Automatic analysis of human facial expressions is one of the challenging problems in machine vision systems. It has many applications in human-computer interactions such as, social signal processing, social robots, deceit detection, interactive video and behavior monitoring. In this paper, we develop a new method for automatic facial expression recognition based on facial muscle anatomy and hum...

Recently, we have developed a nonparametric approach to texture analysis based on simple spatial operators like local binary patterns and signed gray level differences. Very good performance has been obtained in various texture classification and segmentation problems. This paper overviews our approach and presents examples to demonstrate its efficiency. Our results suggest that complementary f...

Within different techniques for texture modelling and recognition, local binary patterns and its variants have received much interest in recent years thanks to their low computational cost and high discrimination power. We propose a new texture description approach, whose principle is to extend the LBP representation from the local gray level to the regional distribution level. The region is re...

Dynamic texture is an extension of texture to the temporal domain. Recently, a powerful method for dynamic texture recognition based on volume local binary patterns (VLBP) was proposed. In this paper, we investigate improvements of the original VLBP operator. A proof on the relation of the two rotation invariant VLBP patterns is given. Methods for obtaining rotation invariance are experimentall...

1 Goal and Scope My project goal is to design a perspective texture synthesis algorithm. With the generated texture world, I hope to create an infinity roam effect similar to [HRRG08]. 2 Problems with traditional texture synthesis Traditional texture synthesis approaches create visually similar images by comparing local neighbors. They can preserve local statistical properties of the texels. Ho...

This paper presents a new approach of texture image classification based on nonsubsampled contourlet transform, Local binary patterns and Support vector machines. Nonsubsampled contourlet transform and Local binary patterns are used to extract texture features of images, Support vector machines are used to classify texture images. Nonsubsampled contourlet transform has translation invariability...

Local Parameter Histograms (LPH) based on Gaussian Markov random fields (GMRFs) have been successfully used in effective texture discrimination. LPH features represent the normalized histograms of locally estimated GMRF parameters via local linear regression. However, these features are not rotation invariant. In this paper two techniques to design rotation invariant LPH texture descriptors are...

The major aim of this paper consists of a comprehensive quantitative evaluation of adaptive texture descriptors when integrated into an unsupervised image segmentation framework. The techniques involved in this evaluation are: the standard and rotation invariant Local Binary Pattern (LBP) operators, multichannel texture decomposition based on Gabor filters and a recently proposed technique that...

We present a novel approach to multi-texture image segmentation based on the formation of an effective texture feature vector. Texture sub-features are derived from the output of an optimized Gabor filter. The filter’s parameters are selected by an immune genetic algorithm, which aims at maximizing the discrimination between the multi-textured regions. Next the texture features are integrated w...