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Author Francesco Ciompi; A. Palaioroutas; M. Loeve; Oriol Pujol; Petia Radeva; H. Tiddens; M. de Bruijne
Title Lung Tissue Classification in Severe Advanced Cystic Fibrosis from CT Scans Type Conference Article
Year 2011 Publication In MICCAI 2011 4th International Workshop on Pulmonary Image Analysis Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Toronto, Canada
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference PIA
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPL2011 Serial 1798
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Author Simone Balocco; Carlo Gatta; Xavier Carrillo; F. Mauri; Petia Radeva
Title Plaque Type, Plaque Burden and Wall Shear Stress Relation in Coronary Arteries Assessed by X-ray Angiography and Intravascular Ultrasound: a Qualitative Study Type Conference Article
Year 2011 Publication 14th International Symposium on Applied Sciences in Biomedical and Communication Technologies Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this paper, we present a complete framework that automatically provides fluid-dynamic and plaque analysis from IVUS and Angiographic sequences. Such framework is used to analyze, in three coronary arteries, the relation between wall shear stress with type and amount of plaque. Preliminary qualitative results show an inverse relation between the wall shear stress and the plaque burden, which is confirmed by the fact that the plaque growth is higher on the wall having concave curvature. Regarding the plaque type it was observed that regions having low shear stress are predominantly fibro-lipidic while the heavy calcifications are in general located in areas of the vessel having high WSS.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4503-0913-4 Medium
Area Expedition Conference ISABEL
Notes MILAB Approved no
Call Number Admin @ si @ BGC2011b Serial 1799
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Author Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol
Title Minimal Design of Error-Correcting Output Codes Type Journal Article
Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 33 Issue 6 Pages 693-702
Keywords Multi-class classification; Error-correcting output codes; Ensemble of classifiers
Abstract IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes MILAB; OR;HuPBA;MV Approved no
Call Number Admin @ si @ BEB2011a Serial 1800
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Author Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva
Title Traffic-Sign Recognition Systems Type Book Whole
Year 2011 Publication SpringerBriefs in Computer Science Abbreviated Journal
Volume Issue Pages 5-13
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Springer London Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4471-2244-9 Medium
Area Expedition Conference
Notes MILAB; OR;HuPBA;MV Approved no
Call Number Admin @ si @ EBP2011 Serial 1801
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Author Carlo Gatta; Eloi Puertas; Oriol Pujol
Title Multi-Scale Stacked Sequential Learning Type Journal Article
Year 2011 Publication Pattern Recognition Abbreviated Journal PR
Volume 44 Issue 10-11 Pages 2414-2416
Keywords Stacked sequential learning; Multiscale; Multiresolution; Contextual classification
Abstract One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ GPP2011 Serial 1802
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Author E. Serradell; Adriana Romero; R. Leta; Carlo Gatta; Francesc Moreno-Noguer
Title Simultaneous Correspondence and Non-Rigid 3D Reconstruction of the Coronary Tree from Single X-Ray Images Type Conference Article
Year 2011 Publication 13th IEEE International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 850-857
Keywords
Abstract
Address Barcelona
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICCV
Notes MILAB Approved no
Call Number Admin @ si @ SRL2011 Serial 1803
Permanent link to this record
 
 
Author Bogdan Raducanu; Fadi Dornaika
Title A Discriminative Non-Linear Manifold Learning Technique for Face Recognition Type Book Chapter
Year 2011 Publication Informatics Engineering and Information Science Abbreviated Journal
Volume 254 Issue 6 Pages 339-353
Keywords
Abstract In this paper we propose a novel non-linear discriminative analysis technique for manifold learning. The proposed approach is a discriminant version of Laplacian Eigenmaps which takes into account the class label information in order to guide the procedure of non-linear dimensionality reduction. By following the large margin concept, the graph Laplacian is split in two components: within-class graph and between-class graph to better characterize the discriminant property of the data.
Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques. The experimental results confirm that our method outperforms, in general, the existing ones. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variance in their appearance.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1865-0929 ISBN 978-3-642-25482-6 Medium
Area Expedition Conference ICIEIS
Notes OR;MV Approved no
Call Number Admin @ si @ RaD2011 Serial 1804
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Author Palaiahnakote Shivakumara; Anjan Dutta; Chew Lim Tan; Umapada Pal
Title Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing Type Journal Article
Year 2014 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 72 Issue 1 Pages 515-539
Keywords
Abstract In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well.
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1380-7501 ISBN Medium
Area Expedition Conference
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ SDT2014 Serial 2357
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Author Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal
Title Near Convex Region Adjacency Graph and Approximate Neighborhood String Matching for Symbol Spotting in Graphical Documents Type Conference Article
Year 2013 Publication 12th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1078-1082
Keywords
Abstract This paper deals with a subgraph matching problem in Region Adjacency Graph (RAG) applied to symbol spotting in graphical documents. RAG is a very important, efficient and natural way of representing graphical information with a graph but this is limited to cases where the information is well defined with perfectly delineated regions. What if the information we are interested in is not confined within well defined regions? This paper addresses this particular problem and solves it by defining near convex grouping of oriented line segments which results in near convex regions. Pure convexity imposes hard constraints and can not handle all the cases efficiently. Hence to solve this problem we have defined a new type of convexity of regions, which allows convex regions to have concavity to some extend. We call this kind of regions Near Convex Regions (NCRs). These NCRs are then used to create the Near Convex Region Adjacency Graph (NCRAG) and with this representation we have formulated the problem of symbol spotting in graphical documents as a subgraph matching problem. For subgraph matching we have used the Approximate Edit Distance Algorithm (AEDA) on the neighborhood string, which starts working after finding a key node in the input or target graph and iteratively identifies similar nodes of the query graph in the neighborhood of the key node. The experiments are performed on artificial, real and distorted datasets.
Address Washington; USA; August 2013
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1520-5363 ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.045; 600.056; 600.061; 601.152 Approved no
Call Number Admin @ si @ DLB2013a Serial 2358
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Author Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal
Title A Product graph based method for dual subgraph matching applied to symbol spotting Type Conference Article
Year 2013 Publication 10th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Product graph has been shown to be an efficient way for matching subgraphs. This paper reports the extension of the product graph methodology for subgraph matching applied to symbol spotting in graphical documents. This paper focuses on the two major limitations of the previous version of product graph: (1) Spurious nodes and edges in the graph representation and (2) Inefficient node and edge attributes. To deal with noisy information of vectorized graphical documents, we consider a dual graph representation on the original graph representing the graphical information and the product graph is computed between the dual graphs of the query graphs and the input graph.
The dual graph with redundant edges is helpful for efficient and tolerating encoding of the structural information of the graphical documents. The adjacency matrix of the product graph locates similar path information of two graphs and exponentiating the adjacency matrix finds similar paths of greater lengths. Nodes joining similar paths between two graphs are found by combining different exponentials of adjacency matrices. An experimental investigation reveals that the recall obtained by this approach is quite encouraging.
Address Bethlehem; PA; USA; August 2013
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference GREC
Notes DAG Approved no
Call Number Admin @ si @ DLB2013b Serial 2359
Permanent link to this record
 
 
Author Ivan Huerta; Ariel Amato; Xavier Roca; Jordi Gonzalez
Title Exploiting Multiple Cues in Motion Segmentation Based on Background Subtraction Type Journal Article
Year 2013 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume 100 Issue Pages 183–196
Keywords Motion segmentation; Shadow suppression; Colour segmentation; Edge segmentation; Ghost detection; Background subtraction
Abstract This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motionsegmentation. In our first contribution, a case analysis of motionsegmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motionsegmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ HAR2013 Serial 1808
Permanent link to this record
 
 
Author Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca
Title Human Action Recognition Using an Ensemble of Body-Part Detectors Type Journal Article
Year 2013 Publication Expert Systems Abbreviated Journal EXSY
Volume 30 Issue 2 Pages 101-114
Keywords Human action recognition;body-part detection;hidden Markov model
Abstract This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body-part detectors, followed by grouping of part detections, to perform human identification. Three example-based body-part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self-occlusions through the use of ten sub-classifiers that detect body parts over a specific range of viewpoints. Each sub-classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint-invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state-of-the-art methods.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ CBG2013 Serial 1809
Permanent link to this record
 
 
Author Nataliya Shapovalova; Carles Fernandez; Xavier Roca; Jordi Gonzalez
Title Semantics of Human Behavior in Image Sequences Type Book Chapter
Year 2011 Publication Computer Analysis of Human Behavior Abbreviated Journal
Volume Issue 7 Pages 151-182
Keywords
Abstract Human behavior is contextualized and understanding the scene of an action is crucial for giving proper semantics to behavior. In this chapter we present a novel approach for scene understanding. The emphasis of this work is on the particular case of Human Event Understanding. We introduce a new taxonomy to organize the different semantic levels of the Human Event Understanding framework proposed. Such a framework particularly contributes to the scene understanding domain by (i) extracting behavioral patterns from the integrative analysis of spatial, temporal, and contextual evidence and (ii) integrative analysis of bottom-up and top-down approaches in Human Event Understanding. We will explore how the information about interactions between humans and their environment influences the performance of activity recognition, and how this can be extrapolated to the temporal domain in order to extract higher inferences from human events observed in sequences of images.
Address
Corporate Author Thesis
Publisher Springer London Place of Publication Editor Albert Ali Salah;
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-85729-993-2 Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ SFR2011 Serial 1810
Permanent link to this record
 
 
Author Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez; Xavier Roca
Title A Selective Spatio-Temporal Interest Point Detector for Human Action Recognition in Complex Scenes Type Conference Article
Year 2011 Publication 13th IEEE International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 1776-1783
Keywords
Abstract Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper we present a new approach for STIP detection by applying surround suppression combined with local and temporal constraints. Our method is significantly different from existing STIP detectors and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-visual words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on existing benchmark datasets, and more challenging datasets of complex scenes, validate our approach and show state-of-the-art performance.
Address Barcelona
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1550-5499 ISBN 978-1-4577-1101-5 Medium
Area Expedition Conference ICCV
Notes ISE Approved no
Call Number Admin @ si @ CHM2011 Serial 1811
Permanent link to this record
 
 
Author Wenjuan Gong; Jürgen Brauer; Michael Arens; Jordi Gonzalez
Title Modeling vs. Learning Approaches for Monocular 3D Human Pose Estimation Type Conference Article
Year 2011 Publication 1st IEEE International Workshop on Performance Evaluation on Recognition of Human Actions and Pose Estimation Methods Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address London, United Kingdom
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference PERHAPS
Notes ISE Approved no
Call Number Admin @ si @ GBA2011 Serial 1812
Permanent link to this record