FRUIT IMAGE SEGMENTATION BASED ON LVQ NEURAL NETWORK
To solve the problem that the traditional edge detection operator is difficult to obtain a complete binary image when the color of fruit is diverse,and the brightness is uneven,and it depends on the optimized threshold,a new method of fruit image segmentation based on simplified LVQ neural network is proposed in this paper.Firstly,the color image is transformed into gray image.Then some points from the edge image obtained by Canny operator are randomly selected as the learning supervision signal,and only the gradient value of Kirsch operator in the 3 × 3 neighborhood of pixels at the same position in the gray image is taken as the network input,and the weights are trained;Finally,the gradient value of Kirsch operator of the whole gray image is re-input into the trained network to obtain a closed edge,which is filled into a binary image as the segmentation result.Fourteen fruit images with 640 × 480 pixels are selected for investigation.The results show that complete,consistent and noiseless binary images are segmented within a wide threshold range(0.001-0.99);The minimum area error is 0.9％with the maximum 8.83％.They are not depended on the optimized threshold and pre-filtering.Compared with algorithms without thresholds and filtering,this scheme has lower errors and time complexity;compared with algorithms that have set thresholds and/or filters,this scheme is comparable or even better.
fruit image segmentationlearning vector quantification(LVQ)neural networkKirsch operatorcanny operatorarea errortime complexitythreshold
万方数据
维普
