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

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.

2014 ◽  
Vol 539 ◽  
pp. 141-145
Author(s):  
Shui Li Zhang

This paper presents new theorems Stevens edge detection method based on cognitive psychology on. Firstly, based on the number of the image is decomposed into high-frequency and low-frequency information, and the high-frequency information extracted by subtracting the maximum number of images to the image after the filter, then the amount of high frequency information into psychological cognitive psychology based on Stevenss theorem. The algorithm suppression refined edge after the non-minimum, applications Pillar K-means algorithm to extract image edge. Experimental results show that: the brightness of the image is converted to the amount of psychological edge can better unify under different brightness values.


2014 ◽  
Vol 563 ◽  
pp. 203-207
Author(s):  
Kun Lin Yu ◽  
Zhi Yu Xie

According to the shortcoming of traditional Canny edge detection algorithm is sensitive to noise and low positioning accuracy, this paper proposes an algorithm of Polynomial interpolation Sub-pixel edge detection based on improved Canny operator: We first use improved Canny operator edge detection algorithm to extract rough image edge, then use the quadratic Polynomial interpolation to calculate on the rough extraction edge, finally refine the edge image. Experiments show that the improved method is better than the traditional detection method can accurately locate the image edge.


2014 ◽  
Vol 511-512 ◽  
pp. 550-553 ◽  
Author(s):  
Jian Yong Liang

Edge detection is an old and hot topic in image processing, pattern recognition and computer vision. Numerous edge detection approaches have been proposed to gray images. It is difficult to extend these approaches to color image edge detection. A novel edge detection method based on mathematical morphology for color images is proposed in this paper. The proposed approach firstly compute vector gradient based on morphological gradient operators, and then compute the optimal gradient according to structure elements with different size. Finally, we use a threshold to binary the gradient images and then obtain the edge images. Experimental results show that the proposed approach has advantages of suppressing noise and preserving edge details and it is not sensitive to noise pixel. The finally edge images via the proposed method have high PSNR and NC compared with the traditional approaches.


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