Edge Detection Algorithms implemented on Bi-i Cellular Vision System

Abstract

Bi-i (Bio-inspired) Cellular Vision system is built mainly on Cellular Neural /Nonlinear Networks (CNNs) type (ACE16k) and Digital Signal Processing (DSP) type microprocessors. CNN theory proposed by Chua has advanced properties for image processing applications. In this study, the edge detection algorithms are implemented on the Bi-i Cellular Vision System. Extracting the edge of an image to be processed correctly and fast is of crucial importance for image processing applications. Threshold Gradient based edge detection algorithm is implemented using ACE16k microprocessor. In addition, pre-processing operation is realized by using an image enhancement technique based on Laplacian operator. Finally, morphologic operations are performed as post processing operations. Sobel edge detection algorithm is performed by convolving sobel operators with the image in the DSP. The performances of the edge detection algorithms are compared using visual inspection and timing analysis. Experimental results show that the ACE16k has great computational power and Bi-i Cellular Vision System is very qualified to apply image processing algorithms in real time.