Image segmentation and edge enhancement with stabilized inverse diffusion equations

被引：73

作者：

Pollak, I ^{[1
]}

Willsky, AS

Krim, H

机构：

[1] Brown Univ, Dept Math Appl, Providence, RI 02912 USA

[2] MIT, Dept Comp Sci & Elect Engn, Cambridge, MA 02139 USA

[3] MIT, Informat & Decis Syst Lab, Cambridge, MA 02139 USA

[4] N Carolina State Univ, Dept Elect Commun Engn, Raleigh, NC 27607 USA

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 2000年 / 9卷 / 02期

关键词：

diffusion; enhancement; scale space; segmentation; sliding modes; synthetic aperture radar (SAR);

D O I：

10.1109/83.821738

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce a family of first-order multidimensional ordinary differential equations (ODE's) with discontinuous right-hand sides and demonstrate their applicability in image processing. An equation belonging to this family is an inverse diffusion everywhere except at local extrema, where some stabilization is introduced. For this reason, we call these equations "stabilized inverse diffusion equations" (SIDE's), Existence and uniqueness of solutions, as well as stability, are proven for SIDE's, A SIDE in one spatial dimension may be interpreted as a limiting case of a semi-discretized Perona-Malik equation [14], [15], In an experimental section, SIDE's are shown to suppress noise while sharpening edges present in the input signal, Their application to image segmentation is also demonstrated.

引用

页码：256 / 266

页数：11

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