Dedicated hardware for real-time computation of second-order statistical features for high resolution images

Authors:
Dimitris Bariamis;Dimitris K. Iakovidis;Dimitris Maroulis
Affiliations:
Dept. of Informatics and Telecommunications, University of Athens, Panepistimiopolis, Illisia, Athens, Greece;Dept. of Informatics and Telecommunications, University of Athens, Panepistimiopolis, Illisia, Athens, Greece;Dept. of Informatics and Telecommunications, University of Athens, Panepistimiopolis, Illisia, Athens, Greece
Venue:
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Year:
2006

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Abstract

We present a novel dedicated hardware system for the extraction of second-order statistical features from high-resolution images. The selected features are based on gray level co-occurrence matrix analysis and are angular second moment, correlation, inverse difference moment and entropy. The proposed system was evaluated using input images with resolutions that range from 512(512 to 2048(2048 pixels. Each image is divided into blocks of user-defined size and a feature vector is extracted for each block. The system is implemented on a Xilinx VirtexE-2000 FPGA and uses integer arithmetic, a sparse co-occurrence matrix representation and a fast logarithm approximation to improve efficiency. It allows the parallel calculation of sixteen co-occurrence matrices and four feature vectors on the same FPGA core. The experimental results illustrate the feasibility of real-time feature extraction for input images of dimensions up to 2048(2048 pixels, where a performance of 32 images per second is achieved.