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
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Detection Method ◽  
Feedforward Neural Network ◽  
Edge Detection Method ◽  
Grayscale Images

A Pedestrian Recognition and Locating Method under the Condition of Surveillance

2014 ◽  
Vol 602-605 ◽  
pp. 1666-1669
Author(s):  
Xiao Qing Wu ◽  
Xiang Long ◽  
Xiong Yang
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Detection Method ◽  
Image Sequence ◽  
Selection Method ◽  
Forward Selection ◽  
Scatter Matrix ◽  
Som Neural Network ◽  
Edge Detection Method ◽  
Sequential Forward Selection

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.

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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