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Author David Berga; Xavier Otazu
Title (down) Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1 Type Journal Article
Year 2020 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume 417 Issue Pages 270-289
Keywords
Abstract Previous studies suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return, oculomotor and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks. Results show that our model outpeforms other biologically inspired models of saliency prediction while predicting visual saccade sequences with the same model. We also show how temporal and spatial characteristics of saccade amplitude and inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts.
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 NEUROBIT Approved no
Call Number Admin @ si @ BeO2020c Serial 3444
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Author Daniel Ponsa
Title (down) Model-Based Visual Localisation of Contours and Vehicles Type Book Whole
Year 2007 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords Phd Thesis
Abstract
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Antonio Lopez;Xavier Roca
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-935251-3-2 Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ Pon2007 Serial 1107
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Author Oriol Pujol
Title (down) Model-based three dimensional interpolation of IVUS images Type Report
Year 1999 Publication CVC Technical Report #27 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address CVC (UAB)
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 BCNPCL @ bcnpcl @ Puj1999 Serial 49
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Author Jose Manuel Alvarez; Antonio Lopez
Title (down) Model-based road detection using shadowless features and on-line learning Type Miscellaneous
Year 2009 Publication BMVA one–day technical meeting on vision for automotive applications Abbreviated Journal
Volume Issue Pages
Keywords road detection
Abstract
Address London, UK
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 ADAS @ adas @ AlA2009 Serial 1272
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Author Bhaskar Chakraborty
Title (down) Model free approach to human action recognition Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Automatic understanding of human activity and action is very important and challenging research area of Computer Vision with wide applications in video surveillance, motion analysis, virtual reality interfaces, video indexing, content based video retrieval, HCI and health care. This thesis presents a series of techniques to solve the problem of human action recognition in video. First approach towards this goal is based on a probabilistic optimization model of body parts using Hidden Markov Model. This strong model based approach is able to distinguish between similar actions by only considering the body parts having major contributions to the actions. In next approach, we apply a weak model based human detector and actions are represented by Bag-of-key poses model to capture the human pose changes during the actions. To tackle the problem of human action recognition in complex scenes, a selective spatio-temporal interest point (STIP) detector is proposed by using a mechanism similar to that of the non-classical receptive field inhibition that is exhibited by most oriented selective neuron in the primary visual cortex. An extension of the selective STIP detector is applied to multi-view action recognition system by introducing a novel 4D STIPs (3D space + time). Finally, we use our STIP detector on large scale continuous visual event recognition problem and propose a novel generalized max-margin Hough transformation framework for activity detection
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Gonzalez;Xavier Roca
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 Admin @ si @ Cha2012 Serial 2207
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Author David Geronimo
Title (down) Model Features and Horizon Line Estimation for Pedestrian Detection in Advanced Driver Assistance Systems Type Report
Year 2006 Publication CVC Technical Report #98 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address CVC (UAB)
Corporate Author Thesis Master's 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 ADAS @ adas @ Ger2006 Serial 737
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Author Oriol Rodriguez-Leon.A.Carol;H.Tizon; Eduard Fernandez-Nofrerias; Josefina Mauri; Vicente del Valle; Debora Gil; Aura Hernandez-Sabate; Petia Radeva
Title (down) Model estadístic-determinístic per la segmentació de l adventicia en imatges d ecografía intracoronaria Type Journal Article
Year 2005 Publication Rev Societat Catalana Cardiologia Abbreviated Journal
Volume 5 Issue Pages 41
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 IAM;MILAB Approved no
Call Number IAM @ iam @ RCT2005 Serial 1637
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Author O. Rodriguez; J. Mauri; E Fernandez-Nofrerias; C. Garcia; R. Villuendas; A. Tovar; A. Duran; V. Valle; Misael Rosales; Petia Radeva
Title (down) Model Empiric de Simulacio d Ecografia Intravascular Type Journal Article
Year 2003 Publication Revista Societat Catalana de Cardiologia, 4(4):42, XIVe Congres de la Societat Catalana de Cardiologia Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona
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 Approved no
Call Number BCNPCL @ bcnpcl @ RMF2003e Serial 412
Permanent link to this record
 
 
Author Emanuel Indermühle; Volkmar Frinken; Horst Bunke
Title (down) Mode Detection in Online Handwritten Documents using BLSTM Neural Networks Type Conference Article
Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages 302-307
Keywords
Abstract Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data.
Address Bari, 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 978-1-4673-2262-1 Medium
Area Expedition Conference ICFHR
Notes DAG Approved no
Call Number Admin @ si @ IFB2012 Serial 2056
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Author Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Syeda Furruka Banu; Farhan Akram; Forhad U. H. Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig
Title (down) MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network Type Miscellaneous
Year 2019 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract CoRR abs/1907.00856
Skin lesion segmentation in dermoscopic images is a challenge due to their blurry and irregular boundaries. Most of the segmentation approaches based on deep learning are time and memory consuming due to the hundreds of millions of parameters. Consequently, it is difficult to apply them to real dermatoscope devices with limited GPU and memory resources. In this paper, we propose a lightweight and efficient Generative Adversarial Networks (GAN) model, called MobileGAN for skin lesion segmentation. More precisely, the MobileGAN combines 1D non-bottleneck factorization networks with position and channel attention modules in a GAN model. The proposed model is evaluated on the test dataset of the ISBI 2017 challenges and the validation dataset of ISIC 2018 challenges. Although the proposed network has only 2.35 millions of parameters, it is still comparable with the state-of-the-art. The experimental results show that our MobileGAN obtains comparable performance with an accuracy of 97.61%.
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; no menciona Approved no
Call Number Admin @ si @ MRA2019 Serial 3384
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Author Cristina Cañero; Nikolaos Thomos; George A. Triantafyllid; George C. Litos; Michael G. Strintzis
Title (down) Mobile Tele-echography: User Interface Design Type Journal
Year 2005 Publication IEEE Transactions on Information Technology in Biomedicine, 9(1):44–49 (IF: 1.376) 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 Admin @ si @ CTT2005 Serial 537
Permanent link to this record
 
 
Author Arnau Ramisa; Adriana Tapus; Ramon Lopez de Mantaras; Ricardo Toledo
Title (down) Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors Type Conference Article
Year 2008 Publication IEEE International Conference on Robotics and Automation, Abbreviated Journal
Volume Issue Pages 538–543
Keywords
Abstract
Address Pasadena; CA; USA
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 ICRA
Notes RV;ADAS Approved no
Call Number Admin @ si @ RTL2008 Serial 1144
Permanent link to this record
 
 
Author Daniel Ponsa; Jordi Vitria
Title (down) Mobile monitoring system using an agent-oriented approach Type Miscellaneous
Year 1999 Publication Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Bilbao
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;OR;MV Approved no
Call Number ADAS @ adas @ DaV1999 Serial 21
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Author Joan Codina-Filba; Sergio Escalera; Joan Escudero; Coen Antens; Pau Buch-Cardona; Mireia Farrus
Title (down) Mobile eHealth Platform for Home Monitoring of Bipolar Disorder Type Conference Article
Year 2021 Publication 27th ACM International Conference on Multimedia Modeling Abbreviated Journal
Volume 12573 Issue Pages 330-341
Keywords
Abstract People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur.

MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians.
Address
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 MMM
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ CEE2021 Serial 3659
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Author Hao Wu; Alejandro Ariza-Casabona; Bartłomiej Twardowski; Tri Kurniawan Wijaya
Title (down) MM-GEF: Multi-modal representation meet collaborative filtering Type Miscellaneous
Year 2023 Publication ARXIV Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representation during modelling user-item interactions, or exploring item-item relationships by analysing multi-modal features. Those methods, however, fail to incorporate the collaborative item-user-item relationships into the multi-modal feature-based item structure. In this work, we propose a graph-based item structure enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion, which effectively combines the latent item structure underlying multi-modal contents with the collaborative signals. Instead of processing the content feature in different modalities separately, we show that the early-fusion of multi-modal features provides significant improvement. MM-GEF learns refined item representations by injecting structural information obtained from both multi-modal and collaborative signals. Through extensive experiments on four publicly available datasets, we demonstrate systematical improvements of our method over state-of-the-art multi-modal recommendation methods.
Address
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
Notes LAMP Approved no
Call Number Admin @ si @ WAT2023 Serial 3988
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