Abstract

This paper discusses the solution of the problem of segmenting a digital image by means of a hierarchical sequence of piecewise-constant approximations that minimally differ from the image in the rms deviation. Analytical justification is given for the computations at the stage of preliminary automatic processing of the image without using controlling parameters. An algorithm is presented for combined segmentation/quality-improvement/clusterization and is illustrated by examples.

© 2015 Optical Society of America

Kernel-based spectral color image segmentation

Hongyu Li, Vladimir Bochko, Timo Jaaskelainen, Jussi Parkkinen, and I-fan Shen
J. Opt. Soc. Am. A 25(11) 2805-2816 (2008)

Multi-color space learning for image segmentation based on a support vector machine

Renzheng Zhang, Guodong Chen, Zheng Wang, Wenzheng Chi, Zhenhua Wang, Lining Sun, Guilin Yang, and Yifang Wen
OSA Continuum 2(11) 3050-3065 (2019)

Image segmentation of a color-blindness plate

Yung-Sheng Chen and Yu-Chang Hsu
Appl. Opt. 33(29) 6818-6822 (1994)

Multifocus color image sequence fusion based on mean shift segmentation

Xingxing Hao, Hui Zhao, and Jing Liu
Appl. Opt. 54(30) 8982-8989 (2015)

Joint contrast optimization and object segmentation in active polarimetric images

Guillaume Anna, Nicolas Bertaux, Frédéric Galland, François Goudail, and Daniel Dolfi
Opt. Lett. 37(16) 3321-3323 (2012)