analysis: Methods and Challenges Type Book Chapter
Year 2000 Publication Recent Research Developments in Pattern Recognition, Transworld Research Network, Abbreviated Journal
Volume 1 Issue Pages 151–178.
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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 81-86846-61-1 Medium
Area Expedition Conference
Notes DAG;IAM Approved no
Call Number IAM @ iam @ LVM2000 Serial 1575
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Author Josep Llados; Enric Marti; Juan J.Villanueva
Title (down) Symbol recognition by error-tolerant subgraph matching between region adjacency graphs Type Journal Article
Year 2001 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal
Volume 23 Issue 10 Pages 1137-1143
Keywords
Abstract The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.
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Notes DAG;IAM;ISE; Approved no
Call Number IAM @ iam @ LMV2001 Serial 1581
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Author Hao Fang; Ajian Liu; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zhen Lei
Title (down) Surveillance Face Presentation Attack Detection Challenge Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal
Volume Issue Pages 6360-6370
Keywords
Abstract Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras. Based on this dataset and protocol-3 for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios.
Address Vancouver; Canada; June 2023
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 CVPRW
Notes HuPBA Approved no
Call Number Admin @ si @ FLW2023 Serial 3917
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Author Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Benedi
Title (down) Structure Detection and Segmentation of Documents Using 2D Stochastic Context-Free Grammars Type Journal Article
Year 2015 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume 150 Issue A Pages 147-154
Keywords document image analysis; stochastic context-free grammars; text classi cation features
Abstract In this paper we de ne a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents.
Two sets of text classi cation features are used to perform an initial classi cation of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for Probabilistic Graphical Models
and the results showed that the proposed grammatical model outperformed
the other methods. Furthermore, grammars also provide the document structure
along with its segmentation.
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes DAG; 601.158; 600.077; 600.061 Approved no
Call Number Admin @ si @ ACS2015 Serial 2531
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Author Manuel Graña; Bogdan Raducanu
Title (down) Special Issue on Bioinspired and knowledge based techniques and applications Type Journal Article
Year 2015 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume Issue Pages 1-3
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Abstract
Address
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Notes LAMP; Approved no
Call Number Admin @ si @ GrR2015 Serial 2598
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Author Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo
Title (down) Single view facial hair 3D reconstruction Type Conference Article
Year 2019 Publication 9th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 11867 Issue Pages 423-436
Keywords 3D Vision; Shape Reconstruction; Facial Hair Modeling
Abstract n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches.
Address Madrid; July 2019
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 IbPRIA
Notes MSIAU; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ Serial 3707
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Author Felipe Lumbreras; Joan Serrat
Title (down) Segmentation of petrographical images of marbles Type Journal Article
Year 1996 Publication Computers and Geosciences Abbreviated Journal
Volume 22 Issue 5 Pages 547–558
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Abstract
Address
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Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ LuS1996b Serial 82
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Author Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras
Title (down) Segmentation of aerial images for plausible detail synthesis Type Journal Article
Year 2018 Publication Computers & Graphics Abbreviated Journal CG
Volume 71 Issue Pages 23-34
Keywords Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation
Abstract The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0097-8493 ISBN Medium
Area Expedition Conference
Notes MSIAU; 600.086; 600.118 Approved no
Call Number Admin @ si @ ACC2018 Serial 3147
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Author Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu
Title (down) Saliency for fine-grained object recognition in domains with scarce training data Type Journal Article
Year 2019 Publication Pattern Recognition Abbreviated Journal PR
Volume 94 Issue Pages 62-73
Keywords
Abstract This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate a large dataset. The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network’s performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes LAMP; 600.109; 600.141; 600.120 Approved no
Call Number Admin @ si @ FGW2019 Serial 3264
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