A new fast one-pass thinning algorithm and its parallel hardware implementation

doi:10.1016/0167-8655(90)90081-C

Pattern Recognition Letters

Volume 11, Issue 7, July 1990, Pages 471-477

https://doi.org/10.1016/0167-8655(90)90081-C Get rights and content

Abstract

A new parallel thinning algorithm and its parallel hardware implementation are proposed. The algorithm requires only a single pass of parallel operations per iteration, and a small number of iterations before obtaining satisfactory results. This makes the algorithm suitable for real-time image processing. The thinning results are also good in quality due to good selection of matching templates and the use of a two-level processing scheme.

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A one-pass thinning algorithm and its parallel implementation
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J.T. Kuehn et al.
Parallel image thinning and vectorization on PASM

There are more references available in the full text version of this article.

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This thinning algorithm needs to ensure the connectivity of the thin lines after thinning, and the thinning result is the center lines of the original lines in the input character image. In this work, the One-Pass Thinning Algorithm (OPTA) is adopted for this task due to its competitive performance with moderate computational consumption [17]. As a result, the skeleton of the Chinese character is generated as a collection of points.
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This standard method can be used as a basis for identifying the abdomen and back of the shrimp. Chen et al. obtained a skeletal diagram of shrimp by using a refinement algorithm method to remove branch lines (Chen and Tsai, 1990). In comparison with manual segmentation, the accuracy of the refinement algorithm was over 97.00%.
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Hidden Deletable Pixel Detection Using Vector Analysis in Parallel Thinning to Obtain Bias-Reduced Skeletons
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The improvement of producing skeletons that preserve the significant geometric features of patterns is of great importance. One of feasible approaches is to develop a method embedded in a known parallel algorithm to produce bias-reduced skeletons since a bias skeleton usually degrades the preservation of significant geometric features of patterns. From our observations, bias skeletons always appear in the junction of lines which form an angle less than or near equal to 90°. In this paper, thehidden deletable pixel(HDP) which influences the speed of deleting the boundary pixels on a concave side is newly defined. Based on the comparable performance of our pseudo 1-subcycle parallel thinning algorithm (CH), a reduced form of larger support (twoL-pixel vectors, which is not the form ofk×ksupport) operated by an intermediate vector analysis about the deleted pixels in each thinning iteration is developed for HDP detection to obtain bias-reduced skeletons, which can be purchased by a reasonable computation cost. HDP restoration and parallel implementation are further considered to formulate an improved algorithm (CYS), where the connectivity preservation is guaranteed by the use ofCH's operators and HDP restoration. A set of synthetic images are used to quantify the skeleton from the geometry viewpoint and investigate the skeleton variations of using different (L)s. Based on the analyzing results, 3 ≤L≤ 9 are suggested for the current algorithm ofCYS.CYSis evaluated in comparison with two small support algorithms (AFP3andCH) and one larger support algorithm (VRCT) using the same patterns. Performances are reported by the number of iterations (NI), CPU time (TC), and number of unmatched pixels (N_unmatchfor bias-reduced measure). Results show that on the measure ofTC,CYSis approximately 2 to 3 times slower than the others, while on the measure ofNI, the four algorithms have approximately identical performance. On the measure ofN_unmatch,CYSis approximately 2 to 3 times less than the others. One-pixel boundary noise is also considered for exploring the noise immunity. The results suggest that the noise immunity ofCYSandCHis identical and is better than that ofAFP3. As a result, the better bias-reduced skeletons produced byCYSmay be purchased by a reasonable computation cost.
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Appendix: Digital topology - A brief introduction and bibliography
1996, Machine Intelligence and Pattern Recognition
In image processing and computer graphics, an object in the plane or 3-space is approximated digitally by a set of pixels or voxels. Digital topology studies the properties of this set of pixels or voxels that correspond to topological properties of the original object. It provides theoretical foundations for important operations such as digitization, connected component labeling and counting, boundary extraction, contour filling, and thinning. Some basic terminology and fundamental concepts of digital topology are discussed and important areas of the field are described in the chapter.

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^∗: Chin-Sung Chen is now with Electronics Research and Service Organization, Industrial Technology Research Institute, Chutung, Hsinchu, Taiwan 31015, Republic of China.

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A new fast one-pass thinning algorithm and its parallel hardware implementation

Abstract

Pattern Recognition

Computer Vision, Graphics, and Image Processing

Parallel image thinning and vectorization on PASM