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Research of Image Segmentation Based on Otsu Algorithm

Author: LiMei
Tutor: HuMin
School: Hefei University of Technology
Course: Applied Computer Technology
Keywords: Image Segmentation Otsu Algorithm Genetic Algorithms Class dispersion
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 188
Quote: 4
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Abstract


Image segmentation is one of the most basic and the most important areas in image processing and computer vision field of low-level vision , with a wide range of applications at the same time . At present, the existing segmentation algorithm in a practical application in the field there are a variety of problems , such as time-consuming , the contour of the target is blurred, broken, or important details have been ignored , and the like . Solve these problems , launched a series of studies , the main content is as follows : First , as the center point of a study to analyze and summarize the existing segmentation algorithm , select classic Otsu threshold segmentation method algorithm and combines the global intelligent optimization algorithm , namely Genetic Algorithms as a starting point for further research . Second , for dimensional 0tsu adaptive threshold algorithm to calculate the high complexity of the problem , and propose a new fast and efficient 0tsu image segmentation improved algorithm . The algorithm by finding the Otsu method the threshold value of the two one-dimensional segmentation threshold instead of the traditional two - dimensional Otsu method , the computational complexity is reduced from O (L4) to O (L) makes segmentation . To ensure the integrity algorithm is introduced split object classes within the concept of the minimum dispersion . The theoretical analysis and experimental results show that the computing speed of the algorithm is not only better than the original two-dimensional Otsu algorithm , and split better . Finally, in order to further overcome Otsu algorithm consuming a combination of an improved genetic algorithm optimization threshold . The algorithm introduces a degree of aggregation of the groups , and as a cross , the division of the mutation probability scale , thus adaptive regulation of crossover , mutation probability , the experimental results show that the algorithm to avoid the problem of \real-time , while the split effect is better than traditional genetic algorithm and AGA effect .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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