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Author Miguel Oliveira; Angel Sappa; V. Santos
Title Color Correction using 3D Gaussian Mixture Models Type Conference Article
Year 2012 Publication 9th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume (down) 7324 Issue I Pages 97-106
Keywords
Abstract The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 10.1007/978-3-642-31295-3_12 Medium
Area Expedition Conference ICIAR
Notes ADAS Approved no
Call Number Admin @ si @ OSS2012a Serial 2015
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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Efficient pairwise classification using Local Cross Off strategy Type Conference Article
Year 2012 Publication 25th Canadian Conference on Artificial Intelligence Abbreviated Journal
Volume (down) 7310 Issue Pages 25-36
Keywords
Abstract The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.
Address Toronto, Ontario
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 0302-9743 ISBN 978-3-642-30352-4 Medium
Area Expedition Conference AI
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2012c Serial 2044
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Author Sergio Vera; Debora Gil; Agnes Borras; F. Javier Sanchez; Frederic Perez; Marius G. Linguraru; Miguel Angel Gonzalez Ballester
Title Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs Type Book Chapter
Year 2012 Publication Workshop on Computational and Clinical Applications in Abdominal Imaging Abbreviated Journal
Volume (down) 7029 Issue Pages 223–230
Keywords medial manifolds, abdomen.
Abstract Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D
objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial
manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our
method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs,
exploring the use of medial manifolds for the representation of multi-organ relations.
Address Toronto; Canada;
Corporate Author Thesis
Publisher Springer Link Place of Publication Berlin Editor H. Yoshida et al
Language English Summary Language English Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-28556-1 Medium
Area Expedition Conference ABDI
Notes IAM;MV Approved no
Call Number IAM @ iam @ VGB2012 Serial 1834
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Author Sergio Vera; Debora Gil; Agnes Borras; F. Javier Sanchez; Frederic Perez; Marius G. Linguraru
Title Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs Type Conference Article
Year 2011 Publication Workshop on Computational and Clinical Applications in Abdominal Imaging Abbreviated Journal
Volume (down) 7029 Issue Pages 223-230
Keywords
Abstract Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations.
Address Nice, France
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor In H. Yoshida et al
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ABDI
Notes IAM; MV Approved no
Call Number VGB2011 Serial 2036
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Author Francesco Ciompi; Oriol Pujol; Carlo Gatta; Xavier Carrillo; Josepa Mauri; Petia Radeva
Title A Holistic Approach for the Detection of Media-Adventitia Border in IVUS Type Conference Article
Year 2011 Publication 14th International Conference on Medical Image Computing and Computer Assisted Intervention Abbreviated Journal
Volume (down) 6893 Issue Pages 401-408
Keywords
Abstract In this paper we present a methodology for the automatic detection of media-adventitia border (MAb) in Intravascular Ultrasound. A robust computation of the MAb is achieved through a holistic approach where the position of the MAb with respect to other tissues of the vessel is used. A learned quality measure assures that the resulting MAb is optimal with respect to all other tissues. The mean distance error computed through a set of 140 images is 0.2164 (±0.1326) mm.
Address Toronto, Canada
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-23625-9 Medium
Area Expedition Conference MICCAI
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPG2011 Serial 1739
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Author Muhammad Anwer Rao; David Vazquez; Antonio Lopez
Title Color Contribution to Part-Based Person Detection in Different Types of Scenarios Type Conference Article
Year 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal
Volume (down) 6855 Issue II Pages 463-470
Keywords Pedestrian Detection; Color
Abstract Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution.
Address Seville, Spain
Corporate Author Thesis
Publisher Springer Place of Publication Berlin Heidelberg Editor P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch
Language English Summary Language english Original Title Color Contribution to Part-Based Person Detection in Different Types of Scenarios
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-23677-8 Medium
Area Expedition Conference CAIP
Notes ADAS Approved no
Call Number ADAS @ adas @ RVL2011b Serial 1665
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Author Naveen Onkarappa; Angel Sappa
Title Space Variant Representations for Mobile Platform Vision Applications Type Conference Article
Year 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal
Volume (down) 6855 Issue II Pages 146-154
Keywords
Abstract The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow.
Address Seville, Spain
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-23677-8 Medium
Area Expedition Conference CAIP
Notes ADAS Approved no
Call Number NaS2011; ADAS @ adas @ Serial 1686
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Author Aura Hernandez-Sabate; Debora Gil; David Roche; Monica M. S. Matsumoto; Sergio S. Furuie
Title Inferring the Performance of Medical Imaging Algorithms Type Conference Article
Year 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal
Volume (down) 6854 Issue Pages 520-528
Keywords Validation, Statistical Inference, Medical Imaging Algorithms.
Abstract Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.
Address Sevilla
Corporate Author Thesis
Publisher Springer-Verlag Berlin Heidelberg Place of Publication Berlin Editor Pedro Real; Daniel Diaz-Pernil; Helena Molina-Abril; Ainhoa Berciano; Walter Kropatsch
Language Summary Language Original Title
Series Editor Series Title L Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CAIP
Notes IAM; ADAS Approved no
Call Number IAM @ iam @ HGR2011 Serial 1676
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Author Jaume Gibert; Ernest Valveny; Oriol Ramos Terrades; Horst Bunke
Title Multiple Classifiers for Graph of Words Embedding Type Conference Article
Year 2011 Publication 10th International Conference on Multiple Classifier Systems Abbreviated Journal
Volume (down) 6713 Issue Pages 36-45
Keywords
Abstract During the last years, there has been an increasing interest in applying the multiple classifier framework to the domain of structural pattern recognition. Constructing base classifiers when the input patterns are graph based representations is not an easy problem. In this work, we make use of the graph embedding methodology in order to construct different feature vector representations for graphs. The graph of words embedding assigns a feature vector to every graph by counting unary and binary relations between node representatives and combining these pieces of information into a single vector. Selecting different node representatives leads to different vectorial representations and therefore to different base classifiers that can be combined. We experimentally show how this methodology significantly improves the classification of graphs with respect to single base classifiers.
Address Napoles, Italy
Corporate Author Thesis
Publisher Place of Publication Editor Carlo Sansone; Josef Kittler; Fabio Roli
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-21556-8 Medium
Area Expedition Conference MCS
Notes DAG Approved no
Call Number Admin @ si @GVR2011 Serial 1745
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Author Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Approximate Convex Hulls Family for One-Class Cassification Type Conference Article
Year 2011 Publication 10th International Workshop on Multiple Classifier Systems Abbreviated Journal
Volume (down) 6713 Issue Pages 106-115
Keywords
Abstract In this work, a new method for one-class classification based on the Convex Hull geometric structure is proposed. The new method creates a family of convex hulls able to fit the geometrical shape of the training points. The increased computational cost due to the creation of the convex hull in multiple dimensions is circumvented using random projections. This provides an approximation of the original structure with multiple bi-dimensional views. In the projection planes, a mechanism for noisy points rejection has also been elaborated and evaluated. Results show that the approach performs considerably well with respect to the state the art in one-class classification.
Address Napoli, Italy
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor Carlo Sansone; Josef Kittler; Fabio Roli
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-21556-8 Medium
Area Expedition Conference MCS
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPR2011b Serial 1761
Permanent link to this record
 

 
Author Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera
Title Introducing the Separability Matrix for Error Correcting Output Codes Coding Type Conference Article
Year 2011 Publication 10th International conference on Multiple Classifier Systems Abbreviated Journal
Volume (down) 6713 Issue Pages 227-236
Keywords
Abstract Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results.
Address Napoles, Italy
Corporate Author Thesis
Publisher Springer-Verlag Berlin Heidelberg Place of Publication Editor Carlo Sansone; Josef Kittler; Fabio Roli
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-21556-8 Medium
Area Expedition Conference MCS
Notes MILAB; OR;HuPBA;MV Approved no
Call Number Admin @ si @ BPB2011a Serial 1771
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Author Eloi Puertas; Sergio Escalera; Oriol Pujol
Title Multi-Class Multi-Scale Stacked Sequential Learning Type Conference Article
Year 2011 Publication 10th International Conference on Multiple Classifier Systems Abbreviated Journal
Volume (down) 6713 Issue Pages 197-206
Keywords
Abstract
Address Napoles, Italy
Corporate Author Thesis
Publisher Springer Place of Publication Editor Carlo Sansone; Josef Kittler; Fabio Roli
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference MCS
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ PEP2011b Serial 1772
Permanent link to this record
 

 
Author Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera
Title Introducing the Separability Matrix for Error Correcting Output Codes Coding Type Conference Article
Year 2011 Publication 10th International Conference on Multiple Classifier Systems Abbreviated Journal
Volume (down) 6713 Issue Pages 227-236
Keywords
Abstract Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results.
Address Napoles, Italy
Corporate Author Thesis
Publisher Springer-Verlag Berlin, Heidelberg Place of Publication Editor Carlo Sansone; Josef Kittler; Fabio Roli
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-21556-8 Medium
Area Expedition Conference MCS
Notes MILAB; OR;HuPBA;MV Approved no
Call Number Admin @ si @ BPB2011b Serial 1887
Permanent link to this record
 

 
Author Muhammad Anwer Rao; David Vazquez; Antonio Lopez
Title Opponent Colors for Human Detection Type Conference Article
Year 2011 Publication 5th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume (down) 6669 Issue Pages 363-370
Keywords Pedestrian Detection; Color; Part Based Models
Abstract Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper.
Address Las Palmas de Gran Canaria. Spain
Corporate Author Thesis
Publisher Springer Place of Publication Berlin Heidelberg Editor J. Vitria; J.M. Sanches; M. Hernandez
Language English Summary Language English Original Title Opponent Colors for Human Detection
Series Editor Series Title Lecture Notes on Computer Science Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-21256-7 Medium
Area Expedition Conference IbPRIA
Notes ADAS Approved no
Call Number ADAS @ adas @ RVL2011a Serial 1666
Permanent link to this record
 

 
Author Joan Marti; Jose Miguel Benedi; Ana Maria Mendonça; Joan Serrat
Title Pattern Recognition and Image Analysis Type Book Whole
Year 2007 Publication 3rd Iberian Conference Abbreviated Journal
Volume (down) 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