In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super 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 analyze. Image segmentation is often used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a tag to each pixel in an image so that pixels with the same tag share certain 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 is similar with respect to some calculated property or property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same feature (s). When applied to a stack of images, typical in the medical image, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of interpolation algorithms like Marching Cubes.
Applications
Some of the practical applications of image segmentation are:
• Content-based image retrieval
• Machine Vision
• Medical images
• Locate tumors and other pathologies
• Measure fabric volumes
• Diagnosis, study of anatomical structure
• Planning surgery
• Simulation of virtual surgery
• Intra-surgical navigation
• Detection of objects
• Pedestrian detection
• Face Detection
• Brake light detection
• Locate objects in satellite images (roads, forests, crops, etc.)
• Recognition Tasks
• Face Recognition
• Fingerprint recognition
• Recognition of the iris
• Traffic control systems
• Video surveillance