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Author Diego Velazquez; Pau Rodriguez; Alexandre Lacoste; Issam H. Laradji; Xavier Roca; Jordi Gonzalez
Title Evaluating Counterfactual Explainers Type Journal
Year 2023 Publication Transactions on Machine Learning Research Abbreviated Journal TMLR
Volume Issue Pages
Keywords Explainability; Counterfactuals; XAI
Abstract Explainability methods have been widely used to provide insight into the decisions made by statistical models, thus facilitating their adoption in various domains within the industry. Counterfactual explanation methods aim to improve our understanding of a model by perturbing samples in a way that would alter its response in an unexpected manner. This information is helpful for users and for machine learning practitioners to understand and improve their models. Given the value provided by counterfactual explanations, there is a growing interest in the research community to investigate and propose new methods. However, we identify two issues that could hinder the progress in this field. (1) Existing metrics do not accurately reflect the value of an explainability method for the users. (2) Comparisons between methods are usually performed with datasets like CelebA, where images are annotated with attributes that do not fully describe them and with subjective attributes such as ``Attractive''. In this work, we address these problems by proposing an evaluation method with a principled metric to evaluate and compare different counterfactual explanation methods. The evaluation method is based on a synthetic dataset where images are fully described by their annotated attributes. As a result, we are able to perform a fair comparison of multiple explainability methods in the recent literature, obtaining insights about their performance. We make the code public for the benefit of the research community.
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 (up) Admin @ si @ VRL2023 Serial 3891
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Author Ernest Valveny; Oriol Ramos Terrades; Joan Mas; Marçal Rusiñol
Title Interactive Document Retrieval and Classification. Type Book Chapter
Year 2013 Publication Multimodal Interaction in Image and Video Applications Abbreviated Journal
Volume 48 Issue Pages 17-30
Keywords
Abstract In this chapter we describe a system for document retrieval and classification following the interactive-predictive framework. In particular, the system addresses two different scenarios of document analysis: document classification based on visual appearance and logo detection. These two classical problems of document analysis are formulated following the interactive-predictive model, taking the user interaction into account to make easier the process of annotating and labelling the documents. A system implementing this model in a real scenario is presented and analyzed. This system also takes advantage of active learning techniques to speed up the task of labelling the documents.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor Angel Sappa; Jordi Vitria
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1868-4394 ISBN 978-3-642-35931-6 Medium
Area Expedition Conference
Notes DAG Approved no
Call Number (up) Admin @ si @ VRM2013 Serial 2341
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Author Victor Vaquero; German Ros; Francesc Moreno-Noguer; Antonio Lopez; Alberto Sanfeliu
Title Joint coarse-and-fine reasoning for deep optical flow Type Conference Article
Year 2017 Publication 24th International Conference on Image Processing Abbreviated Journal
Volume Issue Pages 2558-2562
Keywords
Abstract We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets.
Address Beijing; China; September 2017
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 ADAS; 600.118 Approved no
Call Number (up) Admin @ si @ VRM2017 Serial 2898
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Author Gabriel Villalonga; Sebastian Ramos; German Ros; David Vazquez; Antonio Lopez
Title 3d Pedestrian Detection via Random Forest Type Miscellaneous
Year 2014 Publication European Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 231-238
Keywords Pedestrian Detection
Abstract Our demo focuses on showing the extraordinary performance of our novel 3D pedestrian detector along with its simplicity and real-time capabilities. This detector has been designed for autonomous driving applications, but it can also be applied in other scenarios that cover both outdoor and indoor applications.
Our pedestrian detector is based on the combination of a random forest classifier with HOG-LBP features and the inclusion of a preprocessing stage based on 3D scene information in order to precisely determinate the image regions where the detector should search for pedestrians. This approach ends up in a high accurate system that runs real-time as it is required by many computer vision and robotics applications.
Address Zurich; suiza; September 2014
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 ECCV-Demo
Notes ADAS; 600.076 Approved no
Call Number (up) Admin @ si @ VRR2014 Serial 2570
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Author Meritxell Vinyals; Arnau Ramisa; Ricardo Toledo
Title An Evaluation of an Object Recognition Schema using Multiple Region Detectors Type Book Chapter
Year 2007 Publication Artificial Intelligence Research and Development, 163:213–222, ISBN: 978–1–58603–798–7, Proceedings of the 10th International Conference of the ACIA (CCIA’07) 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 ADAS Approved no
Call Number (up) Admin @ si @ VRT2007 Serial 898
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Author Michael Villamizar; A. Sanfeliu; Juan Andrade
Title Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection Type Miscellaneous
Year 2006 Publication 18th International Conference on Pattern Recognition, 81–85 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Hong Kong
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 (up) Admin @ si @ VSA2006a Serial 663
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Author Michael Villamizar; A. Sanfeliu; Juan Andrade
Title Orientation Invariant Features for Multiclass Object Recognition Type Miscellaneous
Year 2006 Publication 11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 655–664 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Cancun (Mexico)
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 (up) Admin @ si @ VSA2006b Serial 664
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Author T. Alejandra Vidal; A. Sanfeliu; Juan Andrade
Title Autonomous Single Camera Exploration Type Miscellaneous
Year 2006 Publication Jornada de Recerca en Automatica, Visio i Robotica 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 (up) Admin @ si @ VSA2006c Serial 680
Permanent link to this record
 
 
Author Henry Velesaca; Patricia Suarez; Dario Carpio; Angel Sappa
Title Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy Type Conference Article
Year 2021 Publication 16th International Symposium on Visual Computing Abbreviated Journal
Volume 13017 Issue Pages 131–143
Keywords
Abstract This paper presents a complete pipeline to perform deep learning-based instance segmentation of different types of grains (e.g., corn, sunflower, soybeans, lentils, chickpeas, mote, and beans). The proposed approach consists of using synthesized image datasets for the training process, which are easily generated according to the category of the instance to be segmented. The synthesized imaging process allows generating a large set of well-annotated grain samples with high variability—as large and high as the user requires. Instance segmentation is performed through a popular deep learning based approach, the Mask R-CNN architecture, but any learning-based instance segmentation approach can be considered. Results obtained by the proposed pipeline show that the strategy of using synthesized image datasets for training instance segmentation helps to avoid the time-consuming image annotation stage, as well as to achieve higher intersection over union and average precision performances. Results obtained with different varieties of grains are shown, as well as comparisons with manually annotated images, showing both the simplicity of the process and the improvements in the performance.
Address Virtual; October 2021
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 ISVC
Notes MSIAU Approved no
Call Number (up) Admin @ si @ VSC2021 Serial 3667
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Author Henry Velesaca; Patricia Suarez; Dario Carpio; Rafael E. Rivadeneira; Angel Sanchez; Angel Morera
Title Video Analytics in Urban Environments: Challenges and Approaches Type Book Chapter
Year 2022 Publication ICT Applications for Smart Cities Abbreviated Journal
Volume 224 Issue Pages 101-121
Keywords
Abstract This chapter reviews state-of-the-art approaches generally present in the pipeline of video analytics on urban scenarios. A typical pipeline is used to cluster approaches in the literature, including image preprocessing, object detection, object classification, and object tracking modules. Then, a review of recent approaches for each module is given. Additionally, applications and datasets generally used for training and evaluating the performance of these approaches are included. This chapter does not pretend to be an exhaustive review of state-of-the-art video analytics in urban environments but rather an illustration of some of the different recent contributions. The chapter concludes by presenting current trends in video analytics in the urban scenario field.
Address September 2022
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title ISRL
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-06306-0 Medium
Area Expedition Conference
Notes MSIAU; MACO Approved no
Call Number (up) Admin @ si @ VSC2022 Serial 3811
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Author R. Valenti; N. Sebe; Theo Gevers
Title What are you looking at? Improving Visual gaze Estimation by Saliency Type Journal Article
Year 2012 Publication International Journal of Computer Vision Abbreviated Journal IJCV
Volume 98 Issue 3 Pages 324-334
Keywords
Abstract Impact factor 2010: 5.15
Impact factor 2011/12?: 5.36
In this paper we present a novel mechanism to obtain enhanced gaze estimation for subjects looking at a scene or an image. The system makes use of prior knowledge about the scene (e.g. an image on a computer screen), to define a probability map of the scene the subject is gazing at, in order to find the most probable location. The proposed system helps in correcting the fixations which are erroneously estimated by the gaze estimation device by employing a saliency framework to adjust the resulting gaze point vector. The system is tested on three scenarios: using eye tracking data, enhancing a low accuracy webcam based eye tracker, and using a head pose tracker. The correlation between the subjects in the commercial eye tracking data is improved by an average of 13.91%. The correlation on the low accuracy eye gaze tracker is improved by 59.85%, and for the head pose tracker we obtain an improvement of 10.23%. These results show the potential of the system as a way to enhance and self-calibrate different visual gaze estimation systems.
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 0920-5691 ISBN Medium
Area Expedition Conference
Notes ALTRES;ISE Approved no
Call Number (up) Admin @ si @ VSG2012 Serial 1848
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Author Henry Velesaca; Patricia Suarez; Raul Mira; Angel Sappa
Title Computer Vision based Food Grain Classification: a Comprehensive Survey Type Journal Article
Year 2021 Publication Computers and Electronics in Agriculture Abbreviated Journal CEA
Volume 187 Issue Pages 106287
Keywords
Abstract This manuscript presents a comprehensive survey on recent computer vision based food grain classification techniques. It includes state-of-the-art approaches intended for different grain varieties. The approaches proposed in the literature are analyzed according to the processing stages considered in the classification pipeline, making it easier to identify common techniques and comparisons. Additionally, the type of images considered by each approach (i.e., images from the: visible, infrared, multispectral, hyperspectral bands) together with the strategy used to generate ground truth data (i.e., real and synthetic images) are reviewed. Finally, conclusions highlighting future needs and challenges are presented.
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 MSIAU; 600.130; 600.122 Approved no
Call Number (up) Admin @ si @ VSM2021 Serial 3576
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Author Jordi Vitria; Joao Sanchez; Miguel Raposo; Mario Hernandez
Title Pattern Recognition and Image Analysis Type Book Whole
Year 2011 Publication 5th Iberian Conference Pattern Recognition and Image Analysis Abbreviated Journal
Volume 6669 Issue Pages
Keywords
Abstract
Address Las Palmas de Gran Canaria. Spain
Corporate Author Thesis
Publisher Springer-Verlag Place of Publication Berlin Editor J. Vitrià; J. Sanchez; M. Raposo; M. Hernandez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-2125 Medium
Area Expedition Conference IbPRIA
Notes OR;MV Approved no
Call Number (up) Admin @ si @ VSR2011 Serial 1730
Permanent link to this record
 
 
Author Henry Velesaca; Patricia Suarez; Angel Sappa; Dario Carpio; Rafael E. Rivadeneira; Angel Sanchez
Title Review on Common Techniques for Urban Environment Video Analytics Type Conference Article
Year 2022 Publication Anais do III Workshop Brasileiro de Cidades Inteligentes Abbreviated Journal
Volume Issue Pages 107-118
Keywords Video Analytics; Review; Urban Environments; Smart Cities
Abstract This work compiles the different computer vision-based approaches
from the state-of-the-art intended for video analytics in urban environments.
The manuscript groups the different approaches according to the typical modules present in video analysis, including image preprocessing, object detection,
classification, and tracking. This proposed pipeline serves as a basic guide to
representing these most representative approaches in this topic of video analysis
that will be addressed in this work. Furthermore, the manuscript is not intended
to be an exhaustive review of the most advanced approaches, but only a list of
common techniques proposed to address recurring problems in this field.
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 WBCI
Notes MSIAU; 601.349 Approved no
Call Number (up) Admin @ si @ VSS2022 Serial 3773
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Author Javier Vazquez; Maria Vanrell; Ramon Baldrich; Francesc Tous
Title Color Constancy by Category Correlation Type Journal Article
Year 2012 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 21 Issue 4 Pages 1997-2007
Keywords
Abstract Finding color representations which are stable to illuminant changes is still an open problem in computer vision. Until now most approaches have been based on physical constraints or statistical assumptions derived from the scene, while very little attention has been paid to the effects that selected illuminants have
on the final color image representation. The novelty of this work is to propose
perceptual constraints that are computed on the corrected images. We define the
category hypothesis, which weights the set of feasible illuminants according to their ability to map the corrected image onto specific colors. Here we choose these colors as the universal color categories related to basic linguistic terms which have been psychophysically measured. These color categories encode natural color statistics, and their relevance across different cultures is indicated by the fact that they have received a common color name. From this category hypothesis we propose a fast implementation that allows the sampling of a large set of illuminants. Experiments prove that our method rivals current state-of-art performance without the need for training algorithmic parameters. Additionally, the method can be used as a framework to insert top-down information from other sources, thus opening further research directions in solving for color constancy.
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 CIC Approved no
Call Number (up) Admin @ si @ VVB2012 Serial 1999
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