@article{b9d1eb43d3fd486fad77e0bcabaa9f3e,
title = "Fully parallel thinning with tolerance to boundary noise",
abstract = "A new fully parallel thinning algorithm is developed and evaluated in this paper to solve the noise spurs problem and preserve geometric properties efficiently. The algorithm not only prevents the excessive erosions but also lessens the creation of spurious end points for an image with boundary noise. When two input images are similar in shape but with boundary noise, our skeletons produced appear more consistent in topology as compared to those using other algorithms. Although a few additional neighbors other than 3 × 3 are considered in the deletability conditions, the smoothing procedure prior to thinning is avoided. The parallel thinning algorithm runs very fast and can be implemented in real time. Several English and Chinese characters and the difficult patterns often illustrated in the literature are also experimented to show the efficiency and consistency of our algorithm.",
keywords = "Image processing, Parallel algorithm, Pattern recognition, Skeleton, Thinning",
author = "Shih, {Frank Y.} and Wong, {Wai Tak}",
note = "Funding Information: Thinning is a fundamental early processing step in pattern analysis such as industrial parts inspection, tt) fingerprint recognition/2~ optical character recognition, t3t and biomedical diagnosis, t4~ The result of thinning a binary image is called the skeleton. One advantage of thinning is the reduction of memory space required for storage of the essential structural information presented in a pattern. Moreover, it simplifies the data structure required in pattern analysis. Although there is no general agreement on the exact mathematical definition of thinness, a reasonable compromise has been reached that the generated skeleton must be essential to preserve the object's topology and to represent the pattern's shape informatively. The resulting skeleton must be also a single pixel in width and lie approximately along the medical axes of the object regions. Many thinning algorithms are available in the literature. Different algorithms produce slightly different skeletons. Usually thinning algorithms are divided into two groups: sequential and parallel/5'6) A more general classification is: sequential, parallel, hybrid, and others/7) It is generally believed that a sequential algorithm will be faster than a parallel algorithm when they are both implemented on a serial computer/5) However, the parallel thinning algorithms are best suited for parallel computers to gain efficiency. In the parallel thinning approach the new value of t This work was supported by the National Science Foundation under Grant IRI-9109138 and the New Jersey Institute of Technology under Grant 421770.",
year = "1994",
month = dec,
doi = "10.1016/0031-3203(94)90086-8",
language = "English (US)",
volume = "27",
pages = "1677--1695",
journal = "Pattern Recognition",
issn = "0031-3203",
publisher = "Elsevier Limited",
number = "12",
}