An adaptive split-and-merge method for binary image contour data compression

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

24 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)299-307
Journal / PublicationPattern Recognition Letters
Volume22
Issue number3-4
Publication statusPublished - Mar 2001
Externally publishedYes

Abstract

The split-and-merge method is a well-known algorithm for polygonal approximation in computer vision applications such as feature extracting and pattern matching. Its accuracy depends on the tolerance, that is the error threshold value. This study presents a split-and-merge method with an adaptive tolerance value for compressing image contours. The tolerance value, which depends on the grid constant D and the line length of line L in a collinearity test, is adopted to reduce quantization error while keeping its original shape. A contour tracing method that achieves the right shape representation of binary images is also discussed. Experimental results for real binary contours show the method is effective for compression of a binary image. The proposed method allows a precise description of the original image and can smooth coarse contours. It is also computationally efficient. © 2001 Elsevier Science B.V. All rights reserved.

Research Area(s)

  • Contour representation, Data compression, Polygonal approximation, Split-and-merge, Tolerance

Citation Format(s)

An adaptive split-and-merge method for binary image contour data compression. / Xiao, Yi; Jia Zou, Ju; Yan, Hong.
In: Pattern Recognition Letters, Vol. 22, No. 3-4, 03.2001, p. 299-307.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review