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Region growing is a simple region-based image segmentation method. It is also classified as a pixel-based image segmentation method since it involves the selection of initial seed points. This approach to segmentation examines neighboring pixels of initial seed points and determines whether the pixel neighbors should be added to the region. The process is iterated on, in the same manner as general data clustering algorithms. A general discussion of the region growing algorithm is described below. ==Region-based segmentation== The main goal of segmentation is to partition an image into regions. Some segmentation methods such as thresholding achieve this goal by looking for the boundaries between regions based on discontinuities in grayscale or color properties. Region-based segmentation is a technique for determining the region directly. The basic formulation is: : : : : : : is a logical predicate defined over the points in set and is the null set. (a) means that the segmentation must be complete; that is, every pixel must be in a region. (b) requires that points in a region must be connected in some predefined sense. (c) indicates that the regions must be disjoint. (d) deals with the properties that must be satisfied by the pixels in a segmented region. For example if all pixels in have the same grayscale. (e) indicates that region and are different in the sense of predicate . 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Region growing」の詳細全文を読む スポンサード リンク
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