Home [101–110] << 111 112 113 114 115 116 117 118 119 120 >> [121–130]
List View
 | 
Citations
 | 
Details
   web
Records
Author Santiago Segui; Laura Igual; Jordi Vitria
Title Bagged One Class Classifiers in the Presence of Outliers Type Journal Article
Year 2013 Publication International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal IJPRAI
Volume 27 Issue 5 Pages 1350014-1350035
Keywords One-class Classifier; Ensemble Methods; Bagging and Outliers
Abstract The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers.
However, their application to one-class classification has been rather limited. In
this paper, we present a new ensemble method based on a non-parametric weighted bagging strategy for one-class classification, to improve accuracy in the presence of outliers. While the standard bagging strategy assumes a uniform data distribution, the method we propose here estimates a probability density based on a forest structure of the data. This assumption allows the estimation of data distribution from the computation of simple univariate and bivariate kernel densities. Experiments using original and noisy versions of 20 different datasets show that bagging ensemble methods applied to different one-class classifiers outperform base one-class classification methods. Moreover, we show that, in noisy versions of the datasets, the non-parametric weighted bagging strategy we propose outperforms the classical bagging strategy in a statistically significant way.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes OR; 600.046;MV Approved no
Call Number Admin @ si @ SIV2013 Serial 2256
Permanent link to this record
 
 
Author Miguel Angel Bautista; Sergio Escalera; Oriol Pujol
Title On the Design of an ECOC-Compliant Genetic Algorithm Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 2 Pages 865-884
Keywords
Abstract Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BEP2013 Serial 2254
Permanent link to this record
 
 
Author Vitaliy Konovalov; Albert Clapes; Sergio Escalera
Title Automatic Hand Detection in RGB-Depth Data Sequences Type Conference Article
Year 2013 Publication 16th Catalan Conference on Artificial Intelligence Abbreviated Journal
Volume Issue Pages 91-100
Keywords
Abstract Detecting hands in multi-modal RGB-Depth visual data has become a challenging Computer Vision problem with several applications of interest. This task involves dealing with changes in illumination, viewpoint variations, the articulated nature of the human body, the high flexibility of the wrist articulation, and the deformability of the hand itself. In this work, we propose an accurate and efficient automatic hand detection scheme to be applied in Human-Computer Interaction (HCI) applications in which the user is seated at the desk and, thus, only the upper body is visible. Our main hypothesis is that hand landmarks remain at a nearly constant geodesic distance from an automatically located anatomical reference point.
In a given frame, the human body is segmented first in the depth image. Then, a
graph representation of the body is built in which the geodesic paths are computed from the reference point. The dense optical flow vectors on the corresponding RGB image are used to reduce ambiguities of the geodesic paths’ connectivity, allowing to eliminate false edges interconnecting different body parts. Finally, we are able to detect the position of both hands based on invariant geodesic distances and optical flow within the body region, without involving costly learning procedures.
Address Vic; October 2013
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CCIA
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ KCE2013 Serial 2323
Permanent link to this record
 
 
Author Andreas Møgelmose; Chris Bahnsen; Thomas B. Moeslund; Albert Clapes; Sergio Escalera
Title Tri-modal Person Re-identification with RGB, Depth and Thermal Features Type Conference Article
Year 2013 Publication 9th IEEE Workshop on Perception beyond the visible Spectrum, Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 301-307
Keywords
Abstract Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work is mainly based on RGB data, but in this work we for the first time present a system where we combine RGB, depth, and thermal data for re-identification purposes. First, from each of the three modalities, we obtain some particular features: from RGB data, we model color information from different regions of the body, from depth data, we compute different soft body biometrics, and from thermal data, we extract local structural information. Then, the three information types are combined in a joined classifier. The tri-modal system is evaluated on a new RGB-D-T dataset, showing successful results in re-identification scenarios.
Address Portland; oregon; June 2013
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-7695-4990-3 Medium
Area Expedition Conference CVPRW
Notes HUPBA;MILAB Approved no
Call Number Admin @ si @ MBM2013 Serial 2253
Permanent link to this record
 
 
Author Miguel Reyes; Albert Clapes; Jose Ramirez; Juan R Revilla; Sergio Escalera
Title Automatic Digital Biometry Analysis based on Depth Maps Type Journal Article
Year 2013 Publication Computers in Industry Abbreviated Journal COMPUTIND
Volume 64 Issue 9 Pages 1316-1325
Keywords Multi-modal data fusion; Depth maps; Posture analysis; Anthropometric data; Musculo-skeletal disorders; Gesture analysis
Abstract World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ RCR2013 Serial 2252
Permanent link to this record
 
 
Author Eloi Puertas; Sergio Escalera; Oriol Pujol
Title Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification Type Journal Article
Year 2015 Publication Pattern Analysis and Applications Abbreviated Journal PAA
Volume 18 Issue 2 Pages 247-261
Keywords Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification
Abstract In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches.
Address
Corporate Author Thesis
Publisher Springer-Verlag Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1433-7541 ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ PEP2013 Serial 2251
Permanent link to this record
 
 
Author D.Sanchez; J.C.Ortega; Miguel Angel Bautista
Title Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization Type Conference Article
Year 2013 Publication 6th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 7887 Issue Pages 50-58
Keywords Human Body Segmentation; Error-Correcting Output Codes; Cascade of Classifiers; Graph Cuts
Abstract Human body segmentation is a hard task because of the high variability in appearance produced by changes in the point of view, lighting conditions, and number of articulations of the human body. In this paper, we propose a two-stage approach for the segmentation of the human body. In a first step, a set of human limbs are described, normalized to be rotation invariant, and trained using cascade of classifiers to be split in a tree structure way. Once the tree structure is trained, it is included in a ternary Error-Correcting Output Codes (ECOC) framework. This first classification step is applied in a windowing way on a new test image, defining a body-like probability map, which is used as an initialization of a GMM color modelling and binary Graph Cuts optimization procedure. The proposed methodology is tested in a novel limb-labelled data set. Results show performance improvements of the novel approach in comparison to classical cascade of classifiers and human detector-based Graph Cuts segmentation approaches.
Address Madeira; Portugal; June 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-38627-5 Medium
Area Expedition Conference IbPRIA
Notes HUPBA Approved no
Call Number SOB2013 Serial 2250
Permanent link to this record
 
 
Author Albert Clapes; Miguel Reyes; Sergio Escalera
Title Multi-modal User Identification and Object Recognition Surveillance System Type Journal Article
Year 2013 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 34 Issue 7 Pages 799-808
Keywords Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning
Abstract We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; 600.046; 605.203;MILAB Approved no
Call Number Admin @ si @ CRE2013 Serial 2248
Permanent link to this record
 
 
Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Logo recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers Type Conference Article
Year 2013 Publication 26th Canadian Conference on Artificial Intelligence Abbreviated Journal
Volume 7884 Issue Pages 1-12
Keywords Logo recognition; ensemble classification; Dempster-Shafer fusion; Zernike moments; generic Fourier descriptor; shape signature
Abstract Best paper award
The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology.
Address Canada; May 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-38456-1 Medium
Area Expedition Conference AI
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2013b Serial 2249
Permanent link to this record
 
 
Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding Type Journal Article
Year 2013 Publication Pattern Recognition Abbreviated Journal PR
Volume 46 Issue 10 Pages 2830-2839
Keywords Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification
Abstract Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-3203 ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2013a Serial 2247
Permanent link to this record
 
 
Author Fernando Barrera; Felipe Lumbreras; Angel Sappa
Title Multispectral Piecewise Planar Stereo using Manhattan-World Assumption Type Journal Article
Year 2013 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 34 Issue 1 Pages 52-61
Keywords Multispectral stereo rig; Dense disparity maps from multispectral stereo; Color and infrared images
Abstract This paper proposes a new framework for extracting dense disparity maps from a multispectral stereo rig. The system is constructed with an infrared and a color camera. It is intended to explore novel multispectral stereo matching approaches that will allow further extraction of semantic information. The proposed framework consists of three stages. Firstly, an initial sparse disparity map is generated by using a cost function based on feature matching in a multiresolution scheme. Then, by looking at the color image, a set of planar hypotheses is defined to describe the surfaces on the scene. Finally, the previous stages are combined by reformulating the disparity computation as a global minimization problem. The paper has two main contributions. The first contribution combines mutual information with a shape descriptor based on gradient in a multiresolution scheme. The second contribution, which is based on the Manhattan-world assumption, extracts a dense disparity representation using the graph cut algorithm. Experimental results in outdoor scenarios are provided showing the validity of the proposed framework.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS; 600.054; 600.055; 605.203 Approved no
Call Number Admin @ si @ BLS2013 Serial 2245
Permanent link to this record
 
 
Author Naveen Onkarappa; Angel Sappa
Title A Novel Space Variant Image Representation Type Journal Article
Year 2013 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV
Volume 47 Issue 1-2 Pages 48-59
Keywords Space-variant representation; Log-polar mapping; Onboard vision applications
Abstract Traditionally, in machine vision images are represented using cartesian coordinates with uniform sampling along the axes. On the contrary, biological vision systems represent images using polar coordinates with non-uniform sampling. For various advantages provided by space-variant representations many researchers are interested in space-variant computer vision. In this direction the current work proposes a novel and simple space variant representation of images. The proposed representation is compared with the classical log-polar mapping. The log-polar representation is motivated by biological vision having the characteristic of higher resolution at the fovea and reduced resolution at the periphery. On the contrary to the log-polar, the proposed new representation has higher resolution at the periphery and lower resolution at the fovea. Our proposal is proved to be a better representation in navigational scenarios such as driver assistance systems and robotics. The experimental results involve analysis of optical flow fields computed on both proposed and log-polar representations. Additionally, an egomotion estimation application is also shown as an illustrative example. The experimental analysis comprises results from synthetic as well as real sequences.
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0924-9907 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.055; 605.203; 601.215 Approved no
Call Number Admin @ si @ OnS2013a Serial 2243
Permanent link to this record
 
 
Author Naveen Onkarappa; Angel Sappa
Title Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario Type Conference Article
Year 2013 Publication 15th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal
Volume 8048 Issue Pages 483-490
Keywords Optical flow; regularization; Driver Assistance Systems; Performance Evaluation
Abstract Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).
Address York; UK; August 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-40245-6 Medium
Area Expedition Conference CAIP
Notes ADAS; 600.055; 601.215 Approved no
Call Number Admin @ si @ OnS2013b Serial 2244
Permanent link to this record
 
 
Author Olivier Penacchio; Xavier Otazu; Laura Dempere-Marco
Title A Neurodynamical Model of Brightness Induction in V1 Type Journal Article
Year 2013 Publication PloS ONE Abbreviated Journal Plos
Volume 8 Issue 5 Pages e64086
Keywords
Abstract Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Recent neurophysiological evidence suggests that brightness information might be explicitly represented in V1, in contrast to the more common assumption that the striate cortex is an area mostly responsive to sensory information. Here we investigate possible neural mechanisms that offer a plausible explanation for such phenomenon. To this end, a neurodynamical model which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual influences is presented. The proposed computational model successfully accounts for well known psychophysical effects for static contexts and also for brightness induction in dynamic contexts defined by modulating the luminance of surrounding areas. This work suggests that intra-cortical interactions in V1 could, at least partially, explain brightness induction effects and reveals how a common general architecture may account for several different fundamental processes, such as visual saliency and brightness induction, which emerge early in the visual processing pathway.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ POD2013 Serial 2242
Permanent link to this record
 
 
Author Naveen Onkarappa; Angel Sappa
Title Speed and Texture: An Empirical Study on Optical-Flow Accuracy in ADAS Scenarios Type Journal Article
Year 2014 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 15 Issue 1 Pages 136-147
Keywords
Abstract IF: 3.064
Increasing mobility in everyday life has led to the concern for the safety of automotives and human life. Computer vision has become a valuable tool for developing driver assistance applications that target such a concern. Many such vision-based assisting systems rely on motion estimation, where optical flow has shown its potential. A variational formulation of optical flow that achieves a dense flow field involves a data term and regularization terms. Depending on the image sequence, the regularization has to appropriately be weighted for better accuracy of the flow field. Because a vehicle can be driven in different kinds of environments, roads, and speeds, optical-flow estimation has to be accurately computed in all such scenarios. In this paper, we first present the polar representation of optical flow, which is quite suitable for driving scenarios due to the possibility that it offers to independently update regularization factors in different directional components. Then, we study the influence of vehicle speed and scene texture on optical-flow accuracy. Furthermore, we analyze the relationships of these specific characteristics on a driving scenario (vehicle speed and road texture) with the regularization weights in optical flow for better accuracy. As required by the work in this paper, we have generated several synthetic sequences along with ground-truth flow fields.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1524-9050 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ OnS2014a Serial 2386
Permanent link to this record