S. Sadek

Otto-von-Guericke-Universität Magdeburg, Magdeburg, Saxony-Anhalt, Germany

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Publications (4)0 Total impact

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    ABSTRACT: Despite their high stability and compactness, affine moment invariants have received a relatively little attention in action recognition literature. In this paper, we introduce an approach for activity recognition, based on affine moment invariants. In the proposed approach, a compact computationally-efficient shape descriptor is developed by using affine moment invariants. Affine moment invariants are derived from the 3D spatio-temporal action volume and the average image created from the 3D volume, and classified by an SVM classifier. On KTH dataset, the method achieves performance results that compare favorably with these of other contemporary approaches reported in literature.
    Pattern Recognition (ICPR), 2012 21st International Conference on; 01/2012
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    ABSTRACT: Despite their high stability and compactness, chord-length features have received little attention in activity recognition literature. In this paper, we present an SVM approach for activity recognition, based on chord-length shape features. The main contribution of the paper is two-fold. We first show how a compact computationally-efficient shape descriptor is constructed using 1-D chord-length functions. Secondly, we unfold how to use fuzzy membership functions to partition action snippets into a number of temporal states. When tested on KTH benchmark dataset, the approach achieves promising results that compare very favorably with those reported in the literature, while maintaining real-time performance.
    Image Processing (ICIP), 2012 19th IEEE International Conference on; 01/2012
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    ABSTRACT: Real-time feature extraction is a key component for any action recognition system that claims to be truly real-time. In this paper we present a conceptually simple and computationally efficient method for real-time human activity recognition based on simple statistical features. Such features are very cheap to compute and form a relatively low dimensional feature space in which classification can be carried out robustly. On the Weizmann dataset, the proposed method achieves encouraging recognition results with an average rate up to 97.8%. These results are in a good agreement with the literature. Further, the method achieves real-time performance, and thus can offer timing guarantees to real-time applications.
    Soft Computing and Pattern Recognition (SoCPaR), 2010 International Conference of; 01/2011
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    ABSTRACT: Image classification problem is one of the most challenges of computer vision. In this paper, a robust image classification approach using multilevel neural networks is proposed. In this approach, each image is fixedly divided into five regions each equal to half of the original image. Then these regions are classified by the multilevel neural classifier into five categories, i.e., ¿sky¿, ¿water¿, ¿grass¿, ¿soil¿ and ¿urban¿. Both color moments and multilevel wavelets decomposition technique are used to extract features from the regions. Such features have been experimentally proved to be computationally efficient and effective in representing image contents. Experimental results clarify that the proposed approach performs better than other state-of-the-art classification approaches.
    Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on; 12/2009