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Green Image
Issue No. 03 - March (1990 vol. 12)
ISSN: 0162-8828
pp: 225-233
<p>A method that combines region growing and edge detection for image segmentation is presented. The authors start with a split-and merge algorithm wherein the parameters have been set up so that an over-segmented image results. Region boundaries are then eliminated or modified on the basis of criteria that integrate contrast with boundary smoothness, variation of the image gradient along the boundary, and a criterion that penalizes for the presence of artifacts reflecting the data structure used during segmentation (quadtree in this case). The algorithms were implemented in the C language on a Sun 3/160 workstation running under the Unix operating system. Simple tool images and aerial photographs were used to test the algorithms. The impression of human observers is that the method is very successful on the tool images and less so on the aerial photograph images. It is thought that the success in the tool images is because the objects shown occupy areas of many pixels, making it is easy to select parameters to separate signal information from noise.</p>
region boundaries; image gradient variation; pattern recognition; region growing; edge detection; image segmentation; split-and merge algorithm; boundary smoothness; data structure; quadtree; C language; Sun 3/160 workstation; Unix operating system; tool images; aerial photographs; pattern recognition; picture processing

Y. Liow and T. Pavlidis, "Integrating Region Growing and Edge Detection," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 12, no. , pp. 225-233, 1990.
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