| Image segmentation is a classical and basic issue in pattern recognition and computer vision. It is a key technique in the fields of image processing, analysis and understanding, etc, whose result directly impacts on the performance of vision system. In addition, because of various reasons caused by imaging process, there are likelihood and uncertainty between targets and background in the image. Fortunately, fuzzy information processing technique is fit for dealing with such situation, so it has been widely used in image segmentation.In this thesis, the applications of fuzzy -means (FCM) clustering algorithm in image segmentation is studied deeply, and a series of new ideas and approaches are presented in view of some drawbacks of FCM algorithm for image segmentation. The main research results can be concluded as follows.Firstly, two kinds of 2D histogram are constructed. Then, with combining weighting FCM algorithm and pyramid structure, a new fast image segmentation method is proposed based on a 2D histogram weighting and pyramid structure FCM algorithm. The new algorithm overcomes some disadvantages of the standard FCM algorithm, for example lower speed and sensitivity to noise.Secondly, a cluster validity function, named modified partition fuzzy degree, is introduced for realization of automatically determining the optimal category number of image segmentation. Moreover, the effect of weighting exponent m in FCM algorithm on segmentation performance is investigated. Some meaningful conclusions are made which illustrate the relations between image features and parameter m, and connection between segmentation precision, speed and m.Finally, in order to increase the precision of segmentation, spatial information is introduced as constraint to improve the robustness of the FCM algorithm in image segmentation.The experimental results illustrate the effectiveness of the new proposed method, the correctness of the choice of parameter and the accuracy of segmentation results. |
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