Binary picture thinning by an iterative parallel two-subcycle operation

doi:10.1016/0031-3203(87)90005-7

Pattern Recognition

Volume 20, Issue 3, 1987, Pages 297-307

https://doi.org/10.1016/0031-3203(87)90005-7 Get rights and content

Abstract

A thinning algorithm for digital binary pictures is presented. The algorithm repeats the removal of the deletable border points in parallel and the extraction of the final points. It is implemented as an iterative parallel two-subcycle operation. Consequently, the number of iterations required is about equal to the maximum width of the input figures. The algorithm erases the end points while the iteration number is smaller than a given threshold. Utilization of the final point condition makes the algorithm faster and the flexible deletion of end points produces a line pattern of good quality even for noisy figures.

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Cited by (39)

Model-based stroke extraction and matching for handwritten Chinese character recognition
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Citation Excerpt :
All feature points (control and corner points) are stored in a graph as nodes and a line segment linking two feature points is an edge. For character image thinning, we took the algorithm proposed by Suzuki and Abe [33]. It performs fairly well in respect of skeleton shape from our view.
This paper proposes a model-based structural matching method for handwritten Chinese character recognition (HCCR). This method is able to obtain reliable stroke correspondence and enable structural interpretation. In the model base, the reference character of each category is described in an attributed relational graph (ARG). The input character is described with feature points and line segments. The strokes and inter-stroke relations of input character are not determined until being matched with a reference character. The structural matching is accomplished in two stages: candidate stroke extraction and consistent matching. All candidate input strokes to match the reference strokes are extracted by line following and then the consistent matching is achieved by heuristic search. Some structural post-processing operations are applied to improve the stroke correspondence. Recognition experiments were implemented on an image database collected in KAIST, and promising results have been achieved.
General ribbon-based thinning algorithms for stylus-generated images
1999, Computer Vision and Image Understanding
Thinning algorithms for stylus-generated images are presented which are based on the general ribbon model of stylus-generated images. These algorithms have several advantages over existing thinning algorithms, including the existence of a formal specification of the desired output of a thinning algorithm; the preservation of image features which have been shown to be the most perceptually significant for the human perception of stylus-generated images; and the ability to deal easily with images which contain both stroke and blob objects.
Hidden Deletable Pixel Detection Using Vector Analysis in Parallel Thinning to Obtain Bias-Reduced Skeletons
1998, Computer Vision and Image Understanding
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.
Parallel Connectivity-Preserving Thinning Algorithms
1996, Machine Intelligence and Pattern Recognition
A variety of approaches to parallel thinning using operators with small supports are reviewed, with emphasis on how one may preserve, and prove one has preserved, connec- tivity. Tests are demonstrated for verifying connectivity preservation; and for fundamental classes of parallel thinning algorithms, including fully parallel, two-subiteration, and two- subfield, conditions are identified using these tests which are sufficient for preservation of connectivity. Thus“design spaces” for connectivity preserving algorithms belonging to these classes are identified. Some fundamental limitations on parallel thinning operators for images with 8-4 connectivity are also reviewed, including constraints on support size and shape. Parallel computation time issues are addressed and it is shown that existing fully parallel thinning algorithms are nearly optimally fast.
New single-pass algorithm for parallel thinning
1995, Computer Vision and Image Understanding
It is well known that many proposed parallel thinning algorithms cannot satisfy all major thinning requirements. In this paper we propose a new parallel thinning algorithm which can satisfy all major thinning requirements. The algorithm we present is a single-pass parallel thinning algorithm using reduction operators with 13-pixel support. A systematic derivation of the template set for the proposed algorithm is described. The proposed algorithm always requires a small number of iterations in thinning while at the same time it produces perfectly 8-connected medial curves. The proposed algorithm is evaluated and compared with other existing parallel thinning algorithms. It is shown from detailed experimental results that the new algorithm is superior to other algorithms in computation time and thinning results.
Structural Feature Extraction Using Multiple Bases
1995, Computer Vision and Image Understanding
The prime difficulty in research and development of the handwritten character recognition systems is in the variety of shape deformations. In particular, throughout more than a quarter of a century of research, it is found that some qualitative features such as quasi-topological features (convexity and concavity), directional features, and singular points (branch points and crossings) are effective in coping with variations of shapes. On the basis of this observation, Nishida and Mort (IEEE Trans. Pattern Anal. Mach. Intell. 14, 1992, 516-533; and Structured Document Image Analysis (H. S. Baird, H. Bunke, and K. Yamamoto, Eds.), pp. 139-187, Springer-Verlag, New York, 1992) proposed a method for structural description of character shapes by few components with rich features. This method is clear and rigorous, can cope with various deformations, and has been shown to be powerful in practice. Furthermore, shape prototypes (structural models) can be constructed automatically from the training data (Nishida and Mori, IEEE Trans. Pattern Anal. Mach. Intell. 15, 1993, 1298-1311). However, in the analysis of directional features, the number of directions is fixed to 4, and more directions such as 8 or 16 cannot be dealt with. For various applications of Nishida and Mori's method, we present a method for structural analysis and description of simple arcs or closed curves based on 2^m-directional features (m = 2, 3, 4, ...) and convex/concave features. On the other band, software OCR systems without specialized hardware have attracted much attention recently. Based on the proposed method of structural analysis and description, we describe a software implementation of a handwritten character recognition system using multistage strategy.

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Binary picture thinning by an iterative parallel two-subcycle operation

Abstract

Inf. Control

Pattern Recognition

Comput. Vision Graphics Image Process.

Comput. Graphics Image Process.

Pattern Recognition

Comput. Graphics Image Process.

Algorithms for image processing

J. Inf. Process. Soc. Japan

Basic of cellular logic with some applications in medical image processing

Some parallel thinning algorithms for digital pictures

J. Ass. comput. Mach.

Thinning algorithms on rectangular, hexagonal, and triangular arrays

Comm. Ass. comput. Mach.

A fast parallel algorithm for thinning digital patterns

Comm. Ass. comput. Mach.

A comparison of line thinning algorithm from digital geometry viewpoint

From local maxima to connected skeletons

IEEE Trans. Pattern Anal. Mach. Intell.

An asynchronous thinning algorithm

Comput. Graphics Image Process.