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Author Bart M. Ter Haar Romeny; W. Niessen; J. Weickert; P. Van Roermund; W. Van Enk; Antonio Lopez; R. Maas
Title Orientation detection of trabecular bone Type Miscellaneous
Year 1996 Publication (down) Biophysics and Molecular Biology, International Biophysics Congress. Volume 65, pgs. P–H5–43 Abbreviated Journal
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Notes ADAS Approved no
Call Number ADAS @ adas @ HNW1996 Serial 489
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Author Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde
Title Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid ℓ1 - ℓ0 layer decomposition domain Type Journal Article
Year 2021 Publication (down) Biomedical Signal Processing and Control Abbreviated Journal BSPC
Volume 68 Issue Pages 102535
Keywords
Abstract Medical image fusion plays an important role in the clinical diagnosis of several critical neurological diseases by merging complementary information available in multimodal images. In this paper, a novel CT-MR neurological image fusion framework is proposed using an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ1 − ℓ0 decomposition domain using gray wolf optimization to preserve the structural as well as texture information present in source CT and MR images. Initially, the source images are subjected to two-scale ℓ1 − ℓ0 decomposition with optimized parameters, giving a scale-1 detail layer, a scale-2 detail layer and a scale-2 base layer. Two detail layers at scale-1 and 2 are fused using an optimized biologically inspired neural model and weighted average scheme based on local energy and modified spatial frequency to maximize the preservation of edges and local textures, respectively, while the scale-2 base layer gets fused using choose max rule to preserve the background information. To optimize the hyper-parameters of hybrid ℓ1 − ℓ0 decomposition and biologically inspired neural model, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image obtained by adding all the fused components. The fusion performance is analyzed by conducting extensive experiments on different CT-MR neurological images. Experimental results indicate that the proposed method provides better-fused images and outperforms the other state-of-the-art fusion methods in both visual and quantitative assessments.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ DGR2021b Serial 3636
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Author Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Xavier Jimenez ; Oscar Vilarroya; Petia Radeva
Title A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder Type Journal Article
Year 2011 Publication (down) BioMedical Engineering Online Abbreviated Journal BEO
Volume 10 Issue 105 Pages 1-23
Keywords Brain caudate nucleus; segmentation; MRI; atlas-based strategy; Graph Cut framework
Abstract Background
Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations.

Method
We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure.

Results
We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis.

Conclusion
CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.
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 1475-925X ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ ISH2011 Serial 1882
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Author C. Alejandro Parraga
Title Perceptual Psychophysics Type Book Chapter
Year 2015 Publication (down) Biologically-Inspired Computer Vision: Fundamentals and Applications Abbreviated Journal
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Publisher Place of Publication Editor G.Cristobal; M.Keil; L.Perrinet
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-3-527-41264-8 Medium
Area Expedition Conference
Notes CIC; 600.074 Approved no
Call Number Admin @ si @ Par2015 Serial 2600
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Author Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Francesca Vidal; Zaida Sarrate
Title Noves perspectives en l estudi de la territorialitat cromosomica de cel·lules germinals masculines: estudis tridimensionals Type Journal
Year 2017 Publication (down) Biologia de la Reproduccio Abbreviated Journal JBR
Volume 15 Issue Pages 73-78
Keywords
Abstract In somatic cells, chromosomes occupy specific nuclear regions called chromosome territories which are involved in the
maintenance and regulation of the genome. Preliminary data in male germ cells also suggest the importance of chromosome
territoriality in cell functionality. Nevertheless, the specific characteristics of testicular tissue (presence of different
cell types with different morphological characteristics, in different stages of development and with different ploidy)
makes difficult to achieve conclusive results. In this study we have developed a methodology to approach the threedimensional
study of all chromosome territories in male germ cells from C57BL/6J mice (Mus musculus). The method
includes the following steps: i) Optimized cell fixation to obtain an optimal preservation of the three-dimensionality cell
morphology, ii) Chromosome identification by FISH (Chromoprobe Multiprobe® OctoChrome™ Murine System; Cytocell)
and confocal microscopy (TCS-SP5, Leica Microsystems), iii) Cell type identification by immunofluorescence
iv) Image analysis using Matlab scripts, v) Numerical data extraction related to chromosome features, chromosome
radial position and chromosome relative position. This methodology allows the unequivocally identification and the
analysis of the chromosome territories of all spermatogenic stages. Results will provide information about the features
that determine chromosomal position, preferred associations between chromosomes, and the relationship between chromosome
positioning and genome regulation.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-697-3767-5 Medium
Area Expedition Conference
Notes IAM; 600.096; 600.145 Approved no
Call Number Admin @ si @ SBG2017c Serial 2961
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Author Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera
Title Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation Type Conference Article
Year 2020 Publication (down) Bioimage computation workshop Abbreviated Journal
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Abstract
Address Virtual; August 2020
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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 Expedition Conference ECCVW
Notes HUPBA Approved no
Call Number Admin @ si @ AAF2020 Serial 3520
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Author David Geronimo; Angel Sappa; Antonio Lopez
Title Stereo-based Candidate Generation for Pedestrian Protection Systems Type Book Chapter
Year 2010 Publication (down) Binocular Vision: Development, Depth Perception and Disorders Abbreviated Journal
Volume Issue 9 Pages 189–208
Keywords Pedestrian Detection
Abstract This chapter describes a stereo-based algorithm that provides candidate image windows to a latter 2D classification stage in an on-board pedestrian detection system. The proposed algorithm, which consists of three stages, is based on the use of both stereo imaging and scene prior knowledge (i.e., pedestrians are on the ground) to reduce the candidate searching space. First, a successful road surface fitting algorithm provides estimates on the relative ground-camera pose. This stage directs the search toward the road area thus avoiding irrelevant regions like the sky. Then, three different schemes are used to scan the estimated road surface with pedestrian-sized windows: (a) uniformly distributed through the road surface (3D); (b) uniformly distributed through the image (2D); (c) not uniformly distributed but according to a quadratic function (combined 2D-3D). Finally, the set of candidate windows is reduced by analyzing their 3D content. Experimental results of the proposed algorithm, together with statistics of searching space reduction are provided.
Address
Corporate Author Thesis
Publisher NOVA Publishers Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ GSL2010 Serial 1301
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Author Oriol Ramos Terrades; Albert Berenguel; Debora Gil
Title A Flexible Outlier Detector Based on a Topology Given by Graph Communities Type Journal Article
Year 2022 Publication (down) Big Data Research Abbreviated Journal BDR
Volume 29 Issue Pages 100332
Keywords Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors
Abstract Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors.
This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings.
Address August 28, 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 Approved no
Call Number Admin @ si @ RBG2022a Serial 3718
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Author Thierry Brouard; A. Delaplace; Muhammad Muzzamil Luqman; H. Cardot; Jean-Yves Ramel
Title Design of Evolutionary Methods Applied to the Learning of Bayesian Nerwork Structures Type Book Chapter
Year 2010 Publication (down) Bayesian Network Abbreviated Journal
Volume Issue Pages 13-37
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Abstract
Address
Corporate Author Thesis
Publisher Sciyo Place of Publication Editor Ahmed Rebai
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-953-307-124-4 Medium
Area Expedition Conference
Notes Approved no
Call Number Admin @ si @ BDL2010 Serial 1461
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Author Xavier Otazu; Olivier Penacchio; Xim Cerda-Company
Title An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort Type Conference Article
Year 2015 Publication (down) Barcelona Computational, Cognitive and Systems Neuroscience Abbreviated Journal
Volume Issue Pages
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Address Barcelona; June 2015
Corporate Author Thesis
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference BARCCSYN
Notes NEUROBIT; Approved no
Call Number Admin @ si @ OPC2015b Serial 2634
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Author Fernando Vilariño
Title 3D Scanning of Capitals at Library Living Lab Type Book Whole
Year 2019 Publication (down) “Living Lab Projects 2019”. ENoLL. Abbreviated Journal
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Address
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Notes MV; DAG; 600.140; 600.121;SIAI Approved no
Call Number Admin @ si @ Vil2019c Serial 3463
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Author David Masip; M. Bressan; Jordi Vitria
Title Classifier Combination Applied to Real Time Face Detection and Classification Type Miscellaneous
Year 2004 Publication (down) AVR2004 Abbreviated Journal
Volume Issue Pages
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Abstract
Address Barcelona
Corporate Author Thesis
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MBV2004a Serial 448
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Author Arnau Ramisa; Adriana Tapus; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras
Title Robust Vision-Based Localization using Combinations of Local Feature Regions Detectors Type Journal Article
Year 2009 Publication (down) 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 Isabelle Guyon; Imad Chaabane; Hugo Jair Escalante; Sergio Escalera; Damir Jajetic; James Robert Lloyd; Nuria Macia; Bisakha Ray; Lukasz Romaszko; Michele Sebag; Alexander Statnikov; Sebastien Treguer; Evelyne Viegas
Title A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention Type Conference Article
Year 2016 Publication (down) AutoML Workshop Abbreviated Journal
Volume Issue 1 Pages 1-8
Keywords AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning
Abstract The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully automatic, black-box learning machines for feature-based classification and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across different types of complexity. Over six rounds, participants succeeded in delivering AutoML software capable of being trained and tested without human intervention. Although improvements can still be made to close the gap between human-tweaked and AutoML models, this competition contributes to the development of fully automated environments by challenging practitioners to solve problems under specific constraints and sharing their approaches; the platform will remain available for post-challenge submissions at http://codalab.org/AutoML.
Address New York; USA; June 2016
Corporate Author Thesis
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 Expedition Conference ICML
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ GCE2016 Serial 2769
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Author Wenlong Deng; Yongli Mou; Takahiro Kashiwa; Sergio Escalera; Kohei Nagai; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger
Title Vision based Pixel-level Bridge Structural Damage Detection Using a Link ASPP Network Type Journal Article
Year 2020 Publication (down) Automation in Construction Abbreviated Journal AC
Volume 110 Issue Pages 102973
Keywords Semantic image segmentation; Deep learning
Abstract Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface structural damage detection, such as delamination and rebar exposure. It is well known that the quality of a deep learning model is highly dependent on the quality of the training dataset. Bridge damage detection, our application domain, has the following main challenges: (i) labeling the damages requires knowledgeable civil engineering professionals, which makes it difficult to collect a large annotated dataset; (ii) the damage area could be very small, whereas the background area is large, which creates an unbalanced training environment; (iii) due to the difficulty to exactly determine the extension of the damage, there is often a variation among different labelers who perform pixel-wise labeling. In this paper, we propose a novel model for bridge structural damage detection to address the first two challenges. This paper follows the idea of an atrous spatial pyramid pooling (ASPP) module that is designed as a novel network for bridge damage detection. Further, we introduce the weight balanced Intersection over Union (IoU) loss function to achieve accurate segmentation on a highly unbalanced small dataset. The experimental results show that (i) the IoU loss function improves the overall performance of damage detection, as compared to cross entropy loss or focal loss, and (ii) the proposed model has a better ability to detect a minority class than other light segmentation networks.
Address
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Notes HuPBA; no proj Approved no
Call Number Admin @ si @ DMK2020 Serial 3314
Permanent link to this record