Editing Image segmentation (section)

== Edge detection ==

[[Edge detection]] is a well-developed field on its own within image processing. Region boundaries and edges are closely related,
since there is often a sharp adjustment in intensity at the region boundaries. Edge detection techniques have therefore been used as the base of another segmentation technique.

The edges identified by edge detection are often disconnected. To segment an object from an image however, one needs closed region boundaries. The desired edges are the boundaries between such objects or spatial-taxons.<ref>[[R. Kimmel and A.M. Bruckstein.]] https://www.cs.technion.ac.il/~ron/PAPERS/Paragios_chapter2003.pdf, ''International Journal of Computer Vision'' 2003; 53(3):225–243.</ref><ref>[[R. Kimmel]], https://www.cs.technion.ac.il/~ron/PAPERS/laplacian_ijcv2003.pdf, chapter in Geometric Level Set Methods in Imaging, Vision and Graphics, (S. Osher, N. Paragios, Eds.), Springer Verlag, 2003. {{ISBN|0387954880}}</ref>

Spatial-taxons<ref>Barghout, Lauren. [http://www.lirmm.fr/~lafourcade/pub/IPMU2014/papers/0443/04430163.pdf Visual Taxometric approach Image Segmentation using Fuzzy-Spatial Taxon Cut Yields Contextually Relevant Regions]. Communications in Computer and Information Science (CCIS).  Springer-Verlag. 2014</ref> are information granules,<ref>Witold Pedrycz (Editor), Andrzej Skowron (Co-Editor), Vladik Kreinovich (Co-Editor). Handbook of Granular Computing. Wiley 2008</ref> consisting of a crisp pixel region, stationed at abstraction levels within a hierarchical nested scene architecture. They are similar to the [[Gestalt psychology|Gestalt]] psychological designation of figure-ground, but are extended to include foreground, object groups, objects and salient object parts. Edge detection methods can be applied to the spatial-taxon region, in the same manner they would be applied to a silhouette. This method is particularly useful when the disconnected edge is part of an illusory contour<ref>Barghout, Lauren (2014). Vision. Global Conceptual Context Changes Local Contrast Processing (Ph.D. Dissertation 2003). Updated to include Computer Vision Techniques. Scholars' Press. {{ISBN|978-3-639-70962-9}}.</ref><ref>Barghout, Lauren, and Lawrence Lee. "Perceptual information processing system."  Google Patents</ref>

Segmentation methods can also be applied to edges obtained from edge detectors. Lindeberg and Li<ref>{{cite journal | last1 = Lindeberg | first1 = T. | last2 = Li | first2 = M.-X. | year = 1997 | title = Segmentation and classification of edges using minimum description length approximation and complementary junction cues | url = http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A473385&dswid=-8029 | journal = Computer Vision and Image Understanding | volume = 67 | issue = 1| pages = 88–98 | doi=10.1006/cviu.1996.0510}}</ref> developed an integrated method that segments edges into straight and curved edge segments for parts-based object recognition, based on a minimum description length (M<sub>DL</sub>) criterion that was optimized by a split-and-merge-like method with candidate breakpoints obtained from complementary junction cues to obtain more likely points at which to consider partitions into different segments.