An adaptive split-and-merge method for binary image contour data compression
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 299-307 |
Journal / Publication | Pattern Recognition Letters |
Volume | 22 |
Issue number | 3-4 |
Publication status | Published - Mar 2001 |
Externally published | Yes |
Link(s)
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.
In: Pattern Recognition Letters, Vol. 22, No. 3-4, 03.2001, p. 299-307.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review