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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.〔Linda G. Shapiro and George C. Stockman (2001): “Computer Vision”, pp 279-325, New Jersey, Prentice-Hall, ISBN 0-13-030796-3〕〔Barghout, Lauren, and Lawrence W. Lee. "Perceptual information processing system." Paravue Inc. U.S. Patent Application 10/618,543, filed July 11, 2003.〕 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 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. == Applications == Some of the practical applications of image segmentation are: * Content-based image retrieval * Machine vision * Medical imaging * * Locate tumors and other pathologies〔W. Wu, A. Y. C. Chen, L. Zhao and J. J. Corso (2014): "Brain Tumor detection and segmentation in a CRF framework with pixel-pairwise affinity and super pixel-level features", International Journal of Computer Aided Radiology and Surgery, pp. 241-253, Vol. 9.〕〔E. B. George and M. Karnan (2012): "MR Brain image segmentation using Bacteria Foraging Optimization Algorithm", ''International Journal of Engineering and Technology'', Vol. 4.〕 * * Measure tissue volumes * * Diagnosis, study of anatomical structure * * Surgery planning * * Virtual surgery simulation * * Intra-surgery navigation * Object detection〔J. A. Delmerico, P. David and J. J. Corso (2011): "Building façade detection, segmentation and parameter estimation for mobile robot localization and guidance", International Conference on Intelligent Robots and Systems, pp. 1632-1639.〕 * * Pedestrian detection * * Face detection * * Brake light detection * * Locate objects in satellite images (roads, forests, crops, etc.) * Recognition Tasks * * Face recognition * * Fingerprint recognition * * Iris recognition * Traffic control systems * Video surveillance Several general-purpose algorithms and techniques have been developed for image segmentation. To be useful, these techniques must typically be combined with a domain's specific knowledge in order to effectively solve the domain's segmentation problems. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Image segmentation」の詳細全文を読む スポンサード リンク
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