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Dedicated Hardware for Real-Time Computation of Second-Order Statistical Features for High Resolution Images

  • Dimitris Bariamis
  • Dimitris K. Iakovidis
  • Dimitris Maroulis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)

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.

Keywords

Feature Vector Input Image Image Block Very Large Scale Integration Memory Bank 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dimitris Bariamis
    • 1
  • Dimitris K. Iakovidis
    • 1
  • Dimitris Maroulis
    • 1
  1. 1.Dept. of Informatics and TelecommunicationsUniversity of AthensPanepistimiopolis, IllisiaGreece

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