Abstract: To improve the image segmentation accuracy of machine vision system, a Multi-Neural Network (MNN) segmentation algorithm based on wavelet transform is proposed. The algorithm consists of three phases: multi-neural network region division, feature extraction and classification. The training region and the region to be classified near the boundary of the initial segmentation are divided into several small regions by the polygon fitting algorithm. The feature extraction is achieved by the extension of images which are the result of wavelet transform of the original image. The neural network classifier classifies the pixels in the region to be classified to target pixels and background pixels, and to obtain the segmentation results, some post-processing are performed.The multi-neural network algorithm and the threshold segmentation algorithm are compared with the segmentation accuracy of bearing defect image. The result shows that the Pixel Error (PE) of the multi-neural network algorithm is reduced by 75 % than the threshold segmentation algorithm. Segmentation accuracy is improved significantly.

Key words: image segmentation, machine vision, neural network, wavelet transform, feature extraction