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Author Fadi Dornaika; Bogdan Raducanu
Title 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
Keywords
Abstract
Address
Corporate Author Thesis
Publisher NOVA Publishers Place of Publication Editor (down) S.E. Carter
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 Admin @ si @ DoR2012 Serial 2183
Permanent link to this record
 
 
Author Ernest Valveny; Philippe Dosch
Title Performance Evaluation of Symbol Recognition Type Book Chapter
Year 2004 Publication Document Analysis Systems Abbreviated Journal LNCS
Volume 3163 Issue Pages 354–365
Keywords
Abstract
Address Springer-Verlag
Corporate Author Thesis
Publisher Place of Publication Editor (down) S. Marinai, A. Dengel (Eds.),
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 3-540-23060-2 Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ VaD2004a Serial 502
Permanent link to this record
 
 
Author C. Santa-Marta; Jaume Garcia; A. Bajo; J.J. Vaquero; M. Ledesma-Carbayo; Debora Gil
Title Influence of the Temporal Resolution on the Quantification of Displacement Fields in Cardiac Magnetic Resonance Tagged Images Type Conference Article
Year 2008 Publication XXVI Congreso Anual de la Sociedad Española de Ingenieria Biomedica Abbreviated Journal
Volume Issue Pages 352–353
Keywords
Abstract It is difficult to acquire tagged cardiac MR images with a high temporal and spatial resolution using clinical MR scanners. However, if such images are used for quantifying scores based on motion, it is essential a resolution as high as possibl e. This paper explores the influence of the temporal resolution of a tagged series on the quantification of myocardial dynamic parameters. To such purpose we have designed a SPAMM (Spatial Modulation of Magnetization) sequence allowing acquisition of sequences at simple and double temporal resolution. Sequences are processed to compute myocardial motion by an automatic technique based on the tracking of the harmonic phase of tagged images (the Harmonic Phase Flow, HPF). The results have been compared to manual tracking of myocardial tags. The error in displacement fields for double resolution sequences reduces 17%.
Address Valladolid
Corporate Author Thesis
Publisher Place of Publication Editor (down) Roberto hornero, Saniel Abasolo
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CASEIB
Notes IAM; Approved no
Call Number IAM @ iam @ SGB2008 Serial 1033
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Author Marc Serra
Title Modeling, estimation and evaluation of intrinsic images considering color information Type Book Whole
Year 2015 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Image values are the result of a combination of visual information coming from multiple sources. Recovering information from the multiple factors thatproduced an image seems a hard and ill-posed problem. However, it is important to observe that humans develop the ability to interpret images and recognize and isolate specific physical properties of the scene.

Images describing a single physical characteristic of an scene are called intrinsic images. These images would benefit most computer vision tasks which are often affected by the multiple complex effects that are usually found in natural images (e.g. cast shadows, specularities, interreflections...).

In this thesis we analyze the problem of intrinsic image estimation from different perspectives, including the theoretical formulation of the problem, the visual cues that can be used to estimate the intrinsic components and the evaluation mechanisms of the problem.
Address September 2015
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor (down) Robert Benavente;Olivier Penacchio
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-943427-4-5 Medium
Area Expedition Conference
Notes CIC; 600.074 Approved no
Call Number Admin @ si @ Ser2015 Serial 2688
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Author Jaime Lopez-Krahe; Josep Llados; Enric Marti
Title Architectural Floor Plan Analysis Type Report
Year 2000 Publication CVonline Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Edimburg, UK
Corporate Author Thesis
Publisher University of Edinburgh Place of Publication Editor (down) Robert B. Fisher
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium online pdf
Area Expedition Conference
Notes DAG;IAM Approved no
Call Number IAM @ iam @ LLM2000 Serial 1561
Permanent link to this record
 
 
Author Monica Piñol
Title Reinforcement Learning of Visual Descriptors for Object Recognition Type Book Whole
Year 2014 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The human visual system is able to recognize the object in an image even if the object is partially occluded, from various points of view, in different colors, or with independence of the distance to the object. To do this, the eye obtains an image and extracts features that are sent to the brain, and then, in the brain the object is recognized. In computer vision, the object recognition branch tries to learns from the human visual system behaviour to achieve its goal. Hence, an algorithm is used to identify representative features of the scene (detection), then another algorithm is used to describe these points (descriptor) and finally the extracted information is used for classifying the object in the scene. The selection of this set of algorithms is a very complicated task and thus, a very active research field. In this thesis we are focused on the selection/learning of the best descriptor for a given image. In the state of the art there are several descriptors but we do not know how to choose the best descriptor because depends on scenes that we will use (dataset) and the algorithm chosen to do the classification. We propose a framework based on reinforcement learning and bag of features to choose the best descriptor according to the given image. The system can analyse the behaviour of different learning algorithms and descriptor sets. Furthermore the proposed framework for improving the classification/recognition ratio can be used with minor changes in other computer vision fields, such as video retrieval.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor (down) Ricardo Toledo;Angel Sappa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-940902-5-7 Medium
Area Expedition Conference
Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ Piñ2014 Serial 2464
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Author Eduard Vazquez
Title Unsupervised image segmentation based on material reflectance description and saliency Type Book Whole
Year 2011 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Image segmentations aims to partition an image into a set of non-overlapped regions, called segments. Despite the simplicity of the definition, image segmentation raises as a very complex problem in all its stages. The definition of segment is still unclear. When asking to a human to perform a segmentation, this person segments at different levels of abstraction. Some segments might be a single, well-defined texture whereas some others correspond with an object in the scene which might including multiple textures and colors. For this reason, segmentation is divided in bottom-up segmentation and top-down segmentation. Bottom up-segmentation is problem independent, that is, focused on general properties of the images such as textures or illumination. Top-down segmentation is a problem-dependent approach which looks for specific entities in the scene, such as known objects. This work is focused on bottom-up segmentation. Beginning from the analysis of the lacks of current methods, we propose an approach called RAD. Our approach overcomes the main shortcomings of those methods which use the physics of the light to perform the segmentation. RAD is a topological approach which describes a single-material reflectance. Afterwards, we cope with one of the main problems in image segmentation: non supervised adaptability to image content. To yield a non-supervised method, we use a model of saliency yet presented in this thesis. It computes the saliency of the chromatic transitions of an image by means of a statistical analysis of the images derivatives. This method of saliency is used to build our final approach of segmentation: spRAD. This method is a non-supervised segmentation approach. Our saliency approach has been validated with a psychophysical experiment as well as computationally, overcoming a state-of-the-art saliency method. spRAD also outperforms state-of-the-art segmentation techniques as results obtained with a widely-used segmentation dataset show
Address
Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor (down) Ramon Baldrich
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 Admin @ si @ Vaz2011b Serial 1835
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Author Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich
Title Perception Based Representations for Computational Colour Type Conference Article
Year 2011 Publication 3rd International Workshop on Computational Color Imaging Abbreviated Journal
Volume 6626 Issue Pages 16-30
Keywords colour perception, induction, naming, psychophysical data, saliency, segmentation
Abstract The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space.
Address Milan, Italy
Corporate Author Thesis
Publisher Springer-Verlag Place of Publication Editor (down) Raimondo Schettini, Shoji Tominaga, Alain Trémeau
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-20403-6 Medium
Area Expedition Conference CCIW
Notes CIC Approved no
Call Number Admin @ si @ VMB2011 Serial 1733
Permanent link to this record
 
 
Author Fadi Dornaika; Bogdan Raducanu
Title Facial Expression Recognition for HCI Applications Type Book Chapter
Year 2008 Publication Encyclopedia of Artificial Intelligence Abbreviated Journal
Volume II Issue Pages 625–631
Keywords
Abstract
Address
Corporate Author Thesis
Publisher IGI–Global Publisher Place of Publication Editor (down) Rabuñal
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 @ DoR2008c Serial 1034
Permanent link to this record
 
 
Author Bogdan Raducanu; Fadi Dornaika
Title Dynamic Vs. Static Recognition of Facial Expressions Type Book Chapter
Year 2008 Publication Ambient Intelligence. European Conference Abbreviated Journal
Volume 5355 Issue Pages 13–25
Keywords
Abstract
Address Nuremberg (Germany)
Corporate Author Thesis
Publisher Place of Publication Editor (down) Rabuñal
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference AMI
Notes OR; MV Approved no
Call Number BCNPCL @ bcnpcl @ RaD2008 Serial 1035
Permanent link to this record
 
 
Author Josep Llados; Horst Bunke; Enric Marti
Title Using cyclic string matching to find rotational and reflectional symmetric shapes Type Conference Article
Year 1996 Publication Dagstuhl Seminar on Modelling and Planning for Sensor–based Intelligent Robot Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher World Scientific Place of Publication Saarbrucken (Germany). Editor (down) R.C. Bolles, H.B.H.N.
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;IAM Approved no
Call Number IAM @ iam @ LBM1996 Serial 1564
Permanent link to this record
 
 
Author Olivier Penacchio
Title Mixed Hodge Structures and Equivariant Sheaves on the Projective Plane Type Journal Article
Year 2011 Publication Mathematische Nachrichten Abbreviated Journal MN
Volume 284 Issue 4 Pages 526-542
Keywords Mixed Hodge structures, equivariant sheaves, MSC (2010) Primary: 14C30, Secondary: 14F05, 14M25
Abstract We describe an equivalence of categories between the category of mixed Hodge structures and a category of equivariant vector bundles on a toric model of the complex projective plane which verify some semistability condition. We then apply this correspondence to define an invariant which generalizes the notion of R-split mixed Hodge structure and give calculations for the first group of cohomology of possibly non smooth or non-complete curves of genus 0 and 1. Finally, we describe some extension groups of mixed Hodge structures in terms of equivariant extensions of coherent sheaves. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Address
Corporate Author Thesis
Publisher WILEY-VCH Verlag Place of Publication Editor (down) R. Mennicken
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1522-2616 ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ Pen2011 Serial 1721
Permanent link to this record
 
 
Author Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Fernando Azpiroz; Petia Radeva
Title Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions Type Book Chapter
Year 2006 Publication 9th International Conference on Medical Image Computing and Computer–Assisted Intervention Abbreviated Journal
Volume 4191 Issue Pages 161–168
Keywords
Abstract Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of con- traction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions.
Address Copenhagen (Denmark)
Corporate Author Thesis
Publisher Springer Verlag Place of Publication Berlin Heidelberg Editor (down) R. Larsen, M. Nielsen, and J. Sporring
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area 800 Expedition Conference MICCAI06
Notes MV;OR;MILAB;SIAI Approved no
Call Number BCNPCL @ bcnpcl @ SVV2006; IAM @ iam @ SVV2006 Serial 725
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Author Eloi Puertas; Sergio Escalera; Oriol Pujol
Title Classifying Objects at Different Sizes with Multi-Scale Stacked Sequential Learning Type Conference Article
Year 2010 Publication 13th International Conference of the Catalan Association for Artificial Intelligence Abbreviated Journal
Volume 220 Issue Pages 193–200
Keywords
Abstract Sequential learning is that discipline of machine learning that deals with dependent data. In this paper, we use the Multi-scale Stacked Sequential Learning approach (MSSL) to solve the task of pixel-wise classification based on contextual information. The main contribution of this work is a shifting technique applied during the testing phase that makes possible, thanks to template images, to classify objects at different sizes. The results show that the proposed method robustly classifies such objects capturing their spatial relationships.
Address
Corporate Author Thesis
Publisher Place of Publication Editor (down) R. Alquezar, A. Moreno, J. Aguilar
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-60750-642-3 Medium
Area Expedition Conference CCIA
Notes HUPBA;MILAB Approved no
Call Number BCNPCL @ bcnpcl @ PEP2010 Serial 1448
Permanent link to this record
 
 
Author Francesco Ciompi
Title Multi-Class Learning for Vessel Characterization in Intravascular Ultrasound Type Book Whole
Year 2012 Publication PhD Thesis, Universitat de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this thesis we tackle the problem of automatic characterization of human coronary vessel in Intravascular Ultrasound (IVUS) image modality. The basis for the whole characterization process is machine learning applied to multi-class problems. In all the presented approaches, the Error-Correcting Output Codes (ECOC) framework is used as central element for the design of multi-class classifiers.
Two main topics are tackled in this thesis. First, the automatic detection of the vessel borders is presented. For this purpose, a novel context-aware classifier for multi-class classification of the vessel morphology is presented, namely ECOC-DRF. Based on ECOC-DRF, the lumen border and the media-adventitia border in IVUS are robustly detected by means of a novel holistic approach, achieving an error comparable with inter-observer variability and with state of the art methods.
The two vessel borders define the atheroma area of the vessel. In this area, tissue characterization is required. For this purpose, we present a framework for automatic plaque characterization by processing both texture in IVUS images and spectral information in raw Radio Frequency data. Furthermore, a novel method for fusing in-vivo and in-vitro IVUS data for plaque characterization is presented, namely pSFFS. The method demonstrates to effectively fuse data generating a classifier that improves the tissue characterization in both in-vitro and in-vivo datasets.
A novel method for automatic video summarization in IVUS sequences is also presented. The method aims to detect the key frames of the sequence, i.e., the frames representative of morphological changes. This novel method represents the basis for video summarization in IVUS as well as the markers for the partition of the vessel into morphological and clinically interesting events.
Finally, multi-class learning based on ECOC is applied to lung tissue characterization in Computed Tomography. The novel proposed approach, based on supervised and unsupervised learning, achieves accurate tissue classification on a large and heterogeneous dataset.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor (down) Petia Radeva;Oriol Pujol
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 Admin @ si @ Cio2012 Serial 2146
Permanent link to this record