A New Edge Detection Method for Seismic Fault Image

2011 ◽  
Vol 250-253 ◽  
pp. 3953-3957
Author(s):  
Yan Xing Song ◽  
Zhen Jing Yao ◽  
Jing Song Yang ◽  
Qing Bin Tong
Keyword(s):  
Edge Detection ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Haar Wavelet ◽  
Seismic Fault ◽  
Edge Detection Method

A new edge detection method for seismic fault image was presented. The method decomposed an image by morphology Haar wavelet, then used morphology edge detection operator to detect edge of the low-frequency morphological wavelet level, at last, the low-frequency and high-frequency coefficient are synthesized to realize the edge detection. Extensive experiments have shown that the method proposed in this paper can detect the edge of the seismic fault accurately with simple operations, and it is very competitive compared with other methods.

Research on Improvement Algorithm of Image Edge Detection

2014 ◽  
Vol 539 ◽  
pp. 141-145
Author(s):  
Shui Li Zhang
Keyword(s):  
Cognitive Psychology ◽  
Edge Detection ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Frequency Information ◽  
Image Edge Detection ◽  
Improvement Algorithm ◽  
Image Edge ◽  
Edge Detection Method

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.

Subpixel edge detection method based on low-frequency filtering

2016 ◽  
Author(s):  
Yosip Y. Bylinsky ◽  
Andrzej Kotyra ◽  
Konrad Gromaszek ◽  
Aigul Iskakova
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Low Frequency ◽  
Frequency Filtering ◽  
Edge Detection Method

Robust Edge Detection Based on Anisotropic Mathematical Morphology and Scale Multiplication in NSCT Domain

2014 ◽  
Vol 595 ◽  
pp. 289-294
Author(s):  
Yi Mian Dai ◽  
Yi Quan Wu
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Component ◽  
Superior Performance ◽  
Great Ability ◽  
Morphological Gradient ◽  
Frequency Components ◽  
Edge Detection Method

A novel edge detection method based on anisotropic mathematical morphology and scale multiplication in nonsubsampled contourlet transform (NSCT) domain is proposed to obtain a superior and robust performance under heavy noise. One preliminary result is obtained using anisotropic morphological gradient of the low-frequency component, yielding a single-pixel response with few pseudo edges. Due to the great ability of NSCT to localize distributed discontinuities such as edges, scale multiplication results of high-frequency components can get rid of a large amount of noise and produce well-localized edge candidates. The final result is a fusion of the detection results of low-frequency component and high-frequency components. Detailed experiments compared with other state-of-the-art methods demonstrate that the proposed method has a superior performance of edge detection and is quite robust even under heavy noise.

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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