scholarly journals An Edge Detection Method for Grayscale Images based on BP Feedforward Neural Network

2013 ◽  
Vol 67 (2) ◽  
pp. 22-28 ◽  
Author(s):  
Jesal Vasavada ◽  
Shamik Tiwari
2014 ◽  
Vol 602-605 ◽  
pp. 1666-1669
Author(s):  
Xiao Qing Wu ◽  
Xiang Long ◽  
Xiong Yang

In our previous work, we proposed a motion edge detection method to extract the contour of the pedestrian in an image sequence. In order to locate and recognize the pedestrian in an image after its contour was extracted, we propose the BLBP method to describe the binary texture of the contour of the pedestrian in the image, and use the BLBP histogram to get the recognition feature of the pedestrian. And then we use the scatter matrix and the sequential forward selection method to select useful features, and use the SOM neural network to perform the recognition work. At the last part of this paper, some results of our experiments are illustrated there, which shows that our method is satisfactory.


Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  

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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