High Performance Colored Image Segmentation System Based on Neural Network

Abstract

Abstractimage segmentation is often the most time-consuming part of image processing systems. Traditionally, systems employing real-time color-based segmentation are either implemented in hardware, or in very specific software systems. This paper describes an FPGA implementation of a skin color segmentation based on a neural network. The proposed segmentation approach is an essential stage for face detection. The system uses a multilayer feedforward neural architecture with three-inputs, one hidden layer, two output neurons and a pipelined saturating linear activation function to simplify the FPGA hardware implementation. The system was tested by using different colored face images for face segmentation problem and its performance was compared with the results obtained using advanced software system designed specifically for face segmentation. A comparable performance was achieved and a speed up of (64583) was estimated compared to a Pentium 4, 2.4 GHz general purpose sequential computer and when it is compared to reduced instruction set computer IBM RISC 350 station, it was (407).Keywords: Neural network implementation, image Segmentation, FPGA based systems