The Minimum Perimeter Convex Hull of a Given Set of Disjoint Segments

An Efficient Algorithm for Finding the Minimum Perimeter Convex Hull of Disjoint Segments

Proceedings of the 2nd International Conference on Computer Science and Application Engineering - CSAE '18 ◽

10.1145/3207677.3277948 ◽

2018 ◽

Author(s):

Nan Li ◽

Bo Jiang ◽

Nannan Li

Keyword(s):

Convex Hull ◽

Efficient Algorithm ◽

Minimum Perimeter

MINIMUM POLYGON TRANSVERSALS OF LINE SEGMENTS

International Journal of Computational Geometry & Applications ◽

10.1142/s0218195995000143 ◽

1995 ◽

Vol 05 (03) ◽

pp. 243-256 ◽

Cited By ~ 22

Author(s):

DAVID RAPPAPORT

Keyword(s):

Convex Hull ◽

General Problem ◽

Line Segment ◽

Simple Polygon ◽

Fixed Number ◽

Line Segments ◽

Geometric Objects ◽

Finite Set ◽

Minimum Perimeter ◽

Set Of Points

Let S be used to denote a finite set of planar geometric objects. Define a polygon transversal of S as a closed simple polygon that simultaneously intersects every object in S, and a minimum polygon transversal of S as a polygon transversal of S with minimum perimeter. If S is a set of points then the minimum polygon transversal of S is the convex hull of S. However, when the objects in S have some dimension then the minimum polygon transversal and the convex hull may no longer coincide. We consider the case where S is a set of line segments. If the line segments are constrained to lie in a fixed number of orientations we show that a minimum polygon transversal can be found in O(n log n) time. More explicitely, if m denotes the number of line segment orientations, then the complexity of the algorithm is given by O(3mn+log n). The general problem for line segments is not known to be polynomial nor is it known to be NP-hard.

Minimum perimeter convex hull of imprecise points in convex regions

Proceedings of the 27th annual ACM symposium on Computational geometry - SoCG '11 ◽

10.1145/1998196.1998243 ◽

2011 ◽

Author(s):

Christophe Weibel ◽

Linqiao Zhang

Keyword(s):

Convex Hull ◽

Minimum Perimeter

Convex hull of chain-coded blob

IEE Proceedings E Computers and Digital Techniques ◽

10.1049/ip-e.1989.0072 ◽

1989 ◽

Vol 136 (6) ◽

pp. 530

Author(s):

G.R. Wilson ◽

B.G. Batchelor

Keyword(s):

Convex Hull

An Efficient Convex Hull Algorithm for Planar Point Set Based on Recursive Method

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2012.01375 ◽

2012 ◽

Vol 38 (8) ◽

pp. 1375 ◽

Cited By ~ 3

Author(s):

Bin LIU ◽

Tao WANG

Keyword(s):

Convex Hull ◽

Recursive Method ◽

Planar Point ◽

Point Set ◽

Convex Hull Algorithm

Saliency Detection Based on Improved Manifold Ranking via Convex Hull

Journal of Computer-Aided Design & Computer Graphics ◽

10.3724/sp.j.1089.2019.17376 ◽

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

On the Convex Hull of the Union of Certain Polyhedra

10.21236/ada197982 ◽

1988 ◽

Author(s):

Egon Balas

Keyword(s):

Convex Hull

Convex Hull Convolutive Non-Negative Matrix Factorization for Uncovering Temporal Patterns in Multivariate Time-Series Data

10.21437/interspeech.2016-571 ◽

2016 ◽

Cited By ~ 5

Author(s):

Colin Vaz ◽

Asterios Toutios ◽

Shrikanth S. Narayanan

Keyword(s):

Time Series ◽

Convex Hull ◽

Matrix Factorization ◽

Time Series Data ◽

Multivariate Time Series ◽

Temporal Patterns ◽

Series Data ◽

Non Negative Matrix Factorization

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

Computational Geometry ◽

10.1016/j.comgeo.2019.04.002 ◽

2019 ◽

Vol 82 ◽

pp. 16-31 ◽

Cited By ~ 2

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

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407020969502 ◽

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.