AN FPGA-BASED ARCHITECTURE FOR REAL TIME IMAGE FEATURE EXTRACTION
Real-time image pattern recognition is a challenging task which involves image processing, feature extraction and pattern classification. It applies to a wide range of applications including multimedia , military and medical ones. Its high computational requirements force systems to use very expensive clusters, custom VLSI designs or even both. These approaches suffer from various disadvantages, such as high cost and long development times. Recent advances in fabrication technology allow the manufacturing of high density and high performance Field Programmable Gate Arrays ( FPGAs ) capable of performing many complex computations in parallel while hosted by conventional computer hardware. A variety of architecture designs capable of supporting real-time pattern recognition have been proposed in the recent literature , such as implementations of algorithms for image and video processing, classification and image feature extraction algorithms. Texture plays a significant role in image analysis & pattern recognition only a few architectures implement on-board textural feature extraction. Most prominent approaches include the extraction of Gabor wavelet features for face/object recognition and the computation of mean and contrast Gray Level Co occurrence Matrix (GLCM) features