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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.
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to ...
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.
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.
Image segmentation strives to partition a digital image into regions of pixels with similar properties, e.g. homogeneity. [1] The higher-level region representation simplifies image analysis tasks such as counting objects or detecting changes, because region attributes (e.g. average intensity or shape [2]) can be compared more readily than raw pixels.
Quadtree compression of an image step by step. Left shows the compressed image with the tree bounding boxes while the right shows just the compressed image. A quadtree is a tree data structure in which each internal node has exactly four children.
As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision [1]), such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, and many other computer vision problems that can be formulated in terms of energy minimization.