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Proceedings Paper

An edge detector based integrated database framework for security applications
Author(s): Sampathkumar Veeraraghavan; Karen Panetta; Sos Agaian
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Paper Abstract

In this paper, an integrated framework comprising of computer vision algorithms, Database system and Batch processing techniques has been developed to facilitate effective automatic threat recognition and detection for security applications. The proposed approach is used for automatic threat detection. The novel features of this structure include utilizing the Human Visual System model for segmentation, and a new ratio based edge detection algorithm that includes a new adaptive hysteresis thresholding method. The feature vectors of the baseline images are generated and stored in a relational database system using a batch window. The batch window is a special process where image processing tasks with similar needs are grouped together and effectively processed to save computing and memory requirements. The feature vectors of the segmented objects are generated using the CED method and are classified using a support vector machine (SVM) based classifier to identify threat objects. The experimental results demonstrate the presented framework efficiency in reducing the classification time and provide accurate detection.

Paper Details

Date Published: 28 April 2010
PDF: 12 pages
Proc. SPIE 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010, 77080X (28 April 2010); doi: 10.1117/12.850201
Show Author Affiliations
Sampathkumar Veeraraghavan, Tufts Univ. (United States)
Karen Panetta, Tufts Univ. (United States)
Sos Agaian, The Univ. of Texas at San Antonio (United States)

Published in SPIE Proceedings Vol. 7708:
Mobile Multimedia/Image Processing, Security, and Applications 2010
Sos S. Agaian; Sabah A. Jassim, Editor(s)

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