How to compute the Voronoi diagram of line segments: Theoretical and experimental results

1994 ◽  
pp. 227-239 ◽  
Author(s):  
Christoph Burnikel ◽  
Kurt Mehlhorn ◽  
Stefan Schirra
Keyword(s):  
Voronoi Diagram ◽  
Experimental Results ◽  
Line Segments

A Duplicate Chinese Document Image Retrieval System Based on Line Segment Feature in Character Image Block

2011 ◽  
pp. 14-23
Author(s):  
Yung-Kuan Chan ◽  
Tung-Shou Chen ◽  
Yu-An Ho
Keyword(s):  
Image Retrieval ◽  
Line Segment ◽  
Document Image ◽  
Experimental Results ◽  
Document Images ◽  
Rapid Progress ◽  
Image Block ◽  
Line Segments ◽  
Image Retrieval System ◽  
On Line

With the rapid progress of digital image technology, the management of duplicate document images is also emphasized widely. As a result, this paper suggests a duplicate Chinese document image retrieval (DCDIR) system, which uses the ratio of the number of black pixels to that of white pixels on the scanned line segments in a character image block as the feature of the character image block. Experimental results indicate that the system can indeed effectively and quickly retrieve the desired duplicate Chinese document image from a database.

scholarly journals The Higher-Order Voronoi Diagram of Line Segments

Algorithmica ◽  
2014 ◽  
Vol 74 (1) ◽  
pp. 415-439 ◽  
Author(s):  
Evanthia Papadopoulou ◽  
Maksym Zavershynskyi
Keyword(s):  
Voronoi Diagram ◽  
Higher Order ◽  
Line Segments

Fast Depth Generating Algorithm From Binocular Images

2013 ◽  
Vol 284-287 ◽  
pp. 3102-3105
Author(s):  
Fang Hsuan Cheng ◽  
Tzu Hao Kuo
Keyword(s):  
Edge Detection ◽  
Line Segment ◽  
Stereo Matching ◽  
Color Difference ◽  
Experimental Results ◽  
Reference Image ◽  
Depth Information ◽  
High Quality ◽  
Line Segments

This paper proposes a line segment method to estimate the depth information from a pair of rectified images. The proposed method can achieve fast and high quality stereo-matching. This method first uses a simple edge detection to find out the line segments in the reference image and then calculates the color difference of each line segment from binocular images. The last step is to find out the minimum difference of each line segment as the corresponding points. From the experimental results, it is proved that the proposed method can fast and accurately generate the depth information from binocular images.

scholarly journals A Line Matching Method Based on Multiple Intensity Ordering with Uniformly Spaced Sampling

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1639 ◽  
Author(s):  
Jing Xing ◽  
Zhenzhong Wei ◽  
Guangjun Zhang
Keyword(s):  
Experimental Results ◽  
Superior Performance ◽  
Feature Descriptor ◽  
Image Pyramid ◽  
Matching Method ◽  
Concentric Ring ◽  
Line Segments ◽  
Illumination Changes ◽  
Public Datasets ◽  
Local Feature Descriptor

This paper presents a line matching method based on multiple intensity ordering with uniformly spaced sampling. Line segments are extracted from the image pyramid, with the aim of adapting scale changes and addressing fragmentation problem. The neighborhood of line segments was divided into sub-regions adaptively according to intensity order to overcome the difficulty brought by various line lengths. An intensity-based local feature descriptor was introduced by constructing multiple concentric ring-shaped structures. The dimension of the descriptor was reduced significantly by uniformly spaced sampling and dividing sample points into several point sets while improving the discriminability. The performance of the proposed method was tested on public datasets which cover various scenarios and compared with another two well-known line matching algorithms. The experimental results show that our method achieves superior performance dealing with various image deformations, especially scale changes and large illumination changes, and provides much more reliable correspondences.

Dynamic Construction of Voronoi Diagram for a Set of Points and Straight Line Segments

2014 ◽  
Vol 533 ◽  
pp. 264-267
Author(s):  
Xin Liu
Keyword(s):  
Production Process ◽  
Voronoi Diagram ◽  
Voronoi Diagrams ◽  
High Potential ◽  
Line Segments ◽  
Potential Value ◽  
Straight Line ◽  
Traditional Algorithm ◽  
Set Of Points

Voronoi Diagram for a set of points and straight line segments is difficult to construct because general figures have uncertain shapes[. In traditional algorithm, when generator of general figure changes, production process will be extremely complex because of the change of regions neighboring with those generator changed. In this paper, we use dynamicconstruction of Voronoi diagrams. The algorithm can get over all kinds of shortcomings that we have just mentioned. So it is more useful and effective than the traditional algorithm[2]. The results show that the algorithm is both simple and useful, and it is of high potential value in practice.

Constructing the Voronoi diagram of a set of line segments in parallel

Algorithmica ◽  
1993 ◽  
Vol 9 (2) ◽  
pp. 128-141 ◽  
Author(s):  
Michael T. Goodrich ◽  
Colm �'D�nlaing ◽  
Chee K. Yap
Keyword(s):  
Voronoi Diagram ◽  
Line Segments

scholarly journals CONSTRUCTING THE CITY VORONOI DIAGRAM FASTER

2008 ◽  
Vol 18 (04) ◽  
pp. 275-294 ◽  
Author(s):  
ROBERT GÖRKE ◽  
CHAN-SU SHIN ◽  
ALEXANDER WOLFF
Keyword(s):  
Voronoi Diagram ◽  
Transportation Network ◽  
Parallel Line ◽  
Line Segments ◽  
Quickest Path ◽  
Manhattan Metric ◽  
Voronoi Regions ◽  
Axis Parallel ◽  
The City ◽  
And Storage

Given a set P of n point sites in the plane, the city Voronoi diagram subdivides the plane into the Voronoi regions of the sites, with respect to the city metric. This metric is induced by quickest paths according to the Manhattan metric and an accelerating transportation network that consists of c non-intersecting axis-parallel line segments. We describe an algorithm that constructs the city Voronoi diagram (including quickest path information) using O((c+n) polylog (c+n)) time and storage by means of a wavefront expansion. For [Formula: see text] our algorithm is faster than an algorithm by Aichholzer et al., which takes O(n log n + c2 log c) time.

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