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Author Jordi Gonzalez; Javier Varona; Xavier Roca; Juan J. Villanueva
Title (down) Analysis of Human Walking Based on aSpaces Type Miscellaneous
Year 2004 Publication Articulated Motion and Deformable Objects, Third International Workshop, (AMDO 2004), Lecture Notes in Computer Science, F.J. Perales, B.A. Draper (Eds.), 3179:177–188 Abbreviated Journal
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Abstract
Address Springer-Verlag, Berlin, Heidelberg
Corporate Author Thesis
Publisher Place of Publication Editor
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ISSN ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number ISE @ ise @ GVR2004a Serial 492
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Author Onur Ferhat
Title (down) Analysis of Head-Pose Invariant, Natural Light Gaze Estimation Methods Type Book Whole
Year 2017 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Eye tracker devices have traditionally been only used inside laboratories, requiring trained professionals and elaborate setup mechanisms. However, in the recent years the scientific work on easier–to–use eye trackers which require no special hardware—other than the omnipresent front facing cameras in computers, tablets, and mobiles—is aiming at making this technology common–place. These types of trackers have several extra challenges that make the problem harder, such as low resolution images provided by a regular webcam, the changing ambient lighting conditions, personal appearance differences, changes in head pose, and so on. Recent research in the field has focused on all these challenges in order to provide better gaze estimation performances in a real world setup.

In this work, we aim at tackling the gaze tracking problem in a single camera setup. We first analyze all the previous work in the field, identifying the strengths and weaknesses of each tried idea. We start our work on the gaze tracker with an appearance–based gaze estimation method, which is the simplest idea that creates a direct mapping between a rectangular image patch extracted around the eye in a camera image, and the gaze point (or gaze direction). Here, we do an extensive analysis of the factors that affect the performance of this tracker in several experimental setups, in order to address these problems in future works. In the second part of our work, we propose a feature–based gaze estimation method, which encodes the eye region image into a compact representation. We argue that this type of representation is better suited to dealing with head pose and lighting condition changes, as it both reduces the dimensionality of the input (i.e. eye image) and breaks the direct connection between image pixel intensities and the gaze estimation. Lastly, we use a face alignment algorithm to have robust face pose estimation, using a 3D model customized to the subject using the tracker. We combine this with a convolutional neural network trained on a large dataset of images to build a face pose invariant gaze tracker.
Address September 2017
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Fernando Vilariño
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-945373-5-6 Medium
Area Expedition Conference
Notes MV Approved no
Call Number Admin @ si @ Fer2017 Serial 3018
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Author A. Dupuy; Joan Serrat; Jordi Vitria; J. Pladellorens
Title (down) Analysis of gammagraphic images by mathematical morphology. Type Conference Article
Year 1991 Publication Pattern Recognition and image Analysis: IV Spanish Symposium of Pattern Recognition and image Analysis, World Scientific Pub. Abbreviated Journal
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Abstract
Address
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Notes ADAS;OR;MV Approved no
Call Number ADAS @ adas @ DSV1991 Serial 262
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Author Albert Berenguel
Title (down) Analysis of background textures in banknotes and identity documents for counterfeit detection Type Book Whole
Year 2019 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Counterfeiting and piracy are a form of theft that has been steadily growing in recent years. A counterfeit is an unauthorized reproduction of an authentic/genuine object. Banknotes and identity documents are two common objects of counterfeiting. The former is used by organized criminal groups to finance a variety of illegal activities or even to destabilize entire countries due the inflation effect. Generally, in order to run their illicit businesses, counterfeiters establish companies and bank accounts using fraudulent identity documents. The illegal activities generated by counterfeit banknotes and identity documents has a damaging effect on business, the economy and the general population. To fight against counterfeiters, governments and authorities around the globe cooperate and develop security features to protect their security documents. Many of the security features in identity documents can also be found in banknotes. In this dissertation we focus our efforts in detecting the counterfeit banknotes and identity documents by analyzing the security features at the background printing. Background areas on secure documents contain fine-line patterns and designs that are difficult to reproduce without the manufacturers cutting-edge printing equipment. Our objective is to find the loose of resolution between the genuine security document and the printed counterfeit version with a publicly available commercial printer. We first present the most complete survey to date in identity and banknote security features. The compared algorithms and systems are based on computer vision and machine learning. Then we advance to present the banknote and identity counterfeit dataset we have built and use along all this thesis. Afterwards, we evaluate and adapt algorithms in the literature for the security background texture analysis. We study this problem from the point of view of robustness, computational efficiency and applicability into a real and non-controlled industrial scenario, proposing key insights to use these algorithms. Next, within the industrial environment of this thesis, we build a complete service oriented architecture to detect counterfeit documents. The mobile application and the server framework intends to be used even by non-expert document examiners to spot counterfeits. Later, we re-frame the problem of background texture counterfeit detection as a full-reference game of spotting the differences, by alternating glimpses between a counterfeit and a genuine background using recurrent neural networks. Finally, we deal with the lack of counterfeit samples, studying different approaches based on anomaly detection.
Address November 2019
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Oriol Ramos Terrades;Josep Llados
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-121011-2-6 Medium
Area Expedition Conference
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ Ber2019 Serial 3395
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Author Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund
Title (down) Analysis and Retrieval of Tracked Events and Motion in Imagery Streams Type Miscellaneous
Year 2013 Publication ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream Abbreviated Journal
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Address Barcelona; October 2013
Corporate Author Thesis
Publisher Place of Publication Editor
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Notes ISE Approved no
Call Number Admin @ si @ DDB2013 Serial 2372
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Author Alicia Fornes; Gemma Sanchez
Title (down) Analysis and Recognition of Music Scores Type Book Chapter
Year 2014 Publication Handbook of Document Image Processing and Recognition Abbreviated Journal
Volume E Issue Pages 749-774
Keywords
Abstract The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented.
Address
Corporate Author Thesis
Publisher Springer London Place of Publication Editor D. Doermann; K. Tombre
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-85729-860-7 Medium
Area Expedition Conference
Notes DAG; ADAS; 600.076; 600.077 Approved no
Call Number Admin @ si @ FoS2014 Serial 2484
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Author Fadi Dornaika; Bogdan Raducanu
Title (down) Analysis and Recognition of Facial Expressions in Videos Using Facial Shape Deformation Type Book Chapter
Year 2012 Publication Facial Expressions: Dynamic Patterns, Impairments and Social Perceptions Abbreviated Journal
Volume Issue Pages 157-178
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Address
Corporate Author Thesis
Publisher NOVA Publishers Place of Publication Editor S.E. Carter
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes OR;MV Approved no
Call Number Admin @ si @ DoR2012 Serial 2183
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Author David Rotger
Title (down) Analysis and Multi-Modal Fusion of coronary Images Type Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
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Abstract The framework of this thesis is to study in detail different techniques and tools for medical image registration in order to ease the daily life of clinical experts in cardiology. The first aim of this thesis is providing computer tools for
fusing IVUS and angiogram data is of high clinical interest to help the physicians locate in IVUS data and decide which lesion is observed, how long it is, how far from a bifurcation or another lesions stays, etc. This thesis proves and
validates that we can segment the catheter path in angiographies using geodesic snakes (based on fast marching algorithm), a three-dimensional reconstruction of the catheter inspired in stereo vision and a new technique to fuse IVUS
and angiograms that establishes exact correspondences between them. We have developed a new workstation called iFusion that has four strong advantages: registration of IVUS and angiographic images with sub-pixel precision, it works on- and off-line, it is independent on the X-ray system and there is no need of daily calibration. The second aim of the thesis is devoted to developing a computer-aided analysis of IVUS for image-guided intervention. We have designed, implemented
and validated a robust algorithm for stent extraction and reconstruction from IVUS videos. We consider a very special and recent kind of stents, bioabsorbable stents that represent a great clinical challenge due to their property to be
absorbed by time and thus avoiding the “danger” of neostenosis as one of the main problems of metallic stents. We present a new and very promising algorithm based on an optimized cascade of multiple classifiers to automatically detect individual stent struts of a very novel bioabsorbable drug eluting coronary stent. This problem represents a very challenging target given the variability in contrast, shape and grey levels of the regions to be detected, what is
denoted by the high variability between the specialists (inter-observer variability of 0.14~$\pm$0.12). The obtained results of the automatic strut detection are within the inter-observer variability.
Address Barcelona (Espanya)
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Petia Radeva
Language Summary Language Original Title
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Notes Approved no
Call Number Admin @ si @ Rot2009 Serial 1261
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Author A. Pagani
Title (down) Analisis y comparacion de metodos para la caracterizacion de la respuesta espectral de camaras Type Report
Year 2002 Publication CVC Technical Report # 64 Abbreviated Journal
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Address CVC (UAB)
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Publisher Place of Publication Editor
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Notes Approved no
Call Number Admin @ si @ Pag2002 Serial 330
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Author Oriol Rodriguez-Leor; Debora Gil; Eduard Fernandez-Nofrerias
Title (down) Analisis en los cambios en el nivel de gris en las secuencias angiograficas mediante descriptores estadisticos para determinar la perfusion miocardica Type Journal Article
Year 2006 Publication Revista Española de Cardiología Abbreviated Journal REC
Volume 59 Supl 2-166 Issue 2 Pages 128
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Abstract
Address
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Publisher Place of Publication Editor
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Notes IAM; Approved no
Call Number IAM @ iam @ RGF2006 Serial 1640
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Author O. Rodriguez; J. Mauri; E Fernandez-Nofrerias; A. Tovar; R. Villuendas; V. Valle; Oriol Pujol; Petia Radeva
Title (down) Analisis de texturas mediante la modificacion de un modelo binario local para la segmentacion automatica de secuencias de ecografia intracoronaria Type Journal
Year 2003 Publication Revista Española de Cardiologia (IF: 0.959), 56(2), Congreso de las Enfermedades Cardiovasculares Abbreviated Journal
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Address Sevilla (Spain)
Corporate Author Thesis
Publisher Place of Publication Editor
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Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ RMF2003f Serial 413
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Author Victor Ponce; Mario Gorga; Xavier Baro; Petia Radeva; Sergio Escalera
Title (down) Analisis de la Expresion Oral y Gestual en Proyectos Fin de Carrera Via un Sistema de Vision Artificial Type Miscellaneous
Year 2011 Publication Revista electronica de la asociacion de enseñantes universitarios de la informatica AENUI Abbreviated Journal ReVision
Volume 4 Issue 1 Pages 8-18
Keywords
Abstract La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación.
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 1989-1199 ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA;MV Approved no
Call Number Admin @ si @ PGB2011c Serial 1783
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Author Qingshan Chen; Zhenzhen Quan; Yujun Li; Chao Zhai; Mikhail Mozerov
Title (down) An Unsupervised Domain Adaption Approach for Cross-Modality RGB-Infrared Person Re-Identification Type Journal Article
Year 2023 Publication IEEE Sensors Journal Abbreviated Journal IEEE-SENS
Volume 23 Issue 24 Pages
Keywords Q. Chen, Z. Quan, Y. Li, C. Zhai and M. G. Mozerov
Abstract Dual-camera systems commonly employed in surveillance serve as the foundation for RGB-infrared (IR) cross-modality person re-identification (ReID). However, significant modality differences give rise to inferior performance compared to single-modality scenarios. Furthermore, most existing studies in this area rely on supervised training with meticulously labeled datasets. Labeling RGB-IR image pairs is more complex than labeling conventional image data, and deploying pretrained models on unlabeled datasets can lead to catastrophic performance degradation. In contrast to previous solutions that focus solely on cross-modality or domain adaptation issues, this article presents an end-to-end unsupervised domain adaptation (UDA) framework for the cross-modality person ReID, which can simultaneously address both of these challenges. This model employs source domain classes, target domain clusters, and unclustered instance samples for the training, maximizing the comprehensive use of the dataset. Moreover, it addresses the problem of mismatched clustering labels between the two modalities in the target domain by incorporating a label matching module that reassigns reliable clusters with labels, ensuring correspondence between different modality labels. We construct the loss function by incorporating distinctiveness loss and multiplicity loss, both of which are determined by the similarity of neighboring features in the predicted feature space and the difference between distant features. This approach enables efficient feature clustering and cluster class assignment to occur concurrently. Eight UDA cross-modality person ReID experiments are conducted on three real datasets and six synthetic datasets. The experimental results unequivocally demonstrate that the proposed model outperforms the existing state-of-the-art algorithms to a significant degree. Notably, in RegDB → RegDB_light, the Rank-1 accuracy exhibits a remarkable improvement of 8.24%.
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Notes LAMP Approved no
Call Number Admin @ si @ CQL2023 Serial 3884
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Author Salvatore Tabbone; Oriol Ramos Terrades
Title (down) An Overview of Symbol Recognition Type Book Chapter
Year 2014 Publication Handbook of Document Image Processing and Recognition Abbreviated Journal
Volume D Issue Pages 523-551
Keywords Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting
Abstract According to the Cambridge Dictionaries Online, a symbol is a sign, shape, or object that is used to represent something else. Symbol recognition is a subfield of general pattern recognition problems that focuses on identifying, detecting, and recognizing symbols in technical drawings, maps, or miscellaneous documents such as logos and musical scores. This chapter aims at providing the reader an overview of the different existing ways of describing and recognizing symbols and how the field has evolved to attain a certain degree of maturity.
Address
Corporate Author Thesis
Publisher Springer London Place of Publication Editor D. Doermann; K. Tombre
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-85729-858-4 Medium
Area Expedition Conference
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ TaT2014 Serial 2489
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Author Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate
Title (down) An overview of incremental feature extraction methods based on linear subspaces Type Journal Article
Year 2018 Publication Knowledge-Based Systems Abbreviated Journal KBS
Volume 145 Issue Pages 219-235
Keywords
Abstract With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind.
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 0950-7051 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ DFH2018 Serial 3090
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