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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.
After each split, a test is necessary to determine whether each new region needs further splitting. The criterion for the test is the homogeneity of the region. There are several ways to define homogeneity, some examples are: Uniformity- the region is homogeneous if its gray scale levels are constant or within a given threshold.
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.
Motion based segmentation is a technique that relies on motion in the image to perform segmentation. The idea is simple: look at the differences between a pair of images. Assuming the object of interest is moving, the difference will be exactly that object. Improving on this idea, Kenney et al. proposed interactive segmentation . They use a ...
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 ...
The use of image texture can be used as a description for regions into segments. There are two main types of segmentation based on image texture, region based and boundary based. Though image texture is not a perfect measure for segmentation it is used along with other measures, such as color, that helps solve segmenting in 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]
GrabCut is an image segmentation method based on graph cuts.. Starting with a user-specified bounding box around the object to be segmented, the algorithm estimates the color distribution of the target object and that of the background using a Gaussian mixture model.