Study On Image Detection Technique And Its Application On Detecting Defects Of Leather

In order to utilize materials effectively and avoid mixing inferior products with good products in production, it is necessary to detect materials and products. For a long time the detection of materials and products is done manually, this reduces production efficiency and adds costs. As the result of progress of computer vision, computer can make the detection now, this liberates workers from heavy labor and reduces costs. Leather is main material for making shoes, in shoemaking the main working procedures are nesting and cutting all kinds of shoe parts on leather with high quality, but it is inevitable that there are all kinds of defects on leather, such as scratch, mites (insect bites) and scars, so it is necessary to detect the positions and size of defects before nesting and cutting shoe parts in order to utilize materials effectively. Because of printing all kinds of pattern on the face of leather depending on style of shoes to be made, leather has typical texture on the face. Based on image processing this paper presented a series of algorithms aimed at leather which detected defects on the face of leather automatically.This paper presented two classifiers based on Fisher criterion and Nerve Net (NN) to classify the face and the inverse of leather automatically. Because of nesting and cutting being done on the face of leather, so detecting defects is done on the face of leather and it is important for automatic system of detecting defects to recognize the face and the inverse of leather before nesting and cutting shoe parts. Based on different texture of the face and the inverse of leather, this paper extracted characteristic vector of the face and the inverse of leather using co-occurrence matrix and classified the face and the inverse of leather using Fisher classifier and NN base on characteristic vector.This paper presented a novel denoising algorithm based on wavelet packet decomposition in order to wipe off noise mixed with texture lying in high frequency. The quality of image has a great effect on image detection, while acquiring leather image, because of the effect of environment and system, a lot of noise is mixed with leather image, and so it is necessary to improve SNR by denoising image using adaptive methods before detecting defects. Because both texture and noise lie in high frequency, so usual methods for denoising cannot denoise leather image effectively, based on the different character signal and noise show as wavelet decomposition goes on, this paper presented a novel denoising algorithm which decomposed the leather image using wavelet packet and treated signal and noise with different methods in subband images, this algorithm not only can wipe off a lot of noise in leather image, but also can keep the texture and defects in leather image.This paper presented an effective fusion algorithm for leather images based on wavelet decomposition. Due to the limitation of light source and viewing angle of device for acquiring image, only one image of leather cannot exhibit the whole character of leather, it is necessary to fuse many images with redundancy and complementarity to get the whole character of leather. Based on wavelet decomposition this paper presented a fusion algorithm, which extracted the local edge info in subband images as fusion rule and fused two leather images with complementarity.For not getting the prior info of defects on the face of leather, this paper presented a modified FCM based on neighbor info. This algorithm first extracted the texture characteristic vectors ofleather using co-occurrence matrix, then clustered the characteristic vectors using modified FCM, this algorithm can detect the defects on leather exactly. In order to decide the number of regions adaptively, this paper introduced a valid function based on intra-class and inter-class variance.