SEGMENTATION THROUGH VARIABLE-ORDER SURFACE FITTING

被引：626

作者：

BESL, PJ ^{[1
]}

JAIN, RC ^{[1
]}

机构：

[1] UNIV MICHIGAN,DEPT ELECT ENGN & COMP SCI,COMP VIS RES LAB,ANN ARBOR,MI 48109

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1988年 / 10卷 / 02期

关键词：

IMAGE PROCESSING - Image Analysis - MATHEMATICAL TECHNIQUES - Approximation Theory - SURFACES;

D O I：

10.1109/34.3881

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The solution of the segmentation problem requires a mechanism for partitioning the image array into low-level entities based on a model of the underlying image structure. A piecewise-smooth surface model for image data that possesses surface coherence properties is used to develop an algorithm that simultaneously segments a large class of images into regions of arbitrary shape and approximates image data with bivariate functions so that it is possible to compute a complete, noiseless image reconstruction based on the extracted functions and regions. Surface curvature sign labeling provides an initial coarse image segmentation, which is refined by an iterative region-growing method based on variable-order surface fitting. Experimental results show the algorithm's performance on six range images and three intensity images.

引用

页码：167 / 192

页数：26

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