On the Performance of Kernel Methods for Skin Color Segmentation

Human skin detection in color images is a key preprocessing stage in many image processing applications. Though kernel-based methods have been recently pointed out as advantageous for this setting, there is still few evidence on their actual superiority. Specifically, binary Support Vector Classifier (two-class SVM) and one-class Novelty Detection (SVND) have been only tested in some example images or in limited databases. We hypothesize that comparative performance evaluation on a representative application-oriented database will allow us to determine whether proposed kernel methods exhibit significant better performance than conventional skin segmentation methods. Two image databases were acquired for a webcam-based face recognition application, under controlled and uncontrolled lighting and background conditions. Three different chromaticity spaces (YCbCr, CIEL∗a∗b∗, and normalized RGB) were used to compare kernel methods (two-class SVM, SVND) with conventional algorithms (Gaussian Mixture Models and Neural Networks). Our results show that two-class SVM outperforms conventional classifiers and also one-class SVM (SVND) detectors, specially for uncontrolled lighting conditions, with an acceptably low complexity

Aprendizaje basado en la interacción de usuarios para búsqueda y recuperación de imágenes

Se propone, al amparo del proyecto “Nuevos Algoritmos para la Gestión Eficiente de Contenidos Multimedia/nen Redes de Comunicaciones Móviles” (NAGEC), un nuevo mecanismo para la búsqueda y recuperación de/nimágenes basado en realimentación de relevancia. La arquitectura propuesta se compone de una red neuronal y/nun tesauro. La red neuronal extrae de las imágenes dos parámetros: textura y color. El tesauro recoge las/nrelaciones semánticas existentes entre los términos descriptores de las imágenes de la base de datos VisTex./nAmbos componentes se relacionan mediante un modelo de realimentación de relevancia que, a través de las/ninteracciones del usuario con el tesauro durante el proceso de búsqueda, permite a la red aprender relaciones/nsemánticas inherentes a las imágenes.Este artículo ha sido parcialmente financiado por el/nproyecto CICYT TIC 2002-03713

Assistive technology for relieving communication lumber between hearing/speech impaired and hearing people

This study proposes an automatic sign language translator, which is developed as assistive technology to help the hearing/speech impaired communities to communicate with the rest of the world. The system architecture, which includes feature extraction and recognition stages is described in detail. The signs are classified into two types: static and dynamic. Various types of sign features are presented and analysed. Recognition stage considers the hidden Markov model and segmentation signature. Real-time implementation of the system with the use of Windows7 and LINUX Fedora 16 operating systems with VMware workstation is presented in detail. The system has been successfully tested on Malaysian sign language