A high precision edge detection method for the blurred image in motion measurement

Author(s):  
Zhang Ying ◽  
Cai Chenguang ◽  
Liu Zhihua ◽  
Yang Ming
2014 ◽  
Vol 6 ◽  
pp. 904061 ◽  
Author(s):  
Hau-Wei Lee ◽  
Chien Hung Liu ◽  
Jenq-Shyong Chen

There are two kinds of alignment systems, marked and unmarked. The glass substrate for touch panels is categorized as an unmarked work piece. Vision based glass substrate alignment (GSA) relies on the edge of the glass. Traditional GSA systems compensate first for angular and then for linear error. This reduces alignment accuracy and increases alignment time and edge detection usually takes longer than 10 ms. This study proposes an effortless edge detection method. This method is very simple and can significantly reduce the time taken to detect the edge to about 6 ms using a 1.3 megapixel image. In this study, a floating center idea is used to control the glass substrate on a high precision coplanar XXY alignment stage. According to the method, users can set the rotation center anywhere as long as it is on the working ( xy) plane. Tolerance prognosis is also considered in this study to help the operator decide if the substrate is usable or should be rejected. The experimental results show alignment repeatability of the x, y, and θ axes to be 1 μm, 1 μm, and 5 arcsec, respectively.


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.


2014 ◽  
Vol 539 ◽  
pp. 141-145
Author(s):  
Shui Li Zhang

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.


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