scholarly journals A method of workpiece location based on improved generalized Hough transform

2021 ◽  
Vol 1939 (1) ◽  
pp. 012079
Author(s):  
Zhang Yongfei ◽  
Zhang Tong
Author(s):  
ZHI-YONG LIU ◽  
HONG QIAO ◽  
LEI XU

By minimizing the mean square reconstruction error, multisets mixture learning (MML) provides a general approach for object detection in image. To calculate each sample reconstruction error, as the object template is represented by a set of contour points, the MML needs to inefficiently enumerate the distances between the sample and all the contour points. In this paper, we develop the line segment approximation (LSA) algorithm to calculate the reconstruction error, which is shown theoretically and experimentally to be more efficient than the enumeration method. It is also experimentally illustrated that the MML based algorithm has a better noise resistance ability than the generalized Hough transform (GHT) based counterpart.


Author(s):  
Sergio Rubén Geninatti ◽  
José Ignacio Benavides Benítez ◽  
Manuel Hernández Calviño ◽  
Nicolás Guil Mata ◽  
Juan Gómez Luna

2021 ◽  
Author(s):  
Shynimol E. Thayilchira

In this project, an analysis of the faster detection of shapes using Randomized Hough Transform (RHT) was investigated. Since reduced computational complexity and time efficiency are the major concerns for complex image analysis, the focus of the research was to investigate RHT for these specific tasks. Also, a detailed analysis of probability theory associated with RHT theory was investigated as well. Thus effectiveness of RHT was proven mathematically in this project. In this project, RHT technique combined with Generalized Hough Transform (GHT) using Newton's curve fitting technique was proposed for faster detection of shapes in the Hough Domain. Finally, the image under question was enhanced using Minimum Cross-Entropy Optimization to further enhance the image and then RGHT process was carried out. This helped the RGHT process to obtain the required time efficiency.


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