Discriminative generalized Hough transform for object localization in medical images

2013 ◽  
Vol 8 (4) ◽  
pp. 593-606 ◽  
Author(s):  
Heike Ruppertshofen ◽  
Cristian Lorenz ◽  
Georg Rose ◽  
Hauke Schramm
Keyword(s):  
Hough Transform ◽  
Medical Images ◽  
Object Localization ◽  
Generalized Hough Transform

INVESTIGATION ON MULTISETS MIXTURE LEARNING BASED OBJECT DETECTION

2007 ◽  
Vol 21 (08) ◽  
pp. 1339-1351 ◽  
Author(s):  
ZHI-YONG LIU ◽  
HONG QIAO ◽  
LEI XU
Keyword(s):  
Object Detection ◽  
Hough Transform ◽  
Line Segment ◽  
Reconstruction Error ◽  
Mean Square ◽  
Noise Resistance ◽  
Generalized Hough Transform ◽  
The Mean ◽  
Sample Reconstruction

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.

FPGA Implementation of the Generalized Hough Transform

2009 ◽  
Author(s):  
Sergio Rubén Geninatti ◽  
José Ignacio Benavides Benítez ◽  
Manuel Hernández Calviño ◽  
Nicolás Guil Mata ◽  
Juan Gómez Luna
Keyword(s):  
Hough Transform ◽  
Fpga Implementation ◽  
Generalized Hough Transform

scholarly journals Faster and optimal detection of parametric shapes

2021 ◽  
Author(s):  
Shynimol E. Thayilchira
Keyword(s):  
Probability Theory ◽  
Image Analysis ◽  
Computational Complexity ◽  
Hough Transform ◽  
Cross Entropy ◽  
Optimal Detection ◽  
Time Efficiency ◽  
Generalized Hough Transform ◽  
Randomized Hough Transform ◽  
Complex Image

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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