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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 (up) 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
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Notes ADAS Approved no
Call Number Admin @ si @ VRT2007 Serial 898
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Author Javier Vazquez; Maria Vanrell; Anna Salvatella; Eduard Vazquez
Title A colour space based on the image content Type Book Chapter
Year 2007 Publication (up) Artificial Intelligence Research and Development, C. Angulo and L. Godo, pp 205–212 IOS Press Abbreviated Journal
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Notes CIC Approved no
Call Number CAT @ cat @ VVS2007 Serial 829
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Author Bogdan Raducanu; Jordi Vitria
Title Real-Time Face Tracking for Context-Aware Computing Type Book Chapter
Year 2005 Publication (up) Artificial Intelligence Research and Development, IOS Press, 91–98 Abbreviated Journal
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Address Amsterdam
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ RaV2005b Serial 616
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Author Agata Lapedriza; Jordi Vitria
Title Experimental Study of the Usefulness of External Face Features for Face Classification Type Book Chapter
Year 2005 Publication (up) Artificial Intelligence Research and Development, IOS Press, 99–106 Abbreviated Journal
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Address Amsterdam
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ LaV2005 Serial 610
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Author Eduard Vazquez; Francesc Tous; Ramon Baldrich; Maria Vanrell
Title n-Dimensional Distribution Reduction Preserving its Structure Type Book Chapter
Year 2006 Publication (up) Artificial Intelligence Research and Development, M. Polit et al. (Eds.), 146: 167–175 Abbreviated Journal
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Abstract
Address IOS Press
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Notes CIC Approved no
Call Number CAT @ cat @ VTB2006a Serial 681
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Author David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri
Title Blood Detection In IVUS Longitudinal Cuts Using AdaBoost With a Novel Feature Stability Criterion Type Conference Article
Year 2007 Publication (up) Artificial Intelligence Research and Development. Proceedings of the 10th International Conference of the ACIA Abbreviated Journal
Volume 163 Issue Pages 197–204
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ISSN ISBN 978-1-58603-798-7 Medium
Area Expedition Conference CCIA’07
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ RRF2007a Serial 831
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Author Jose Elias Yauri; Aura Hernandez-Sabate; Pau Folch; Debora Gil
Title Mental Workload Detection Based on EEG Analysis Type Conference Article
Year 2021 Publication (up) Artificial Intelligent Research and Development. Proceedings 23rd International Conference of the Catalan Association for Artificial Intelligence. Abbreviated Journal
Volume 339 Issue Pages 268-277
Keywords Cognitive states; Mental workload; EEG analysis; Neural Networks.
Abstract The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement.
Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training.
In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation.
Address Virtual; October 20-22 2021
Corporate Author Thesis
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference CCIA
Notes IAM; 600.139; 600.118; 600.145 Approved no
Call Number Admin @ si @ Serial 3723
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Author Adriana Romero; Petia Radeva; Carlo Gatta
Title No more meta-parameter tuning in unsupervised sparse feature learning Type Miscellaneous
Year 2014 Publication (up) Arxiv Abbreviated Journal
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Abstract CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well.
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Notes MILAB; LAMP; 600.079 Approved no
Call Number Admin @ si @ RRG2014 Serial 2471
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Author Wenjuan Gong; Y.Huang; Jordi Gonzalez; Liang Wang
Title An Effective Solution to Double Counting Problem in Human Pose Estimation Type Miscellaneous
Year 2015 Publication (up) Arxiv Abbreviated Journal
Volume Issue Pages
Keywords Pose estimation; double counting problem; mix-ture of parts Model
Abstract The mixture of parts model has been successfully applied to solve the 2D
human pose estimation problem either as an explicitly trained body part model
or as latent variables for pedestrian detection. Even in the era of massive
applications of deep learning techniques, the mixture of parts model is still
effective in solving certain problems, especially in the case with limited
numbers of training samples. In this paper, we consider using the mixture of
parts model for pose estimation, wherein a tree structure is utilized for
representing relations between connected body parts. This strategy facilitates
training and inferencing of the model but suffers from double counting
problems, where one detected body part is counted twice due to lack of
constrains among unconnected body parts. To solve this problem, we propose a
generalized solution in which various part attributes are captured by multiple
features so as to avoid the double counted problem. Qualitative and
quantitative experimental results on a public available dataset demonstrate the
effectiveness of our proposed method.

An Effective Solution to Double Counting Problem in Human Pose Estimation – ResearchGate. Available from: http://www.researchgate.net/publication/271218491AnEffectiveSolutiontoDoubleCountingProbleminHumanPose_Estimation [accessed Oct 22, 2015].
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Notes ISE; 600.078 Approved no
Call Number Admin @ si @ GHG2015 Serial 2590
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Author Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
Title Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos Type Miscellaneous
Year 2015 Publication (up) Arxiv Abbreviated Journal
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Abstract Egocentric images offer a hands-free way to record daily experiences and special events, where social interactions are of special interest. A natural question that arises is how to extract and track the appearance of multiple persons in a social event captured by a wearable camera. In this paper, we propose a novel method to find correspondences of multiple-faces in low temporal resolution egocentric sequences acquired through a wearable camera. This kind of sequences imposes additional challenges to the multitracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution (2 fpm), abrupt changes in the field of view, in illumination conditions and in the target location are very frequent. To overcome such a difficulty, we propose to generate, for each detected face, a set of correspondences along the whole sequence that we call tracklet and to take advantage of their redundancy to deal with both false positive face detections and unreliable tracklets. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which are aimed to correspond to specific persons. Finally, a prototype tracklet is extracted for each eBoT. We validated our method over a dataset of 18.000 images from 38 egocentric sequences with 52 trackable persons and compared to the state-of-the-art methods, demonstrating its effectiveness and robustness.
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Notes MILAB Approved no
Call Number Admin @ si @ ADR2015b Serial 2713
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Author Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez
Title Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest Type Miscellaneous
Year 2016 Publication (up) Arxiv Abbreviated Journal
Volume Issue Pages
Keywords Domain Adaptation; Pedestrian detection; Random Forest
Abstract Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved.
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Notes ADAS Approved no
Call Number ADAS @ adas @ MVJ2016 Serial 2868
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Author Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven
Title End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks Type Miscellaneous
Year 2017 Publication (up) Arxiv Abbreviated Journal
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Abstract arXiv:1703.03305
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies.
We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them.
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Notes HuPBA; ISE; 600.098; 600.119 Approved no
Call Number Admin @ si @ GGM2017 Serial 2932
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Author Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen
Title Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images Type Miscellaneous
Year 2023 Publication (up) Arxiv Abbreviated Journal
Volume Issue Pages 107241
Keywords
Abstract This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori.
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Notes IAM Approved no
Call Number Admin @ si @ CCG2023 Serial 3855
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Author Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal
Title SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification Type Miscellaneous
Year 2018 Publication (up) Arxiv Abbreviated Journal
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Abstract Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction.
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Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ DDT2018 Serial 3085
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Author Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guclu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia C. S. Liem; Marcel A. J. van Gerven; Rob van Lier
Title Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos Type Miscellaneous
Year 2018 Publication (up) Arxiv Abbreviated Journal
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Abstract Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.
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Notes HUPBA Approved no
Call Number Admin @ si @ JKS2018 Serial 3095
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