scholarly journals An Multi-scale Edge Detection Approach

2012 ◽  
Vol 25 ◽  
pp. 1616-1620 ◽  
Author(s):  
Chen Zhigang ◽  
Cui Yueli ◽  
Chen Aihua
Keyword(s):  
Edge Detection ◽  
Multi Scale ◽  
Detection Approach

scholarly journals Correct and Crisp Edge Detection Approach based on Dense Network

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xiaohua Li ◽  
Guangxiao Niu ◽  
Xiangxiang Li ◽  
Xiaoli Wang ◽  
Gang Shi
Keyword(s):  
Edge Detection ◽  
Dense Network ◽  
Detection Approach

scholarly journals A Two-Step Phenotypic Parameter Measurement Strategy for Overlapped Grapes under Different Light Conditions

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4532
Author(s):  
Yubin Miao ◽  
Leilei Huang ◽  
Shu Zhang
Keyword(s):  
Edge Detection ◽  
Parameter Measurement ◽  
Average Error ◽  
Projection Area ◽  
Shape Fitting ◽  
Multi Scale ◽  
Image Pyramids ◽  
Phenotypic Parameter ◽  
Contour Fitting ◽  
Image Edge

Phenotypic characteristics of fruit particles, such as projection area, can reflect the growth status and physiological changes of grapes. However, complex backgrounds and overlaps always constrain accurate grape border recognition and detection of fruit particles. Therefore, this paper proposes a two-step phenotypic parameter measurement to calculate areas of overlapped grape particles. These two steps contain particle edge detection and contour fitting. For particle edge detection, an improved HED network is introduced. It makes full use of outputs of each convolutional layer, introduces Dice coefficients to original weighted cross-entropy loss function, and applies image pyramids to achieve multi-scale image edge detection. For contour fitting, an iterative least squares ellipse fitting and region growth algorithm is proposed to calculate the area of grapes. Experiments showed that in the edge detection step, compared with current prevalent methods including Canny, HED, and DeepEdge, the improved HED was able to extract the edges of detected fruit particles more clearly, accurately, and efficiently. It could also detect overlapping grape contours more completely. In the shape-fitting step, our method achieved an average error of 1.5% in grape area estimation. Therefore, this study provides convenient means and measures for extraction of grape phenotype characteristics and the grape growth law.

An Improved Multi-scale Probabilistic Edge Detection for Urban remote sensing images

2021 ◽  
Author(s):  
Xuyang Teng ◽  
Yiming Pan ◽  
Meilin He ◽  
Meihua Bi ◽  
Zhaoyang Qiu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Remote Sensing Images ◽  
Multi Scale ◽  
Urban Remote Sensing

scholarly journals An improved edge detection approach and its application in defect detection

2017 ◽  
Vol 244 ◽  
pp. 012017 ◽  
Author(s):  
Vilas H Gaidhane ◽  
Navdeep ◽  
Asha Rani ◽  
Vijander Singh
Keyword(s):  
Edge Detection ◽  
Defect Detection ◽  
Detection Approach

Improved multi-scale wavelet in pantograph slide edge detection

Optik ◽  
2014 ◽  
Vol 125 (19) ◽  
pp. 5681-5683 ◽  
Author(s):  
Hui Zhang ◽  
Lin Luo ◽  
Kai Yang ◽  
Li Wang ◽  
Xiaorong Gao
Keyword(s):  
Edge Detection ◽  
Multi Scale
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