Segmentation And Recognition Of Particle Object Based On Fuzzy Set Theory

The automatic particle segmentation and recognition is widely used in industry andagriculture. It is an important method of automatic detection. In this work, fuzzy set theory isapplied and well solves the fuzzy problem of particle segmentation and recognition. It wellhelps to improve the accuracy of segmentation and recognition. Several problems of fuzzy settheory and its application in particle segmentation and recognition are studied intensely. Themain works are arranged as follow:1. The fuzzy partition entropy has been widely adopted as a global optimizationtechnique for finding the optimized thresholds for multilevel image segmentation. However, itusually involves the local optimum thresholds. The main reason is the applied geneticalgorithm easily lead to the premature phenomena. In order to solve this problem, theimproved GA has been adapted which improves encoding mechanism, genetic operators andevolutionary direction of conventional genetic algorithm. Many experiments are carried outon particle images to demonstrate the feasibility and efficiency of the improved scheme.2. The fuzzy partition entropy segmentation involves expensive computation as thenumber of thresholds increases and often yields noisy segmentation results since spatialcoherence is not enforced. In this paper, an iterative calculation scheme is presented forreducing redundant computations in entropy evaluation. The efficiency of threshold selectionis further improved through utilizing population optimization algorithm. Consequently,instead of performing threshold segmentation for each pixel independently, the presentedalgorithm over-segments the input image into small regions and uses the probabilities offuzzy events to define the costs of different label assignments for each region. The finalsegmentation results are computed using graph cut, which produces smooth segmentationresults. Experiment results indicate that the presented method is not only superior to the samefuzzy entropy methods with different optimizing strategies in terms of processing time, butalso outperforms widely-used multi-threshold segmentation methods in terms of thesegmentation quality of aggregate images. Furthermore, the iterative scheme can dramaticallyreduce the runtime and keep it stable as the number of required thresholds increases. Dependson the optimization methods and the number of thresholds, the speedup varies from10to100times.3. To solve the problem of noise and poor precision about fuzzy partition entropy approach for segmenting the small particle objects (the propotion of object pix number ismore than10%), a new adaptive fuzzy partition entropy algorithm for the multi-thresholdsegmentation is proposed. Firstly, a threshold zone is obtained automatically by the iterativevalidation algorithm. After that, the particle images are divided into several differentsub-images by the automatic block algorithm. Finally, the fuzzy partition entropy based onthe population optimized algorithm is adopted to find out the best thresholds for eachsub-image. And the segmentation results are computed using graph cut for smoothing thesegmentation results. After being evaluated by various types of real FISH images andsimulated images, the misclassification error of other common algorithms are above8.00×10-2,while the one of proposed algorithm is less than7.00×10-2.4. To solve the fuzzy boundary problem of different characteristic parameters, a fuzzycomprehensive evaluation algorithm for particle recognition is presented. Firstly, themorphological image preprocessing method has been adopted to eliminate the adhesionparticles. After that, the characteristic parameters are extracted from the candidate particleobjects, and corresponding membership functions are built up. Finally, the fuzzycomprehensive evaluation has been applied to identify the particle objects in combinationwith characteristic weights and fuzzy relation matrix. The experiment results show that theproposed method produces more accurate results than the results acquired by the commonrecognition methods and the accuracy is up to95%.5. A completed particle segmentation and recognition system is designed. The fuzzyneural network is adopted to determine the suited fuzzy partition entropy method for differentparticle images. In order to implement the function of automatic decision, the fuzzy entropyvalues which under different fuzzy partition numbers are set as the input of the neural network.The fuzzy comprehensive evaluation matrix is used as nerve cell. And the determined fuzzypartition entropy method is designed as output. After learning a large number of samples, themodel of automatic algorithm selection can be acquired. Experiment results show that thesystem can work effectively in particle segmentation and recognition.