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.

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.

scholarly journals A FAST RANDOMIZED HOUGH TRANSFORM FOR CIRCLE/CIRCULAR ARC RECOGNITION

2010 ◽  
Vol 24 (03) ◽  
pp. 457-474 ◽  
Author(s):  
SHIH-HSUAN CHIU ◽  
JIUN-JIAN LIAW ◽  
KUO-HUNG LIN
Keyword(s):  
Hough Transform ◽  
Computational Time ◽  
Seed Point ◽  
Circular Arc ◽  
Randomized Hough Transform ◽  
Complex Image ◽  
And Storage ◽  
Complex Images

The main drawbacks of the Hough transform (HT) are the heavy requirement of computation and storage. To improve the drawbacks of the HT, the randomized Hough transform (RHT) was proposed. But the RHT is not suitable for detecting the pattern with the complex image because the probability is too low. In this paper, we propose a fast randomized Hough transform for circle/circular arc detection. We pick one point at random to be the seed point. Then, we propose a checking rule to confirm if the seed point is on the true circle. Compared with the previous techniques, the proposed method requires less computational time and is more suitable for complex images. In the experiments, synthetic and real images are used to show the effect of the proposed method.

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

Coarse-To-Fine 3D Randomized Hough Transform for Dim Target Detection

2014 ◽  
Vol 519-520 ◽  
pp. 1040-1045
Author(s):  
Ling Fan
Keyword(s):  
Target Detection ◽  
Hough Transform ◽  
Constant Velocity ◽  
Three Dimensional ◽  
Randomized Hough Transform ◽  
Straight Line ◽  
Memory Complexity ◽  
Coarse To Fine

This paper makes some improvements on Roberts representation for straight line in space and proposes a coarse-to-fine three-dimensional (3D) Randomized Hough Transform (RHT) for the detection of dim targets. Using range, bearing and elevation information of the received echoes, 3D RHT can detect constant velocity target in space. In addition, this paper applies a coarse-to-fine strategy to the 3D RHT, which aims to solve both the computational and memory complexity problems. The validity of the coarse-to-fine 3D RHT is verified by simulations. In comparison with the 2D case, which only uses the range-bearing information, the coarse-to-fine 3D RHT has a better practical value in dim target detection.

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