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The simplest method of image segmentation is called the thresholding method. This method is based on a clip-level (or a threshold value) to turn a gray-scale image into a binary image. The key of this method is to select the threshold value (or values when multiple-levels are selected).
In computer vision and image processing, Otsu's method, named after Nobuyuki Otsu (大津展之, Ōtsu Nobuyuki), is used to perform automatic image thresholding. [1] In the simplest form, the algorithm returns a single intensity threshold that separate pixels into two classes, foreground and background.
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.
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.
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 .
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.
The random walker algorithm is an algorithm for image segmentation.In the first description of the algorithm, [1] a user interactively labels a small number of pixels with known labels (called seeds), e.g., "object" and "background".
Example of livewire segmentation on a baby's photo. Livewire, is a segmentation technique which allows a user to select regions of interest to be extracted quickly and accurately, using simple mouse clicks. [1] It is based on the lowest cost path algorithm, by Edsger W. Dijkstra. Firstly convolve the image with a Sobel filter to extract edges ...