Home << 1 2 3 4 5 6 7 8 9 10 >> [11–11]
List View
 | 
Citations
 | 
Details
   web
Records
Author Carles Sanchez
Title Tracheal ring detection in bronchoscopy Type Report
Year 2011 Publication CVC Technical Report Abbreviated Journal
Volume 168 Issue Pages
Keywords Bronchoscopy, tracheal ring, segmentation
Abstract (up) Endoscopy is the process in which a camera is introduced inside a human.
Given that endoscopy provides realistic images (in contrast to other modalities) and allows non-invase minimal intervention procedures (which can aid in diagnosis and surgical interventions), its use has spreaded during last decades.
In this project we will focus on bronchoscopic procedures, during which the camera is introduced through the trachea in order to have a diagnostic of the patient. The diagnostic interventions are focused on: degree of stenosis (reduction in tracheal area), prosthesis or early diagnosis of tumors. In the first case, assessment of the luminal area and the calculation of the diameters of the tracheal rings are required. A main limitation is that all the process is done by hand,
which means that the doctor takes all the measurements and decisions just by looking at the screen. As far as we know there is no computational framework for helping the doctors in the diagnosis.
This project will consist of analysing bronchoscopic videos in order to extract useful information for the diagnostic of the degree of stenosis. In particular we will focus on segmentation of the tracheal rings. As a result of this project several strategies (for detecting tracheal rings) had been implemented in order to compare their performance.
Address
Corporate Author Thesis Master's thesis
Publisher Place of Publication Editor Debora Gil, F.Javier Sanchez
Language english Summary Language english Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM;MV Approved no
Call Number IAM @ iam @ San2011 Serial 1841
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 6713 Issue Pages 227-236
Keywords
Abstract (up) 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
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 6713 Issue Pages 227-236
Keywords
Abstract (up) 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 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 6854 Issue Pages 520-528
Keywords Validation, Statistical Inference, Medical Imaging Algorithms.
Abstract (up) 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
Permanent link to this record
 
 
Author Arjan Gijsenij; Theo Gevers
Title Color Constancy Using Natural Image Statistics and Scene Semantics Type Journal Article
Year 2011 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 33 Issue 4 Pages 687-698
Keywords
Abstract (up) Existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of color constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing color constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in color constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific color constancy algorithm can be used instead of the classifier considering several algorithms.
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 0162-8828 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ GiG2011 Serial 1724
Permanent link to this record
 
 
Author Koen E.A. van de Sande; Jasper Uilings; Theo Gevers; Arnold Smeulders
Title Segmentation as Selective Search for Object Recognition Type Conference Article
Year 2011 Publication 13th IEEE International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 1879-1886
Keywords
Abstract (up) For object recognition, the current state-of-the-art is based on exhaustive search. However, to enable the use of more expensive features and classifiers and thereby progress beyond the state-of-the-art, a selective search strategy is needed. Therefore, we adapt segmentation as a selective search by reconsidering segmentation: We propose to generate many approximate locations over few and precise object delineations because (1) an object whose location is never generated can not be recognised and (2) appearance and immediate nearby context are most effective for object recognition. Our method is class-independent and is shown to cover 96.7% of all objects in the Pascal VOC 2007 test set using only 1,536 locations per image. Our selective search enables the use of the more expensive bag-of-words method which we use to substantially improve the state-of-the-art by up to 8.5% for 8 out of 20 classes on the Pascal VOC 2010 detection challenge.
Address Barcelona
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 1550-5499 ISBN 978-1-4577-1101-5 Medium
Area Expedition Conference ICCV
Notes ISE Approved no
Call Number Admin @ si @ SUG2011 Serial 1780
Permanent link to this record
 
 
Author Anjan Dutta; Josep Llados; Umapada Pal
Title Bag-of-GraphPaths Descriptors for Symbol Recognition and Spotting in Line Drawings Type Conference Article
Year 2011 Publication In proceedings of 9th IAPR Workshop on Graphic Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract (up) Graphical symbol recognition and spotting recently have become an important research activity. In this work we present a descriptor for symbols, especially for line drawings. The descriptor is based on the graph representation of graphical objects. We construct graphs from the vectorized information of the binarized images, where the critical points detected by the vectorization algorithm are considered as nodes and the lines joining them are considered as edges. Graph paths between two nodes in a graph are the finite sequences of nodes following the order from the starting to the final node. The occurrences of different graph paths in a given graph is an important feature, as they capture the geometrical and structural attributes of a graph. So the graph representing a symbol can efficiently be represent by the occurrences of its different paths. Their occurrences in a symbol can be obtained in terms of a histogram counting the number of some fixed prototype paths, we call the histogram as the Bag-of-GraphPaths (BOGP). These BOGP histograms are used as a descriptor to measure the distance among the symbols in vector space. We use the descriptor for three applications, they are: (1) classification of the graphical symbols, (2) spotting of the architectural symbols on floorplans, (3) classification of the historical handwritten words.
Address Seoul, Korea
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 978-3-642-36823-3 Medium
Area Expedition Conference GREC
Notes DAG Approved no
Call Number Admin @ si @ DLP2011c Serial 1825
Permanent link to this record
 
 
Author Victor Ponce; Mario Gorga; Xavier Baro; Sergio Escalera
Title Human Behavior Analysis from Video Data Using Bag-of-Gestures Type Conference Article
Year 2011 Publication 22nd International Joint Conference on Artificial Intelligence Abbreviated Journal
Volume 3 Issue Pages 2836-2837
Keywords
Abstract (up) Human Behavior Analysis in Uncontrolled Environments can be categorized in two main challenges: 1) Feature extraction and 2) Behavior analysis from a set of corporal language vocabulary. In this work, we present our achievements characterizing some simple behaviors from visual data on different real applications and discuss our plan for future work: low level vocabulary definition from bag-of-gesture units and high level modelling and inference of human behaviors.
Address Barcelona
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 978-1-57735-516-8 Medium
Area Expedition Conference IJCAI
Notes HuPBA;MV Approved no
Call Number Admin @ si @ PGB2011b Serial 1770
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 (up) 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 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 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 6669 Issue Pages 363-370
Keywords Pedestrian Detection; Color; Part Based Models
Abstract (up) 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 Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Xavier Roca
Title Efficient Discriminative Multiresolution Cascade for Real-Time Human Detection Applications Type Journal Article
Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 32 Issue 13 Pages 1581-1587
Keywords
Abstract (up) Human detection is fundamental in many machine vision applications, like video surveillance, driving assistance, action recognition and scene understanding. However in most of these applications real-time performance is necessary and this is not achieved yet by current detection methods.

This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a linear Support Vector Machine (SVM) composed of HOG features at different resolutions, from coarse at the first level to fine at the last one.

In contrast to previous methods, our approach uses a non-uniform stride of the sliding window that is defined by the feature resolution and allows the detection to be incrementally refined as going from coarse-to-fine resolution. In this way, the speed-up of the cascade is not only due to the fewer number of features computed at the first levels of the cascade, but also to the reduced number of windows that need to be evaluated at the coarse resolution. Experimental results show that our method reaches a detection rate comparable with the state-of-the-art of detectors based on HOG features, while at the same time the detection search is up to 23 times faster.
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 Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ PGB2011a Serial 1707
Permanent link to this record
 
 
Author Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo
Title Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes Type Journal Article
Year 2011 Publication Computer Graphics Forum Abbreviated Journal CGF
Volume 30 Issue 7 Pages 2107-2115
Keywords
Abstract (up) IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy.
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 Medium
Area Expedition Conference
Notes MILAB; HuPBA Approved no
Call Number Admin @ si @ EPA2011 Serial 1881
Permanent link to this record
 
 
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 (up) 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 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
Permanent link to this record
 
 
Author Sergio Escalera; David Masip; Eloi Puertas; Petia Radeva; Oriol Pujol
Title Online Error-Correcting Output Codes Type Journal Article
Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 32 Issue 3 Pages 458-467
Keywords
Abstract (up) IF JCR CCIA 1.303 2009 54/103
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication North Holland Editor
Language Summary Language Original Title
Series Editor Series Title 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 @ EMP2011 Serial 1714
Permanent link to this record
 
 
Author Maria Salamo; Sergio Escalera
Title Increasing Retrieval Quality in Conversational Recommenders Type Journal Article
Year 2011 Publication IEEE Transactions on Knowledge and Data Engineering Abbreviated Journal TKDE
Volume 99 Issue Pages 1-1
Keywords
Abstract (up) IF JCR CCIA 2.286 2009 24/103
JCR Impact Factor 2010: 1.851
A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user’s soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns Turing the recommendation process, we propose two novel exponential reinforcement learning approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a global weighting approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches
Address
Corporate Author Thesis
Publisher IEEE Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1041-4347 ISBN Medium
Area Expedition Conference
Notes MILAB; HuPBA Approved no
Call Number Admin @ si @ SaE2011 Serial 1713
Permanent link to this record