The study of a new image thinning algorithm

2014 ◽  
Author(s):  
Wei Wang ◽  
Mingyi Duan ◽  
Yajun Yang
Keyword(s):  
Thinning Algorithm ◽  
Image Thinning

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.

scholarly journals Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique

2021 ◽  
Vol 3 (3) ◽  
pp. 243-258
Author(s):  
A Sathesh ◽  
Edriss Eisa Babikir Adam
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
Binary Image ◽  
Topological Properties ◽  
Image Processing Algorithm ◽  
Thinning Algorithm ◽  
Binary Images ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Image Thinning

Image thinning is the most essential pre-processing technique that plays major role in image processing applications such as image analysis and pattern recognition. It is a process that reduces a thick binary image into thin skeleton. In the present paper we have used hybrid parallel thinning algorithm to obtain the skeleton of the binary image. The result skeleton contains one pixel width which preserves the topological properties and retains the connectivity.

scholarly journals ALGORITMA IMAGE THINNING

2009 ◽  
Vol 1 (1) ◽  
pp. 29-37
Author(s):  
Zurnawita Zurnawita ◽  
Zulharbi Suar
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Thinning Algorithm ◽  
Recognition Process ◽  
Image Thinning

Using image thinning algorithm, various example of application is processing image to become more and more variate and helpfully for recognition process an image such as were studied  by  example. First, this handout will be explained  some term which concerning image thinning in digital image processing, then definition from image thinning and algorithm to process image thinning at one particular digital image will be explained. In the  next will be given an image thinning application which used for Extraction of Filarial Worms.

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.

scholarly journals Detection method for auto guide vehicle’s walking deviation based on image thinning and Hough transform

2019 ◽  
Vol 52 (3-4) ◽  
pp. 252-261 ◽  
Author(s):  
Xiaohua Cao ◽  
Daofan Liu ◽  
Xiaoyu Ren
Keyword(s):  
Hough Transform ◽  
Detection Method ◽  
Detection Algorithm ◽  
Visual Navigation ◽  
Guide Line ◽  
Actual Environment ◽  
Lane Line ◽  
Image Thinning ◽  
High Level ◽  
Refinement Algorithm

Auto guide vehicle’s position deviation always appears in its walking process. Current edge approaches applied in the visual navigation field are difficult to meet the high-level requirements of complex environment in factories since they are easy to be affected by noise, which results in low measurement accuracy and unsteadiness. In order to avoid the defects of edge detection algorithm, an improved detection method based on image thinning and Hough transform is proposed to solve the problem of auto guide vehicle’s walking deviation. First, the image of lane line is preprocessed with gray processing, threshold segmentation, and mathematical morphology, and then, the refinement algorithm is employed to obtain the skeleton of the lane line, combined with Hough detection and line fitting, the equation of the guide line is generated, and finally, the value of auto guide vehicle’s walking deviation can be calculated. The experimental results show that the methodology we proposed can deal with non-ideal factors of the actual environment such as bright area, path breaks, and clutters on road, and extract the parameters of the guide line effectively, after which the value of auto guide vehicle’s walking deviation is obtained. This method is proved to be feasible for auto guide vehicle in indoor environment for visual navigation.

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