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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Karkanis, S.A., Iakovidis, D.K., Maroulis, D.E., Karras, D.A., Tzivras, M.: Computer Aided Tumor Detection in Endoscopic Video Using Color Wavelet Features. IEEE Trans. Inf. Technol. Biomed. 7, 141–152 (2003)CrossRefGoogle Scholar
  2. 2.
    Tahir, M.A., Bouridane, A., Kurugollu, F.: An FPGA Based Coprocessor for GLCM and Haralick Texture Features and their Application in Prostate Cancer Classification. Anal. Int. Circ. Signal Process. 43, 205–215 (2005)CrossRefGoogle Scholar
  3. 3.
    Baraldi, A., Parmiggiani, F.: An Investigation of the Textural Characteristics Associated with Gray Level Cooccurrence Matrix Statistical Parameters. IEEE Trans. Geosc. Rem. Sens. 33(2), 293–304 (1995)CrossRefGoogle Scholar
  4. 4.
    Shiranita, K., Miyajima, T., Takiyama, R.: Determination of Meat Quality by Texture Analysis. Patt. Rec. Lett. 19, 1319–1324 (1998)zbMATHCrossRefGoogle Scholar
  5. 5.
    Iivarinen, J., Heikkinen, K., Rauhamaa, J., Vuorimaa, P., Visa, A.: A Defect Detection Scheme for Web Surface Inspection. Int. J. Pat. Rec. Artif. Intell., 735–755 (2000)Google Scholar
  6. 6.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)CrossRefGoogle Scholar
  7. 7.
    Iakovidis, D.K., Maroulis, D.E., Karkanis, S.A., Flaounas, I.N.: Color Texture Recognition in Video Sequences Using Wavelet Covariance Features and Support Vector Machines. In: Proc. 29th EUROMICRO, Antalya, Turkey, September 2003, pp. 199–204 (2003)Google Scholar
  8. 8.
    Wei, C.-H., Li, C.-T., Wilson, R.: A Content-Based Approach to Medical Image Database Retrieval. In: Ma, Z. (ed.) Database Modeling for Industrial Data Management: Emerging Technologies and Applications. Idea Group Publishing, USA (2005)Google Scholar
  9. 9.
    York, T.A.: Survey of Field Programmable Logic Devices. Microprocessors and Microsystems 17(7), 371–381 (1993)CrossRefGoogle Scholar
  10. 10.
    Ba, M., Degrugillier, D., Berrou, C.: Digital VLSI Using Parallel Architecture for Co-occurrence Matrix Determination. In: Proc. Int. Conf. on Acoustics, Speech, and Signal Proc., vol. 4, pp. 2556–2559 (1989)Google Scholar
  11. 11.
    Heikkinen, K., Vuorimaa, P.: Computation of Two Texture Features in Hardware. In: Proc. 10th Int. Conf. Image Analysis and Processing, Venice, Italy, September 1999, pp. 125–129 (1999)Google Scholar
  12. 12.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, San Diego (1999)Google Scholar
  13. 13.
    Haralick, R.M.: Texture Measures for Carpet Wear Assessment. IEEE Trans. Pattern Analysis and Machine Intelligence 10(1), 92–104 (1988)CrossRefGoogle Scholar
  14. 14.
    Hennesy, J.L., Patterson, D.A.: Computer Architecture, A Quantitative Approach. Morgan Kaufmann, San Francisco (2002)Google Scholar

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

Personalised recommendations