Algorithms Of 2-D Fruit Shape Detection And Classification

In the process of fruit quality inspection and fruit sorting, shape is a very important index, which isprescribed by national Standard. Based on the comparison analysis of different algorithms and a numberof experiments, a united framework algorithm for shape detection was developed; the concept of shapewas defined from the mathematic view; and difference shape descriptors were analyzed for normalshape, slight abnormal shape and serious abnormal shape respectively, the effects of differenct classifierto class result were discussed; At last, for correction ration of the classification by shape descriptors wasnot satisfied with request, a new metric was designed and a new shape classification framework basedon registration technique wasconstructed, and expected research object was achieved. Main contentsand results were listed as follows:1. A difficult problem of how to select appropriate method for fruit shape detection from many imagedenoise techniques was solved. When the noise type was "Guassian" or "Random", and SNR wasover 8, TV method can achieve the best resume result, when SNR was under 8, Winner filter canachieve the best resume result; when the noise type was "salt&pepper", median filter can achievethe best resume result.2. A method based on General Matrix Inverse (GMI) and Singular Value Decomposition (SVD) wasdeveloped. Some standard balls were used for the experiments to detect the size of balls in theresumed image. The results showed that the new method was superior to the traditional blindconvolution and the speed is faster than difference successive 6 times and than project iteration 60times.3. Multi-scale level set algorithm for shape detection was developed, and resolved the problem thatfruit shape fruit shape which surface contain rich color features could not be detected by traditionalalgorithms not only detection operator but also Gradient Vector Flow. The experiment resultdemonstrated that this algorithm has many virtues: no need of any shape preprocess; is illuminationadaptive; can smoothly detect fruit shape which surface contain rich color features, So it is very fitfor fruit shape detection.4. A shape descriptors based on multi-scale energy distribution was proposed. The main idea was thatshape contour series could be viewed as one periodic signal, and from the view of multi-resolutionanalysis, the main energies representing global shape information were distributed at coarse scale,while the subordinate energies representing local shape information were distributed at fine scale.This method was effective for the classification serious abnormal fruit and experiment resultdemonstrated that the correct ratio can achieve 81.20%, the method for selection start point whichwas base on maximum expectation in this algorithm, could determine only start point, with wasvery effective to solute the invariant of rotation for shape description.5. The shape descriptors currently used were system analyzed, and wavelet moment was proposed forfruit shape description, and some conclusions were concluded that when wavelet base which is symmetry (e.g. Morlet) and nearest neighbor were used for fruit shape classification, the correctratio of normal shape, slight abnormal and serious abnormal can arrived at 69.42ï¼…, 80.47ï¼…and72.62ï¼…respectively.6. The influence of different classifiers on fruit shade classification was analyzed, especially on theinfluence of four classifiers (linear discrimination function, cluster analysis, back propagationneural network and support vector machine) combined with three feature patterns (Fourierdescriptor feature patterns, Zernike moment feature patterns and wavelet moment feature patterns).The conclusions were listed as follows: 1) best results were obtained by the wavelet featurepatterns combined with each classifier; 2) for wavelet moment feature patterns, good precision ofclassification were obtained by the classifier of cluster analysis (three cluster centre) and supportvector machine. The correction ration of normal shape, slight abnormality and serious abnormalitywas 86.21ï¼…, 65.78ï¼…and 85.71ï¼…when cluster analysis (three cluster center) was used. Whensupport vector machine was used as classifier, the correction ration of normal shape, slightabnormality and serious abnormality was 70ï¼…, 83.56ï¼…and 75ï¼…, respectively.7. A new method for shape classification based on registration was developed. In this method, a newmetric based on the principle of level set representation of difference area was designed. Minimalmetric registration and motion estimating registration were analyzed, the normal classificationcorrection ratio is 91.20ï¼…, slight abnormal classification correction ratio is 85.88ï¼…and seriousabnormal classification correction ratio was 83.34ï¼…. The result demonstrated that this methodbased on registration could obtain better classification correction ration than the traditional methodbased on shape description.