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This paper describes an integrated approach for segmenting foreground, namely a moving object, from a stationary background using image intensities and textural information. The proposed technique is based on the accumulative difference image (ADI) technique. Textural information is utilized with the unit gradient vectors (UGVs) of an image. Experimental results show that the UGV-based ADI technique...
We have investigated a technique for recognising faces invariant of facial expressions. We apply multi-linear tensor algebra, which subsumes linear algebra, to analyse and recognise 3D face surfaces. This potent framework possesses a remarkable ability to deal with the shortcomings of principle component analysis in less constrained situations. A set of vector spaces can be used to represent the variation...
Detection of moving objects is the first step in many applications using video sequences like video-surveillance, optical motion capture and multimedia application. The process mainly used is the background subtraction which one key step is the foreground detection. The goal is to classify pixels of the current image as foreground or background. Some critical situations as shadows, illumination variations...
Background subtraction is a widely used method for moving object detection in computer vision. It is usually applied in video surveillance systems. There are two major kinds of background subtraction approaches: pixel-based and block based. Yet there are three problems that can not be simultaneously solved by either method: the robustness to illumination changes, the effectiveness in suppressing shadows,...
In this paper we present a model based method for fast face orientation estimation and pose recovery from a monocular image sequence captured by an uncalibrated camera. To accomplish this method, CANDIDE-3 model is used to construct 3D individual face model manually. Four facial feature points (outer corners of eyes and mouth) are tracked by SIFT algorithm. Taking advantage of these feature points...
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