• Title/Summary/Keyword: Intra Class Minimum Variance

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Using Mean Shift Algorithm and Self-adaptive Canny Algorithm for I mprovement of Edge Detection (경계선 검출의 향상을 위한 Mean Shift 알고리즘과 자기 적응적 Canny 알고리즘의 활용)

  • Shin, Seong-Yoon;Pyo, Seong-Bae
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.7
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    • pp.33-40
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    • 2009
  • Edge detection is very significant in low level image processing. However, majority edge detection methods are not only effective enough cause of the noise points' influence, even not flexible enough to different input images. In order to sort these problems, in this paper an algorithm is presented that has an extra noise reduction stage at first, and then automatically selects the both thresholds depending on gradient amplitude histogram and intra class minimum variance. Using this algorithm, can fade out almost all of the sensitive noise points, and calculate the propose thresholds for different images without setting up the practical parameters artificially, and then choose edge pixels by fuzzy algorithm. In finally, get the better result than the former Canny algorithm.