| Study on the real time fruit quality detection by computer vision is an attractive and prospective R & D subject for improving marketing competition and post harvesting value-added processing technology of fruit products. As China entering WTO,it becomes more and more urgent. The objectives of this research are contributed to develop method and technology for fruit on line detection by computer vision. It aims at solving the problems,such as fast processing the large amount of image information,improving system performance for real time dynamic image capture and processing capability,increasing precision of detection and on line grading system establishment,etc. The results of study are briefly summarized as follows:1. Based on analysis of current differential edge detection arithmetic operators and the requirement to detect only outer edge of objects in fruit detection,two new methods - gray adjacent area and template analysis were introduced for the solution. The image area detected with the new methods is only equal to half of traditional way,but the processing speed can be doubled. Because the search was carried out along equal angle on a circle,the edge point detected can be clear ordered and keeping continuity,so the farther thinning and serial processing were not needed and the processing speed of system was much improved.2. Based on the uniformity of image background,a method of quick image orientation and marking was put forward. The 10 x 10 grids can be used to deal with the image of 160 x 140. Only after processing 224 pixels,the reference figure center and average radius of object can be calculated by particle method. It is very effective to reduce processing area and to improve processing speed.3. With the problem of blurred image caused by object motion,traditional difference algorithm based on analyzing the model of moving image was generally adopted to recover the blurred images. But its calculation work is too much and the capability of real time processing is bad. The resume process actually is as approximate calculation. A new method in this paper was presented to resume the original image based on the pixel analysis. The method of motion imaging and combining with the characteristic of image could be resumed by pixels decomposing. The test showed good features and processing speed is quite fast.4. In order to measure the size of fruit,a new way for the measurement of axis direction and width was presented in this paper. In the traditional way,the stem-end can not be ascertained,which would cause measurement error. The new method may overcome this disadvantage. The measurement direction of new method was consistent with national standards. By the measurement experiment,the results of computer detection has better than the human operation. The presented model of fruit shape can improve the shape description both in qualitative and quantitative analysis. The model is used as basis for shape classification. It may simplify the classification process and make the process more effective.5. The fractal dimension of every hue area was considered as color feature value. The fractal featurevalue of four hue area were used as input mode. The color was graded by artificial neural networks. Because of considering accumulative character and space distributing character of each hue at the same time,these feature values can make the color classification more close to reality.6. A normal sphere hue model based on reflect character and average radius of image was introduced. It is used for fruit defects segmentation through comparison between the gray values of model and the detected object image. The defects were divided by the gray value of the image. Only one threshold is needed for a successful segmentation of the defects flaw of every gray level. In this method,less calculation is required and the processing speed is faster. There was no any juncture on edge.7. A method based on the sphere frame character was used to identify the surface defects an...
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