FPGA-based System for Real-Time Video Texture Analysis

Authors:
Dimitris Maroulis;Dimitris K. Iakovidis;Dimitris Bariamis
Affiliations:
Real Time Systems and Image Analysis Laboratory, Department of Informatics and Telecommunications, University of Athens, Athens, Greece;Real Time Systems and Image Analysis Laboratory, Department of Informatics and Telecommunications, University of Athens, Athens, Greece;Real Time Systems and Image Analysis Laboratory, Department of Informatics and Telecommunications, University of Athens, Athens, Greece
Venue:
Journal of Signal Processing Systems
Year:
2008

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Abstract

This paper describes a novel system for real-time video texture analysis. The system utilizes hardware to extract second-order statistical features from video frames. These features are based on the Gray Level Co-occurrence Matrix (GLCM) and describe the textural content of the video frames. They can be used in a variety of video analysis and pattern recognition applications, such as remote sensing, industrial and medical. The hardware is implemented on a Virtex-XCV2000E-6 FPGA programmed in VHDL. It is based on an architecture that exploits the symmetry and the sparseness of the GLCM and calculates the features using integer and fixed point arithmetic. Moreover, it integrates an efficient algorithm for fast and accurate logarithm approximation, required in feature calculations. The software handles the video frame transfers from/to the hardware and executes only complementary floating point operations. The performance of the proposed system was experimentally evaluated using standard test video clips. The system was implemented and tested and its performance reached 133 and 532 fps for the analysis of CIF and QCIF video frames respectively. Compared to the state of the art GLCM feature extraction systems, the proposed system provides more efficient use of the memory bandwidth and the FPGA resources, in addition to higher processing throughput, that results in real time operation. Furthermore, its fundamental units can be used in any hardware application that requires sparse matrix representation or accurate and efficient logarithm estimation.