In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.
The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s). When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of interpolation algorithms like Marching cubes.
Read more about Image Segmentation: Applications, Thresholding, Clustering Methods, Compression-based Methods, Histogram-based Methods, Edge Detection, Region-growing Methods, Split-and-merge Methods, Partial Differential Equation-based Methods, Graph Partitioning Methods, Watershed Transformation, Model Based Segmentation, Multi-scale Segmentation, Semi-automatic Segmentation, Trainable Segmentation, Segmentation Benchmarking
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“You make yourselves out to be shepherds of the flock and yet you allow your sheep to live in filth and poverty. And if they try and raise their voices against it, you calm them by telling them their suffering is the will of God. Sheep, indeed. Are we sheep to be herded and sheared by a handful of owners? I was taught man was made in the image of God, not a sheep.”
—Philip Dunne (1908–1992)