A Compact and Complete AFMT Invariant with Application to Face Recognition

In this paper, we present a complete set of hybrid similarity invariants under the Analytical Fourier-Mellin Transform (AFMT) framework, and apply it to invariant face recognition. Because the magnitude and phase spectra are not processed separately, this invariant descriptor is complete. In order to simplify the invariant feature data for recognition and discrimination, a 2D-PCA approach is introduced into this complete invariant descriptor. The experimental results indicate that the presented invariant descriptor is complete and similarityinvariant. Its compact representation through the 2D-PCA preserves the essential structure of an object. Furthermore, we apply this compact form into ORL, Yale and BioID face databases for experimental verification, and achieve the desired results

Application of textural descriptors for the evaluation of surface roughness class in the machining of metals

La medición de la rugosidad superficial ha sido una cuestión de especial interés en la investigación de mecanizado de metales durante los últimos cincuenta años. El acabado superficial se puede evaluar mediante algunos parámetros de rugosidad definidos en las normas internacionales. Estas normas están orientadas a dispositivos de medición táctiles que proporcionan registros bidimensionales del perfil de la pieza. Sin embargo, en la última década, la mejora de la visión computarizada y la óptica ha animado a muchos grupos a investigar en la aplicación de estas tecnologías. La evaluación de rugosidad de la superficie no es una excepción. La ventaja de la visión por ordenador en esta área es la caracterización de amplias áreas de superficie proporcionando más información (información 3D). En este contexto, este documento propone un método basado en la visión por ordenador para evaluar la calidad superficial delas piezas mecanizadas. El método consiste en el análisis de imágenes de acabado superficial de piezas mecanizadas mediante cinco vectores de características basados en momentos: Hu, Flusser, Taubin, Zernike y Legendre. Atendiendo a estos descriptores las imágenes se clasificaron en dos clases: baja rugosidad y alta rugosidad, utilizando el algoritmo del vecino k-nn y las redes neuronales. Los momentos utilizados como descriptores en este artículo muestran un comportamiento diferente con respecto a la identificación del acabado superficial, concluyendo que los descriptores Zernike y Legendre proporcionan el mejor rendimiento. Se logró una tasa de error del 6,5% utilizando descriptores Zernike con clasificación k-nn

Statistical region-based active contours for segmentation: an overview

International audienceIn this paper we propose a brief survey on geometric variational approaches and more precisely on statistical region-based active contours for medical image segmentation. In these approaches, image features are considered as random variables whose distribution may be either parametric, and belongs to the exponential family, or non-parametric estimated with a kernel density method. Statistical region-based terms are listed and reviewed showing that these terms can depict a wide spectrum of segmentation problems. A shape prior can also be incorporated to the previous statistical terms. A discussion of some optimization schemes available to solve the variational problem is also provided. Examples on real medical images are given to illustrate some of the given criteria

Human Face Recognition

Face recognition, as the main biometric used by human beings, has become more popular for the last twenty years. Automatic recognition of human faces has many commercial and security applications in identity validation and recognition and has become one of the hottest topics in the area of image processing and pattern recognition since 1990. Availability of feasible technologies as well as the increasing request for reliable security systems in today’s world has been a motivation for many researchers to develop new methods for face recognition. In automatic face recognition we desire to either identify or verify one or more persons in still or video images of a scene by means of a stored database of faces. One of the important features of face recognition is its non-intrusive and non-contact property that distinguishes it from other biometrics like iris or finger print recognition that require subjects’ participation. During the last two decades several face recognition algorithms and systems have been proposed and some major advances have been achieved. As a result, the performance of face recognition systems under controlled conditions has now reached a satisfactory level. These systems, however, face some challenges in environments with variations in illumination, pose, expression, etc. The objective of this research is designing a reliable automated face recognition system which is robust under varying conditions of noise level, illumination and occlusion. A new method for illumination invariant feature extraction based on the illumination-reflectance model is proposed which is computationally efficient and does not require any prior information about the face model or illumination. A weighted voting scheme is also proposed to enhance the performance under illumination variations and also cancel occlusions. The proposed method uses mutual information and entropy of the images to generate different weights for a group of ensemble classifiers based on the input image quality. The method yields outstanding results by reducing the effect of both illumination and occlusion variations in the input face images