Article

A novel single-pass thinning algorithm and an effective set of performance criteria

Intelligent Systems Laboratory, School of Applied Science, Nanyang Technological University, Nanyang Avenue, Singapore 2263, Singapore
Pattern Recognition Letters (Impact Factor: 1.55). 12/1995; 16(12):1267-1275. DOI: 10.1016/0167-8655(95)00078-X
Source: DBLP

ABSTRACT

A new sequential thinning algorithm, which uses both flag map and bitmap simultaneously to decide if a boundary pixel can be deleted, as well as the incorporation of smoothing templates to smooth the final skeleton, is proposed in this paper. Three performance measurements are proposed for an objective evaluation of this novel algorithm against a set of well established techniques. Extensive result comparison and analysis are presented in this paper for discussion.

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    • "There are number of performance measures on the basis of which we can measure various skeletonization algorithms. Some of them are described below: 1. Connectivity Measurement CM [6]: It is used to measure the connectivity in the skeletons that are produced as outputs. This is given by: Where CN is defined as current neighborhood function and is defined as follows: "
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    • "he binary images by using two pulse coupled Neural networks and claimed that in future the applications of their algorithm will be studied. They used filling technique to fill the region for the obtaining of the inner and outer image. The firing step obtains the thinned image which is decided and the final thinned image or the process is repeated.(Zhou et al. (1995) presented a novel thinning algorithm based on single pass. Bitmap and flag map are used concurrently for the decision of pixel to be deleted. Problems in existing algorithms and their solutions are presented and a smoothing template is also proposed for the smoothing of thinned image.Chiu and Tseng (1997) presents a handwritten Chinese "
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    • "re the line segments and the reflex vertices , see Veltkamp et al. (2004). Ogniewicz et al. (1992 also presented skeletonization algorithms through Voronoi poly- gons. Talbot (1992) discussed the skeletonization through the grassfire algorithm (all connected locations of the meet points of propagated firefronts) which is based on Euclidean metrics. Zhou et al. (1995) implemented a refined thinning algorithm to derive skeletons from binary im- ages. Ivanov et. al. (2000) focused on the problem of the medial axis transform through integer-based programming. Haunert (2008) and Gold et. al. (2003) specifically described the application of skeletons within map generalization to collapse elongated feature"
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