Automatic license plate detection using vertical edge detection method

2015 ◽  
Author(s):  
Amritha Mary Davis ◽  
C. Arunvinodh ◽  
N.P. Arathy Menon
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
License Plate ◽  
Vertical Edge ◽  
License Plate Detection ◽  
Edge Detection Method

scholarly journals Vertical-Edge-Based Car-License-Plate Detection Method

2013 ◽  
Vol 62 (1) ◽  
pp. 26-38 ◽  
Author(s):  
Abbas M. Al-Ghaili ◽  
Syamsiah Mashohor ◽  
Abdul Rahman Ramli ◽  
Alyani Ismail
Keyword(s):  
Detection Method ◽  
License Plate ◽  
Vertical Edge ◽  
License Plate Detection ◽  
Edge Based

scholarly journals Vertical Edge Detection-Based Automatic Optical Inspection of HGA Solder Jet Ball Joint Defects

1970 ◽  
Vol 9 (2) ◽  
pp. 173-181
Author(s):  
Jirarat Ieamsaard ◽  
Suchart Yammen ◽  
Paisarn Muneesawang ◽  
Frode Eika Sandnes
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Disk Drive ◽  
Detection Methods ◽  
Vertical Edge ◽  
Ball Joint ◽  
Optical Inspection ◽  
Head Gimbal Assembly ◽  
The Media ◽  
Edge Detection Method

The Head Gimbal Assembly (HGA) is an essential hard disk drive (HDD) component allowing data to be read from and written to the media. Defects on the HGA may affect the data read/write process and reduce the quality of the HDD. Therefore, HGA inspection needs to be improved during HDD manufacturing. This paper describes an image processing method that automate the optical inspection of HGA solder jet ball joint defects. Vertical edge detection methods are proposed for identifying defects. The performance of the vertical edge detection method is compared to a Sobel-based method, Roberts’ method and a Prewitt’s method. The methods were tested with 18,123 HGA images. The experimental results show that the vertical edge detection method outperforms the other methods, which had an accuracy of 99.3%, as compared to the Sobel based method, with an accuracy of 80% and 78.2 for Roberts’ method and 65.9 for Prewitt’s method.

Research and Improvement on Fast Location Algorithm in License Plate

2014 ◽  
Vol 543-547 ◽  
pp. 2792-2795
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Edge Detection ◽  
Partial Derivative ◽  
High Frequency ◽  
Detection Method ◽  
Recognition System ◽  
License Plate ◽  
The Third ◽  
Edge Points ◽  
Image Edge ◽  
Edge Detection Method

In the recognition system of license plate, the detection effect is often influenced by the speed of vehicle, the weather and illumination condition. However, the image edge is less influenced by the above conditions, so it gets more and more attention by using edge detection method to detect license plate. In this paper, three kinds of edge detection method based on partial derivative are compared. Firstly, using the first derivative to get the point set of gray step is discussed and thus the edge is obtained. However, this methods' result is largely influenced by noise. Secondly, adopting denosing theory and second partial derivative to acquire the image edge is represented, but the result shows that this method would filter out some high frequency edges and lead to the edge loss. Finally, the improved algorithm that is the fusion of three aspects: denosing theory, the second partial derivative and linking isolated edge points, is put forward. The result shows that the third algorithm has strong ability to restrain noise. However, at the same time it would smooth some high frequency edges out and lead to the edge loss. However, the third method finally makes isolated points link together, which ensure the integrity of the edge. Therefore, the result obtained by the second partial algorithm is better than the results by the two previous algorithms.

scholarly journals Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

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