S. Escalera | University of Barcelona (original) (raw)

Papers by S. Escalera

Page 1. Administració de Sistemes Operatius(ASO) Iniciació a l'administració de sistemes... more Page 1. Administració de Sistemes Operatius(ASO) Iniciació a l'administració de sistemes Dept. Matemàtica Aplicada i Anàlisi, Universitat de Barcelona © Sergio Escalera, David Masip Page 2. 2 Administració de Sistemes Operatius(ASO) ...

Proceedings - International Conference on Pattern Recognition, 2010

This article proposes a general extension of the Error Correcting Output Codes (ECOC) framework t... more This article proposes a general extension of the Error Correcting Output Codes (ECOC) framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. Validation on UCI database and two real machine vision applications show that the online problem-dependent ECOC proposal provides a feasible and robust way for handling new classes using any base classifier.

2010 20th International Conference on Pattern Recognition, 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, 2010

Active Appearance Models to perform full face and pose recovery. Results over public data sets as... more Active Appearance Models to perform full face and pose recovery. Results over public data sets as well as proper human action base show a robust segmentation and recovery of both face and pose using the presented methodology.

Studies in Computational Intelligence, 2010

American Trypanosomiasis, or Chagas' disease is an infectious illness caused by the parasite Trip... more American Trypanosomiasis, or Chagas' disease is an infectious illness caused by the parasite Tripanosoma Cruzi. This disease is endemic in all Latin America, affecting millions of people in the continent. In order to diagnose and treat the chagas' disease, it is important to detect and measure the coronary damage of the patient. In this paper, we analyze and categorize patients into different groups based on the coronary damage produced by the disease. Based on the features of the heart cycle extracted using high resolution ECG, a multi-class scheme of Error-Correcting Output Codes (ECOC) is formulated and successfully applied. The results show that the proposed scheme obtains significant performance improvements compared to previous works and state-of-the-art ECOC designs.

Technologies for Machine Learning and Vision Applications, 2013

Different methodologies of uniform sampling over the rotation group, SO(3), for building unbiased... more Different methodologies of uniform sampling over the rotation group, SO(3), for building unbiased 2D shape models from 3D objects are introduced and reviewed in this chapter. State-of-the-art non uniform sampling approaches are discussed, and uniform sampling methods using Euler angles and quaternions are introduced. Moreover, since presented work is oriented to model building applications, it is not limited to general discrete methods to obtain uniform 3D rotations, but also from a continuous point of view in the case of Procrustes Analysis.

Pattern Recognition Letters, 2009

Many symbol recognition problems require the use of robust descriptors in order to obtain rich in... more Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance.

Pattern Recognition Letters, 2011

This article proposes a general extension of the error correcting output codes framework to the o... more 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 oneversus-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.

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2000

In this paper, we propose a circular blurred shape model descriptor to deal with the problem of s... more In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations.

... Sergio Escalera Xavier Baró Petia Radeva Departamento Matemàtica Aplicada i Anàlisi, Universi... more ... Sergio Escalera Xavier Baró Petia Radeva Departamento Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Gran Via de les Corts Catalanes 585, 08007 Barcelona Centro de ... Frontal: Porcentaje de capturas frontales (aque-llas en las que el sujeto mira al público/tribunal ...

maia.ub.es

Error-Correcting Output Codes (ECOC) is a gen-eral framework for combining binary classifica-tion... more Error-Correcting Output Codes (ECOC) is a gen-eral framework for combining binary classifica-tion in order to address the multi-class categoriza-tion problem. In this paper, we include contextual and semantic information in the decoding process of the ECOC framework, ...

It is proposed an exportable and robust system for automatic Robot Navigation in unknown environm... more It is proposed an exportable and robust system for automatic Robot Navigation in unknown environments. The system is composed by three main modules: the Artificial Vision, the Reinforcement Learning, and the reactive anti-collision module. The aim of the system is to allow a robot to automatically find a path that leads to a given goal, avoiding obstacles, only using vision and the least number of sensors. Keywords: Robot Vision, SURF, BoVW, Motion Field, Robot Navigation, Reinforcement Learning, Policy Gradient.

Advances in Computational Intelligence, 2011

An exportable and robust system using only camera images is proposed for path execution in robot ... more An exportable and robust system using only camera images is proposed for path execution in robot navigation. Motion information is extracted in the form of optical flow from SURF robust descriptors of consecutive frames, so the method is called SURF flow. This information is used to correct robot displacement when a straight forward path command is sent to the robot, but it is not really executed due to several robot and environmental concerns. The proposed system has been successfully tested on the legged robot Aibo.

IEEE Transactions on Information Technology in Biomedicine, 2000

Segmentation of coronary arteries in X-Ray angiography is a fundamental tool to evaluate arterial... more Segmentation of coronary arteries in X-Ray angiography is a fundamental tool to evaluate arterial diseases and choose proper coronary treatment. The accurate segmentation of coronary arteries has become an important topic for the registration of different modalities which allows physicians rapid access to different medical imaging information from Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI). In this paper, we propose an accurate fully automatic algorithm based on Graph-cuts for vessel centerline extraction, caliber estimation, and catheter detection. Vesselness, geodesic paths, and a new multi-scale edgeness map are combined to customize the Graph-cuts approach to the segmentation of tubular structures, by means of a global optimization of the Graph-cuts energy function. Moreover, a novel supervised learning methodology that integrates local and contextual information is proposed for automatic catheter detection.