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AN FPGA-BASED ARCHITECTURE FOR REAL TIME IMAGE FEATURE EXTRACTION

Platform : DSP

IEEE Projects Years : 2004

This paper appears in: Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on Issue Date: 23-26 Aug. 2004 AIM: To extract the features of the image from modular FPGA based architecture ABSTRACT We propose a novel FPGA-based architecture for the extraction of four texture features using gray level cooccurrence matrix (GLCM) analysis. These features are angular second moment, correlation, inverse difference moment, and entropy. The proposed architecture consists of a hardware and a software module. The hardware module is implemented on Xilinx Virtex-E V2000 FPGA using VHDL. It calculates many GLCMs and GLCM integer features in parallel. The software retrieves the feature vectors calculated in hardware, and performs complementary computations. The architecture was evaluated using standard grayscale images and video clips. The results show that it can be efficiently used in realtime pattern recognition applications. LEARNING OBJECTIVE: To understand the use of feature extraction which can be efficiently used in real time pattern recognition. INPUT: textured Image OUTPUT : Extraction of features using gray level co occurrence matrix (GLCM). These features are angular second moment, correlation, inverse difference moment. APPLICATIONS: Image processing, Video processing and transmission SOFTWARE TOOL USED: MATLAB 2007A

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