Combining Convex Hull and Directed Graph for Fast and Accurate Ellipse Detection

2021 ◽  
pp. 101110
Author(s):  
Zeyu Shen ◽  
Mingyang Zhao ◽  
Xiaohong Jia ◽  
Yuan Liang ◽  
Lubin Fan ◽  
...  
Keyword(s):  
Convex Hull ◽  
Directed Graph ◽  
Ellipse Detection

Using Convex Hull for Fast and Accurate Ellipse Detection

2020 ◽  
Author(s):  
Zeyu Shen ◽  
Mingyang Zhao ◽  
Xiaohong Jia ◽  
Dong-Ming Yan
Keyword(s):  
Convex Hull ◽  
Ellipse Detection

Convex hull of chain-coded blob

1989 ◽  
Vol 136 (6) ◽  
pp. 530
Author(s):  
G.R. Wilson ◽  
B.G. Batchelor
Keyword(s):  
Convex Hull

Method based on directed graph and binary decision diagram to break logic loops

Kerntechnik ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. 488-499
Author(s):  
S. Yuan ◽  
M. Huifang
Keyword(s):  
Directed Graph ◽  
Binary Decision Diagram ◽  
Binary Decision

Design and application of workflow modeling based on extended-directed graph

2009 ◽  
Vol 28 (9) ◽  
pp. 2437-2439 ◽  
Author(s):  
Lin LI ◽  
Zheng-yi CHAI ◽  
Li ZHANG
Keyword(s):  
Directed Graph ◽  
Workflow Modeling ◽  
Design And Application

Saliency Detection Based on Improved Manifold Ranking via Convex Hull

2019 ◽  
Vol 31 (5) ◽  
pp. 761
Author(s):  
Xiao Lin ◽  
Zuxiang Liu ◽  
Xiaomei Zheng ◽  
Jifeng Huang ◽  
Lizhuang Ma
Keyword(s):  
Convex Hull ◽  
Saliency Detection ◽  
Manifold Ranking

scholarly journals On the expected diameter, width, and complexity of a stochastic convex hull

2019 ◽  
Vol 82 ◽  
pp. 16-31 ◽  
Author(s):  
Jie Xue ◽  
Yuan Li ◽  
Ravi Janardan
Keyword(s):  
Convex Hull

Spherical object recognition based on the number of contour edges extracted by fitting and convex hull processing

2020 ◽  
pp. 095440702096950
Author(s):  
Bochang Zou ◽  
Huadong Qiu ◽  
Yufeng Lu
Keyword(s):  
Convex Hull ◽  
Target Object ◽  
Identification Accuracy ◽  
Detection Accuracy ◽  
Morphological Operations ◽  
Circle Detection ◽  
Spherical Object ◽  
Single Feature ◽  
Contour Fitting ◽  
Rgb Images

The detection of spherical targets in workpiece shape clustering and fruit classification tasks is challenging. Spherical targets produce low detection accuracy in complex fields, and single-feature processing cannot accurately recognize spheres. Therefore, a novel spherical descriptor (SD) for contour fitting and convex hull processing is proposed. The SD achieves image de-noising by combining flooding processing and morphological operations. The number of polygon-fitted edges is obtained by convex hull processing based on contour extraction and fitting, and two RGB images of the same group of objects are obtained from different directions. The two fitted edges of the same target object obtained at two RGB images are extracted to form a two-dimensional array. The target object is defined as a sphere if the two values of the array are greater than a custom threshold. The first classification result is obtained by an improved K-NN algorithm. Circle detection is then performed on the results using improved Hough circle detection. We abbreviate it as a new Hough transform sphere descriptor (HSD). Experiments demonstrate that recognition of spherical objects is obtained with 98.8% accuracy. Therefore, experimental results show that our method is compared with other latest methods, HSD has higher identification accuracy than other methods.

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