scholarly journals Low-level Grouping of Straight Line Segments

1991 ◽  
Author(s):  
A. Etemadi ◽  
J. P. Schmidt ◽  
G. Matas ◽  
J. Illingworth ◽  
J. Kittler
Keyword(s):  
Line Segments ◽  
Low Level ◽  
Straight Line

scholarly journals Low-level Grouping of Straight Line Segments

BMVC91 ◽  
1991 ◽  
pp. 118-126 ◽  
Author(s):  
A. Etemadi ◽  
J.-P. Schmidt ◽  
G. Matas ◽  
J. Illingworth ◽  
J. Kittler
Keyword(s):  
Line Segments ◽  
Low Level ◽  
Straight Line

scholarly journals Use of otoliths to estimate size at sexual maturity in fish

2000 ◽  
Vol 43 (4) ◽  
pp. 437-440 ◽  
Author(s):  
Carlos Sérgio Agostinho
Keyword(s):  
Sexual Maturity ◽  
Line Segments ◽  
Alternative Method ◽  
Straight Line ◽  
Size At Sexual Maturity

The viability of an alternative method for estimating the size at sexual maturity of females of Plagioscion squamosissimus (Perciformes, Sciaenidae) was analyzed. This methodology was used to evaluate the size at sexual maturity in crabs, but has not yet been used for this purpose in fishes. Separation of young and adult fishes by this method is accomplished by iterative adjustment of straight-line segments to the data for length of the otolith and length of the fish. The agreement with the estimate previously obtained by another technique and the possibility of calculating the variance indicates that in some cases, the method analyzed can be used successfully to estimate size at sexual maturity in fish. However, additional studies are necessary to detect possible biases in the method.

Detecting Straight Line Segments Using a Triangular Neighborhood

2010 ◽  
pp. 320-328 ◽  
Author(s):  
Shengzhi Du ◽  
Chunling Tu ◽  
Barend Jacobus van Wyk
Keyword(s):  
Line Segments ◽  
Straight Line

scholarly journals An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature

2017 ◽  
Author(s):  
Lixin He ◽  
Jing Yang ◽  
Bin Kong ◽  
Can Wang
Keyword(s):  
Automatic Measurement ◽  
Depth Estimation ◽  
Depth Information ◽  
Scale Invariant ◽  
Line Segments ◽  
Sift Feature ◽  
Novel Approach ◽  
Straight Line ◽  
Object Depth ◽  
Scale Invariant Feature

It is one of very important and basic problem in compute vision field that recovering depth information of objects from two-dimensional images. In view of the shortcomings of existing methods of depth estimation, a novel approach based on SIFT (the Scale Invariant Feature Transform) is presented in this paper. The approach can estimate the depths of objects in two images which are captured by an un-calibrated ordinary monocular camera. In this approach, above all, the first image is captured. All of the camera parameters remain unchanged, and the second image is acquired after moving the camera a distance d along the optical axis. Then image segmentation and SIFT feature extraction are implemented on the two images separately, and objects in the images are matched. Lastly, an object depth can be computed by the lengths of a pair of straight line segments. In order to ensure that the best appropriate a pair of straight line segments are chose and reduce the computation, the theory of convex hull and the knowledge of triangle similarity are employed. The experimental results show our approach is effective and practical.

scholarly journals Minimum Cell Connection in Line Segment Arrangements

2017 ◽  
Vol 27 (03) ◽  
pp. 159-176
Author(s):  
Helmut Alt ◽  
Sergio Cabello ◽  
Panos Giannopoulos ◽  
Christian Knauer
Keyword(s):  
Linear Time ◽  
Optimal Solution ◽  
Time Algorithm ◽  
Linear Time Algorithm ◽  
Constant Number ◽  
Line Segments ◽  
Straight Line ◽  
Minimum Number ◽  
Number Of Segments ◽  
Connection Problems

We study the complexity of the following cell connection problems in segment arrangements. Given a set of straight-line segments in the plane and two points [Formula: see text] and [Formula: see text] in different cells of the induced arrangement: [(i)] compute the minimum number of segments one needs to remove so that there is a path connecting [Formula: see text] to [Formula: see text] that does not intersect any of the remaining segments; [(ii)] compute the minimum number of segments one needs to remove so that the arrangement induced by the remaining segments has a single cell. We show that problems (i) and (ii) are NP-hard and discuss some special, tractable cases. Most notably, we provide a near-linear-time algorithm for a variant of problem (i) where the path connecting [Formula: see text] to [Formula: see text] must stay inside a given polygon [Formula: see text] with a constant number of holes, the segments are contained in [Formula: see text], and the endpoints of the segments are on the boundary of [Formula: see text]. The approach for this latter result uses homotopy of paths to group the segments into clusters with the property that either all segments in a cluster or none participate in an optimal solution.

Mapping with Monocular Vision in Two Dimensions

2010 ◽  
Vol 1 (4) ◽  
pp. 56-65 ◽  
Author(s):  
Nicolau Leal Werneck ◽  
Anna Helena Reali Costa
Keyword(s):  
Optical Axis ◽  
Two Dimensions ◽  
Obstacle Detection ◽  
Uniform Motion ◽  
Mapping Algorithms ◽  
Multiple Images ◽  
Line Segments ◽  
Straight Line ◽  
Camera Intrinsic Parameters ◽  
Over Time

This article presents the problem of building bi-dimensional maps of environments when the sensor available is a camera used to detect edges crossing a single line of pixels and motion is restricted to a straight line along the optical axis. The position over time must be provided or assumed. Mapping algorithms for these conditions can be built with the landmark parameters estimated from sets of matched detection from multiple images. This article shows how maps that are correctly up to scale can be built without knowledge of the camera intrinsic parameters or speed during uniform motion, and how performing an inverse parameterization of the image coordinates turns the mapping problem into the fitting of line segments to a group of points. The resulting technique is a simplified form of visual SLAM that can be better suited for applications such as obstacle detection in mobile robots.

Algorithm for Detecting Straight Line Segments in Color Images

2019 ◽  
Vol 56 (21) ◽  
pp. 211002
Author(s):  
刘雨晴 Liu Yuqing ◽  
钟宝江 Zhong Baojiang ◽  
郑行家 Zheng Hangjia
Keyword(s):  
Color Images ◽  
Line Segments ◽  
Straight Line

Grouping of Straight Line Segments and Circular Arcs for Scene Analysis

1995 ◽  
pp. 163-185
Author(s):  
Jean-Luc Arseneault ◽  
Robert Bergevin ◽  
Denis Laurendeau
Keyword(s):  
Scene Analysis ◽  
Line Segments ◽  
Circular Arcs ◽  
Straight Line
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