Research And Application Of Image Segmentation Based On Two-dimensional Otsu And Fuzzy Clustering

As one of the most important and basic fields in image processing andcomputer vision fields, image segmentation is widely applied in practice. Presently,there are many problems in the existed algorithms which applied in the practicalapplication, such as time consuming, the vaguely, rupture, or important details areignored. To solve these problems, this dissertation expands a series of researches.and research result is used to facial recognition. The main works of the dissertationcan be organized as follows:Firstly, analysis and summary the existing algorithms of imagesegmentation.The typical threshold segmentation algorithm, Otsu, is chosen as thecenter point of the research. Considering the disadvantage that the classicaltwo-dimensional Otsu thresholding method is time-consuming, this dissertationproposes an improved algorithm. By calculating two1D Otsu thresholdings insteadof the traditional2D Otsu thresholding, it reduced the complexity of the algorithm.Secondly, this dissertation introduces the theories of two-dimensional Otsuthresholding method. This dissertation analyzes the status and trends of FuzzyC-means clustering method. By discussing the advantages and disadvantages of thetwo-dimensional Otsu thresholding method and Fuzzy clustering method，a newimage segmentation method based on these two methods (two-dimensional Otsuthresholding method and Fuzzy Clustering method) is proposed，and the experimentresults prove its effectiveness.Finally, through researching of color face image segmentation and colorspaces，a color threshold image segmentation based on YCgCr color space isproposed. Then, this dissertation use mathematical morphology filter and coarsescreen based on the information of area and length-width ratio. The results ascandidates face are be shown. At the last, by calculating the Euler’s numbers, finalresults are obtained.