Developable Approximation via Gauss Image Thinning

2021 ◽  
Vol 40 (5) ◽  
pp. 289-300
Author(s):  
Alexandre Binninger ◽  
Floor Verhoeven ◽  
Philipp Herholz ◽  
Olga Sorkine‐Hornung
Keyword(s):  
Gauss Image ◽  
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 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.

Image thinning

2000 ◽  
pp. 199-221
Author(s):  
Stéphane Marchand-Maillet ◽  
Yazid M. Sharaiha
Keyword(s):  
Image Thinning

scholarly journals The Gauss Image Problem

2020 ◽  
Vol 73 (7) ◽  
pp. 1406-1452
Author(s):  
Károly J. Böröczky ◽  
Erwin Lutwak ◽  
Deane Yang ◽  
Gaoyong Zhang ◽  
Yiming Zhao
Keyword(s):  
Gauss Image
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