A scalable and embedded FPGA architecture for efficient computation of grey level co-occurrence matrices and Haralick textures features

This paper presents a novel and optimized embedded architecture based FPGA for an efficient and fast computation of grey level co-occurrence matrices (GLCM) and Haralick textures features for use in high throughput image analysis applications where time performance is critical. The originality of th...

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Bibliographic Details
Published in:Microprocessors and microsystems 2010-02, Vol.34 (1), p.14-24
Main Authors: Siéler, L., Tanougast, C., Bouridane, A.
Format: Article
Language:English
Subjects:
Co-occurrence matrix
Embedded system
FPGA
Halarick features
Image and texture analysis
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Summary:This paper presents a novel and optimized embedded architecture based FPGA for an efficient and fast computation of grey level co-occurrence matrices (GLCM) and Haralick textures features for use in high throughput image analysis applications where time performance is critical. The originality of this architecture allows for a scalable and a totally embedded on Chip FPGA for the processing of large images. The architecture was implemented on Xilinx Virtex-FPGAs without the use of external memory and/or host machine. The implementations demonstrate that our proposed architecture can deliver a high reduction of the memory and FPGA logic requirements when compared with the state of the art counterparts and it also achieves much improved processing times when compared against optimized software implementation running on a conventional general purpose processor.
ISSN:0141-9331
1872-9436
DOI:10.1016/j.micpro.2009.11.001