Segmentation through variable-order surface fitting

Author

Besl, Paul J. ; Jain, Ramesh C.

Author_Institution

Dept. of Comput. Sci., Michigan Univ., Ann Arbor, MI, USA

Volume

10

Issue

2

fYear

1988

fDate

3/1/1988 12:00:00 AM

Firstpage

167

Lastpage

192

Abstract

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

Keywords

computerised picture processing; iterative methods; bivariate functions; computerised picture processing; image segmentation; image structure; iterative region-growing method; noiseless image reconstruction; piecewise-smooth surface model; surface coherence; surface curvature sign labelling; variable-order surface fitting; Coherence; Data mining; Image reconstruction; Image segmentation; Labeling; Noise shaping; Partitioning algorithms; Shape; Surface fitting; Surface reconstruction;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.3881

Filename

3881