scholarly journals Automatic Detection of Regulatory Traffic Signs via Circle Detection by Post Edge Detection Applied to Straight Line Hough Transform

2020 ◽  
Vol 4 (2) ◽  
pp. 49-58
Author(s):  
Seçkin ULUSKAN
Keyword(s):  
Edge Detection ◽  
Hough Transform ◽  
Automatic Detection ◽  
Circle Detection ◽  
Traffic Signs ◽  
Straight Line

Measuring the Dimensions of Mechanical Component using Image Processing Techniques

2017 ◽  
Vol 7 (9) ◽  
pp. 65
Author(s):  
Dipti Nilesh Aswar
Keyword(s):  
Edge Detection ◽  
Hough Transform ◽  
Detection Technique ◽  
Circle Detection ◽  
Canny Edge Detection ◽  
Depth Measurement ◽  
Mechanical Component ◽  
Canny Edge ◽  
Processing Techniques ◽  
Real Time Application

This paper introduces automated process for measuring the dimensions of mechanical component. The proposed method includes image pre-processing techniques, edge detection technique, hough transform technique for circle detection and stereo vision concept is used for hole depth measurement of mechanical component. In practical, there are many factors which affects the measurement result. Noise may play key role. In order to eliminate noise effect on measurement Gaussian filtering algorithm is used. Then canny edge detection technique is used for edge detection, which helps to improve the accuracy of the result. For hole diameter measurement first we have to find out the circular shape and for circle identification we are using Hough transform technique. We are going to calculate the depth of hole by using the elevation by parallax technique. This proposed method is used for specific type of component. But in future this method can be applied for many type of real time application.

scholarly journals Automatic Circle Detection on Images Based on an Evolutionary Algorithm That Reduces the Number of Function Evaluations

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Erik Cuevas ◽  
Eduardo L. Santuario ◽  
Daniel Zaldívar ◽  
Marco Perez-Cisneros
Keyword(s):  
Evolutionary Algorithm ◽  
Hough Transform ◽  
Automatic Detection ◽  
Experimental Results ◽  
Natural Images ◽  
Evolutionary Approach ◽  
Circle Detection ◽  
Noisy Images ◽  
Accuracy Speed ◽  
Fitness Calculation

This paper presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of the conventional Hough transform principles. The proposed algorithm is based on a newly developed evolutionary algorithm called the Adaptive Population with Reduced Evaluations (APRE). Our proposed algorithm reduces the number of function evaluations through the use of two mechanisms: (1) adapting dynamically the size of the population and (2) incorporating a fitness calculation strategy, which decides whether the calculation or estimation of the new generated individuals is feasible. As a result, the approach can substantially reduce the number of function evaluations, yet preserving the good search capabilities of an evolutionary approach. Experimental results over several synthetic and natural images, with a varying range of complexity, validate the efficiency of the proposed technique with regard to accuracy, speed, and robustness.

scholarly journals Fractional Order Processing of Satellite Images

2021 ◽  
Vol 11 (11) ◽  
pp. 5288
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Rui Melicio
Keyword(s):  
Edge Detection ◽  
Fractional Order ◽  
High Performance ◽  
Satellite Images ◽  
Automatic Detection ◽  
Automatic Processing ◽  
Order Processing ◽  
Detection Algorithms ◽  
Grey Scale ◽  
Fixed Set

Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance.

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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