Title :
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
fDate :
3/1/1988 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
