Adaptive generalized metrics, distance maps and nearest neighbor transforms on gray tone images

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摘要

This paper aims to introduce and study two novel metrics on gray tone images. These metrics are based on the General Adaptive Neighborhood Image Processing (GANIP) framework that enables to represent an image by spatial neighborhoods, named General Adaptive Neighborhoods (GAN) that fit to their local context. These metrics are generalized in the sense that they do not satisfy all the axioms of a standard mathematical metric. This notion of adaptive generalized metrics leads to the definition of relevant GAN distance maps and GAN nearest neighbor transforms used for image segmentation.

论文关键词：CLIP,Classical Linear Image Processing,DT,distance transform,DTOCS,distance transform on curved space,GAN,General Adaptive Neighborhood,GANIP,General Adaptive Neighborhood Image Processing,GLIP,General Linear Image Processing,GTT,geodesic time transform,LIP,logarithmic image processing,NNT,nearest neighbor transform,SKIZ,skeleton by influence zones,WDTOCS,weighted distance transform on curved space,Adaptive generalized metric,Distance transform,General adaptive neighborhood,Generalized distance map,Image segmentation,Nearest neighbor transform

论文评审过程：Received 21 October 2009, Revised 15 July 2011, Accepted 24 December 2011, Available online 13 January 2012.

论文官网地址：https://doi.org/10.1016/j.patcog.2011.12.026