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This paper proposes a simple and effective human action recognition algorithm based on projection and mass center movement features. Firstly, divide the original video into equal length subsequences with overlapping time window, and make moving parts detection using adjacent frame difference. Then, horizontal projection and vertical projection of binary image are made, and in order to get ride of...
An approach is proposed for abnormal sections detection in video sequences. In this approach, firstly the histogram is selected to describe the color change in the section, and then the histograms of the frames selected from the section compose the histogram matrix. In order to improve the process efficiency, the principal components analysis (PCA) is used to reduce dimensions of the histogram matrix...
Facial expression recognition (FER) from video is an essential research area in the field of human computer interfaces (HCI). In this work, we present a new method to recognize several facial expressions from time sequential facial expression images. To produce robust facial expression features, enhanced independent component analysis (EICA) is utilized to extract locally independent component (IC)...
We model the spatio-temporal variations of the shape of objects in a video sequence using a unique SVD-like decomposition. The decomposition is used to compute shape features, which form an approximation of the original shape sequence. The features are used to train separate classifiers using multi-class boosting strategy. We demonstrate the effectiveness of the proposed approach for shape recognition...
In this paper, we propose a new manifold representation for visual speech recognition. The developed system consists of three main steps: a) lip extraction from input video data, b) generate the expectation-maximization PCA (EMPCA) manifolds for the entire image sequence and perform manifold interpolation and re-sampling, c) classify the manifolds using a HMM classifier to identify the words described...
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