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Split and merge segmentation is an image processing technique used to segment an image.The image is successively split into quadrants based on a homogeneity criterion and similar regions are merged to create the segmented result.
Split-and-merge segmentation is based on a quadtree partition of an image. It is sometimes called quadtree segmentation. This method starts at the root of the tree that represents the whole image. If it is found non-uniform (not homogeneous), then it is split into four child squares (the splitting process), and so on.
Statistical region merging (SRM) is an algorithm used for image segmentation. [1] [2] The algorithm is used to evaluate the values within a regional span and grouped together based on the merging criteria, resulting in a smaller list.
A simple elastic snake is defined by a set of n points for =, …,, the internal elastic energy term , and the external edge-based energy term .The purpose of the internal energy term is to control the deformations made to the snake, and the purpose of the external energy term is to control the fitting of the contour onto the image.
The random walker algorithm is a segmentation algorithm solving the combinatorial Dirichlet problem, adapted to image segmentation by L. Grady in 2006. [16] In 2011, C. Couprie et al. proved that when the power of the weights of the graph converge toward infinity, the cut minimizing the random walker energy is a cut by maximum spanning forest. [17]
The lambda-connected segmentation is a region-growing segmentation method in general. It can also be made for split-and-merge segmentation. [ 4 ] Its time complexity also reaches the optimum at O ( n l o g n ) {\displaystyle O(nlogn)} where n {\displaystyle n} is the number of pixels in the image.
Pages in category "Image segmentation" ... Split and merge segmentation; Statistical region merging; T. Thresholding (image processing) W. Watershed (image processing) Y.
Connected-component labeling (CCL), connected-component analysis (CCA), blob extraction, region labeling, blob discovery, or region extraction is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic.