A New Edge Detection Method with an Excellent Anti-noise Property Based on High-dimensional Wavelet Transform

2016 ◽  
Author(s):  
Q.Z. Wang ◽  
J.M. Zhang
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Detection Method ◽  
High Dimensional ◽  
Noise Property ◽  
Edge Detection Method

scholarly journals An Edge Detection Method Based on Wavelet Transform at Arbitrary Angles

2018 ◽  
Vol E101.D (9) ◽  
pp. 2392-2400 ◽  
Author(s):  
Su LIU ◽  
Xingguang GENG ◽  
Yitao ZHANG ◽  
Shaolong ZHANG ◽  
Jun ZHANG ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Detection Method ◽  
Edge Detection Method

Study of Edge Detection of Printed Circuit Board Photoelectric Image Based on Multi-Scale Wavelet Transform

2012 ◽  
Vol 542-543 ◽  
pp. 850-853
Author(s):  
Nao Sheng Qiao ◽  
Jing Tang
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Basic Principle ◽  
Printed Circuit Board ◽  
Detection Method ◽  
Circuit Board ◽  
Theory Analysis ◽  
Printed Circuit ◽  
Multi Scale ◽  
Edge Detection Method

Aiming at the dark printed circuit board photoelectric image that contains noise acquired by CCD system, the edge detection method by using multi-scale wavelet transform is proposed. Firstly, its basic principle is analyzed in detail, and the simple physics explanation and formula deduction are given. Secondly, the idiographic detection steps are given. Finally, the results of experiment verify the correctness of theory analysis.

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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