A method of edge detection based on improved canny algorithm for the lidar depth image

2006 ◽  
Author(s):  
Jingzhong Xu ◽  
Youchuan Wan ◽  
Xubing Zhang
2012 ◽  
Vol 220-223 ◽  
pp. 1279-1283 ◽  
Author(s):  
Li Hong Dong ◽  
Peng Bing Zhao

The coal-rock interface recognition is one of the critical automated technologies in the fully mechanized mining face. The poor working conditions underground result in the seriously polluted edge information of the coal-rock interface, which affects the positioning precision of the shearer drum. The Gaussian filter parameters and the high-low thresholds are difficult to select in the traditional Canny algorithm, which causes the information loss of gradual edge and the phenomenon of false edge. Consequently, this paper presents an improved Canny edge detection algorithm, which adopts the adaptive median filtering algorithm to calculate the thresholds of Canny algorithm according to the grayscale mean and variance mean. This algorithm can protect the image edge details better and can restrain the blurred image edge. Experimental results show that this algorithm has improved the edge extraction effect under the case of noise interference and improved the detection precision and accuracy of the coal-rock image effectively.


Author(s):  
Poonam S. Deokar ◽  
Anagha P. Khedkar

The Edge can be defined as discontinuities in image intensity from one pixel to another. Modem image processing applications demonstrate an increasing demand for computational power and memories space. Typically, edge detection algorithms are implemented using software. With advances in Very Large Scale Integration (VLSI) technology, their hardware implementation has become an attractive alternative, especially for real-time applications. The Canny algorithm computes the higher and lower thresholds for edge detection based on the entire image statistics, which prevents the processing of blocks independent of each other. Direct implementation of the canny algorithm has high latency and cannot be employed in real-time applications. To overcome these, an adaptive threshold selection algorithm may be used, which computes the high and low threshold for each block based on the type of block and the local distribution of pixel gradients in the block. Distributed Canny Edge Detection using FPGA reduces the latency significantly; also this allows the canny edge detector to be pipelined very easily. The canny edge detection technique is discussed in this paper.


Kybernetes ◽  
2011 ◽  
Vol 40 (5/6) ◽  
pp. 883-893
Author(s):  
Tai Kuang ◽  
Qing‐Xin Zhu ◽  
Yue Sun

2013 ◽  
Vol 756-759 ◽  
pp. 3492-3496
Author(s):  
Xiang Hua Hou ◽  
Hong Hai Liu

When using international popular algorithms deal with RMB paper currency, there exist three main aspects. First, the size of RMB paper currency is different with other countries. Second, the color of RMB paper currency is different from other countries, and the different face values have different major color, pattern and texture. Third, the location of face value in paper currency of our country is different from other countries. For RMB paper currency image, using edge detection is to separate the paper currency from the image according to boundary. In other words, the background is removed and only paper currency is left. This paper firstly introduces the principles of removing background by gradient operator, LOG algorithm and Canny algorithm. Then it analyzes the edge image results obtained by these three kind algorithms. At last, these three algorithms are evaluated. Results show that the Canny algorithm can satisfy the requirement of removing background and can improve a good foundation for the subsequent of paper currency extraction.


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