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

摘要

The split-and-merge method is a well-known algorithm for polygonal approximation in computer vision appli- cations 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.