A Sequential Thinning Algorithm For Image Skeletonization

1984 ◽  
Author(s):  
Chung C. Lee
Keyword(s):  
Thinning Algorithm ◽  
Sequential Thinning

A Width-Independent Sequential Thinning Algorithm

1997 ◽  
Vol 11 (03) ◽  
pp. 393-403 ◽  
Author(s):  
Prabir Bhattacharya ◽  
Xun Lu
Keyword(s):  
Topological Properties ◽  
Neighbor Distance ◽  
Distance Transformation ◽  
Thinning Algorithm ◽  
Binary Images ◽  
Standard Algorithm ◽  
Run Time ◽  
Sequential Thinning

We propose a new sequential width-independent thinning algorithm for binary images. The algorithm uses a 4-neighbor distance transformation, and is designed to preserve the topological properties and shape of the object to be thinned. Comparison of the run time with a standard algorithm is given.

scholarly journals Distance-Based Skeletonization on the BCC Grid

2021 ◽  
Author(s):  
Gábor Karai ◽  
Péter Kardos
Keyword(s):  
The Body ◽  
Body Centered Cubic ◽  
Thinning Algorithm ◽  
Sequential Thinning ◽  
Curve Skeletons ◽  
Novel Algorithm

Strand proposed a distance-based thinning algorithm for computing surface skeletons on the body-centered cubic (BCC) grid. In this paper, we present two modified versions of this algorithm that are faster than the original one, and less sensitive to the visiting order of points in the sequential thinning phase. In addition, a novel algorithm capable of producing curve skeletons is also reported.

Evaluation of the effectiveness of different image skeletonization methods in biometric security systems

2020 ◽  
Vol 10 ◽  
Author(s):  
Mariya Nazarkevych ◽  
Serhii Dmytruk ◽  
Volodymyr Hrytsyk ◽  
Olha Vozna ◽  
Anzhela Kuza ◽  
...  
Keyword(s):  
Experimental Studies ◽  
Fingerprint Recognition ◽  
Good Time ◽  
Gabor Filtering ◽  
Security Systems ◽  
Thinning Algorithm ◽  
Biometric Security ◽  
Recognition Quality ◽  
Control And Management ◽  
The Internet Of Things

Background: Systems of the Internet of Things are actively implementing biometric systems. For fast and high-quality recognition in sensory biometric control and management systems, skeletonization methods are used at the stage of fingerprint recognition. The analysis of the known skeletonization methods of Zhang-Suen, Hilditch, Ateb-Gabor with the wave skeletonization method has been carried out and it shows a good time and qualitative recognition results. Methods: The methods of Zhang-Suen, Hildich and thinning algorithm based on Ateb-Gabor filtration, which form the skeletons of biometric fingerprint images, are considered. The proposed thinning algorithm based on Ateb-Gabor filtration showed better efficiency because it is based on the best type of filtering, which is both a combination of the classic Gabor function and the harmonic Ateb function. The combination of this type of filtration makes it possible to more accurately form the surroundings where the skeleton is formed. Results: Along with the known ones, a new Ateb-Gabor filtering algorithm with the wave skeletonization method has been developed, the recognition results of which have better quality, which allows to increase the recognition quality from 3 to 10%. Conclusion: The Zhang-Suen algorithm is a 2-way algorithm, so for each iteration, it performs two sets of checks during which pixels are removed from the image. Zhang-Suen's algorithm works on a plot of black pixels with eight neighbors. This means that the pixels found along the edges of the image are not analyzed. Hilditch thinning algorithm occurs in several passages, where the algorithm checks all pixels and decides whether to replace a pixel from black to white if certain conditions are satisfied. This Ateb-Gabor filtering will provide better performance, as it allows to obtain more hollow shapes, organize a larger range of curves. Numerous experimental studies confirm the effectiveness of the proposed method.

Parallel Improvement for a Classical Template Serial Thinning Algorithm

2011 ◽  
Vol 271-273 ◽  
pp. 1509-1513 ◽  
Author(s):  
Mei Xiu Lu ◽  
Fu Rong Wang ◽  
Feng Li
Keyword(s):  
Mathematical Morphology ◽  
Fingerprint Recognition ◽  
Thinning Algorithm ◽  
Recognition Algorithms ◽  
Characteristic Points ◽  
Image Thinning ◽  
Ideal Algorithm ◽  
Improved Algorithm

Image thinning is one of important steps of fingerprint preprocessing. Most of fingerprint recognition algorithms checked the characteristic points on thinning image. In this paper, we discover some shortages in OPTA and mathematical morphology thinning algorithm and find out the reasons for some shortages such as many glitches and snags, defective thinning, and so on. A new improved algorithm is proposed in the paper, which is an ideal algorithm because it is faster, produces less glitch, and thins completely.

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