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Author Joan Marti; Jose Miguel Benedi; Ana Maria Mendonça; Joan Serrat
Title Pattern Recognition and Image Analysis Type (down) Book Whole
Year 2007 Publication 3rd Iberian Conference Abbreviated Journal
Volume 6669 Issue Pages 4477-4478
Keywords
Abstract
Address Girona (Spain)
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 IbPRIA
Notes ADAS Approved no
Call Number ADAS @ adas @ MBM2007 Serial 994
Permanent link to this record
 
 
Author Jordi Gonzalez; Thomas B. Moeslund
Title Tracking Humans for the Evaluation of their Motion in Image Sequences Type (down) Book Whole
Year 2008 Publication First International Workshop, THEMIS Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Leeds (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 978-84-935251-9-4 Medium
Area Expedition Conference THEMIS
Notes Approved no
Call Number ISE @ ise @ GMW2008 Serial 1002
Permanent link to this record
 
 
Author Juan J. Villanueva
Title Visualization, Imaging, and Image Processing, Type (down) Book Whole
Year 2008 Publication Proceedings of the Eight IASTED International Conference Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Palma de Mallorca (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 978-0-88986-759-8 Medium
Area Expedition Conference
Notes Approved no
Call Number ISE @ ise @ Vil2008 Serial 1003
Permanent link to this record
 
 
Author Liu Wenyin; Josep Llados; Jean-Marc Ogier
Title Graphics Recognition. Recent Advances and New Opportunities. Type (down) Book Whole
Year 2008 Publication 7th International Workshop, Selected Papers, Abbreviated Journal
Volume 5046 Issue Pages
Keywords
Abstract
Address Curitiba (Brazil)
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 978-3-540-88184-1 Medium
Area Expedition Conference GREC
Notes DAG Approved no
Call Number DAG @ dag @ WLO2008 Serial 1012
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Author Aymen Azaza
Title Context, Motion and Semantic Information for Computational Saliency Type (down) Book Whole
Year 2018 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The main objective of this thesis is to highlight the salient object in an image or in a video sequence. We address three important—but in our opinion
insufficiently investigated—aspects of saliency detection. Firstly, we start
by extending previous research on saliency which explicitly models the information provided from the context. Then, we show the importance of
explicit context modelling for saliency estimation. Several important works
in saliency are based on the usage of object proposals. However, these methods
focus on the saliency of the object proposal itself and ignore the context.
To introduce context in such saliency approaches, we couple every object
proposal with its direct context. This allows us to evaluate the importance
of the immediate surround (context) for its saliency. We propose several
saliency features which are computed from the context proposals including
features based on omni-directional and horizontal context continuity. Secondly,
we investigate the usage of top-downmethods (high-level semantic
information) for the task of saliency prediction since most computational
methods are bottom-up or only include few semantic classes. We propose
to consider a wider group of object classes. These objects represent important
semantic information which we will exploit in our saliency prediction
approach. Thirdly, we develop a method to detect video saliency by computing
saliency from supervoxels and optical flow. In addition, we apply the
context features developed in this thesis for video saliency detection. The
method combines shape and motion features with our proposed context
features. To summarize, we prove that extending object proposals with their
direct context improves the task of saliency detection in both image and
video data. Also the importance of the semantic information in saliency
estimation is evaluated. Finally, we propose a newmotion feature to detect
saliency in video data. The three proposed novelties are evaluated on standard
saliency benchmark datasets and are shown to improve with respect to
state-of-the-art.
Address October 2018
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Joost Van de Weijer;Ali Douik
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-945373-9-4 Medium
Area Expedition Conference
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ Aza2018 Serial 3218
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Author Alfons Juan-Ciscar; Gemma Sanchez
Title PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008 Type (down) Book Whole
Year 2008 Publication 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 DAG Approved no
Call Number DAG @ dag @ JuS2008 Serial 1054
Permanent link to this record
 
 
Author Miquel Ferrer
Title Theory and Algorithms on the Median Graph. Application to Graph-based Classification and Clustering Type (down) Book Whole
Year 2008 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor Francesc Serratosa Casanelles;Ernest Valveny
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition 978-84-935251-7-0 Conference
Notes Approved no
Call Number Admin @ si @ Fer2008 Serial 1105
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Author Daniel Ponsa
Title Model-Based Visual Localisation of Contours and Vehicles Type (down) 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
Permanent link to this record
 
 
Author Robert Benavente
Title A Parametric Model for Computational Colour Naming Type (down) 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 Maria Vanrell
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number CAT @ cat @ Ben2007 Serial 1108
Permanent link to this record
 
 
Author Robert Benavente; Laura Igual; Fernando Vilariño
Title Current Challenges in Computer Vision Type (down) Book Whole
Year 2008 Publication Proccedings of the Third Internal Workshop 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 978-84-936529-0-6 Medium
Area Expedition Conference CVCRD
Notes MILAB;CIC;SIAI Approved no
Call Number BCNPCL @ bcnpcl @ BIV2008 Serial 1110
Permanent link to this record
 
 
Author Pau Baiget
Title Modeling Human Behavior for Image Sequence Understanding and Generation Type (down) Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The comprehension of animal behavior, especially human behavior, is one of the most ancient and studied problems since the beginning of civilization. The big list of factors that interact to determine a person action require the collaboration of different disciplines, such as psichology, biology, or sociology. In the last years the analysis of human behavior has received great attention also from the computer vision community, given the latest advances in the acquisition of human motion data from image sequences.

Despite the increasing availability of that data, there still exists a gap towards obtaining a conceptual representation of the obtained observations. Human behavior analysis is based on a qualitative interpretation of the results, and therefore the assignment of concepts to quantitative data is linked to a certain ambiguity.

This Thesis tackles the problem of obtaining a proper representation of human behavior in the contexts of computer vision and animation. On the one hand, a good behavior model should permit the recognition and explanation the observed activity in image sequences. On the other hand, such a model must allow the generation of new synthetic instances, which model the behavior of virtual agents.

First, we propose methods to automatically learn the models from observations. Given a set of quantitative results output by a vision system, a normal behavior model is learnt. This results provides a tool to determine the normality or abnormality of future observations. However, machine learning methods are unable to provide a richer description of the observations. We confront this problem by means of a new method that incorporates prior knowledge about the enviornment and about the expected behaviors. This framework, formed by the reasoning engine FMTL and the modeling tool SGT allows the generation of conceptual descriptions of activity in new image sequences. Finally, we demonstrate the suitability of the proposed framework to simulate behavior of virtual agents, which are introduced into real image sequences and interact with observed real agents, thereby easing the generation of augmented reality sequences.

The set of approaches presented in this Thesis has a growing set of potential applications. The analysis and description of behavior in image sequences has its principal application in the domain of smart video--surveillance, in order to detect suspicious or dangerous behaviors. Other applications include automatic sport commentaries, elderly monitoring, road traffic analysis, and the development of semantic video search engines. Alternatively, behavioral virtual agents allow to simulate accurate real situations, such as fires or crowds. Moreover, the inclusion of virtual agents into real image sequences has been widely deployed in the games and cinema industries.
Address Bellaterra (Spain)
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 Approved no
Call Number Admin @ si @ Bai2009 Serial 1210
Permanent link to this record
 
 
Author Dena Bazazian
Title Fully Convolutional Networks for Text Understanding in Scene Images Type (down) Book Whole
Year 2018 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Text understanding in scene images has gained plenty of attention in the computer vision community and it is an important task in many applications as text carries semantically rich information about scene content and context. For instance, reading text in a scene can be applied to autonomous driving, scene understanding or assisting visually impaired people. The general aim of scene text understanding is to localize and recognize text in scene images. Text regions are first localized in the original image by a trained detector model and afterwards fed into a recognition module. The tasks of localization and recognition are highly correlated since an inaccurate localization can affect the recognition task.
The main purpose of this thesis is to devise efficient methods for scene text understanding. We investigate how the latest results on deep learning can advance text understanding pipelines. Recently, Fully Convolutional Networks (FCNs) and derived methods have achieved a significant performance on semantic segmentation and pixel level classification tasks. Therefore, we took benefit of the strengths of FCN approaches in order to detect text in natural scenes. In this thesis we have focused on two challenging tasks of scene text understanding which are Text Detection and Word Spotting. For the task of text detection, we have proposed an efficient text proposal technique in scene images. We have considered the Text Proposals method as the baseline which is an approach to reduce the search space of possible text regions in an image. In order to improve the Text Proposals method we combined it with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same level of accuracy and thus gaining a significant speed up. Our experiments demonstrate that this text proposal approach yields significantly higher recall rates than the line based text localization techniques, while also producing better-quality localization. We have also applied this technique on compressed images such as videos from wearable egocentric cameras. For the task of word spotting, we have introduced a novel mid-level word representation method. We have proposed a technique to create and exploit an intermediate representation of images based on text attributes which roughly correspond to character probability maps. Our representation extends the concept of Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We call this representation the Soft-PHOC. Furthermore, we show how to use Soft-PHOC descriptors for word spotting tasks through an efficient text line proposal algorithm. To evaluate the detected text, we propose a novel line based evaluation along with the classic bounding box based approach. We test our method on incidental scene text images which comprises real-life scenarios such as urban scenes. The importance of incidental scene text images is due to the complexity of backgrounds, perspective, variety of script and language, short text and little linguistic context. All of these factors together makes the incidental scene text images challenging.
Address November 2018
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Dimosthenis Karatzas;Andrew Bagdanov
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-948531-1-1 Medium
Area Expedition Conference
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ Baz2018 Serial 3220
Permanent link to this record
 
 
Author David Rotger
Title Analysis and Multi-Modal Fusion of coronary Images Type (down) Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
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
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 @ Rot2009 Serial 1261
Permanent link to this record
 
 
Author Xavier Baro
Title Probabilistic Darwin Machines: A New Approach to Develop Evolutionary Object Detection Type (down) Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Ever since computers were invented, we have wondered whether they might perform some of the human quotidian tasks. One of the most studied and still nowadays less understood problem is the capacity to learn from our experiences and how we generalize the knowledge that we acquire. One of that unaware tasks for the persons and that more interest is awakening in different scientific areas since the beginning, is the one that is known as pattern recognition. The creation of models that represent the world that surrounds us, help us for recognizing objects in our environment, to predict situations, to identify behaviors... All this information allows us to adapt ourselves and to interact with our environment. The capacity of adaptation of individuals to their environment has been related to the amount of patterns that are capable of identifying.

This thesis faces the pattern recognition problem from a Computer Vision point of view, taking one of the most paradigmatic and extended approaches to object detection as starting point. After studying this approach, two weak points are identified: The first makes reference to the description of the objects, and the second is a limitation of the learning algorithm, which hampers the utilization of best descriptors.

In order to address the learning limitations, we introduce evolutionary computation techniques to the classical object detection approach.

After testing the classical evolutionary approaches, such as genetic algorithms, we develop a new learning algorithm based on Probabilistic Darwin Machines, which better adapts to the learning problem. Once the learning limitation is avoided, we introduce a new feature set, which maintains the benefits of the classical feature set, adding the ability to describe non localities. This combination of evolutionary learning algorithm and features is tested on different public data sets, outperforming the results obtained by the classical approach.
Address Barcelona (Spain)
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Vitria
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes OR;HuPBA;MV Approved no
Call Number BCNPCL @ bcnpcl @ Bar2009 Serial 1262
Permanent link to this record
 
 
Author Agata Lapedriza
Title Multitask Learning Techniques for Automatic Face Classification Type (down) Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Automatic face classification is currently a popular research area in Computer Vision. It involves several subproblems, such as subject recognition, gender classification or subject verification.

Current systems of automatic face classification need a large amount of training data to robustly learn a task. However, the collection of labeled data is usually a difficult issue. For this reason, the research on methods that are able to learn from a small sized training set is essential.

The dependency on the abundance of training data is not so evident in human learning processes. We are able to learn from a very small number of examples, given that we use, additionally, some prior knowledge to learn a new task. For example, we frequently find patterns and analogies from other domains to reuse them in new situations, or exploit training data from other experiences.

In computer science, Multitask Learning is a new Machine Learning approach that studies this idea of knowledge transfer among different tasks, to overcome the effects of the small sample sized problem.

This thesis explores, proposes and tests some Multitask Learning methods specially developed for face classification purposes. Moreover, it presents two more contributions dealing with the small sample sized problem, out of the Multitask Learning context. The first one is a method to extract external face features, to be used as an additional information source in automatic face classification problems. The second one is an empirical study on the most suitable face image resolution to perform automatic subject recognition.
Address Barcelona (Spain)
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Vitria;David Masip
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ Lap2009 Serial 1263
Permanent link to this record