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About 10,000 papers for “Line segment”

Straight line or line segment?

Vlasta Moravcová, Jana Hromadová · Teaching Mathematics and Computer Science · 2020

The article focuses on students’ understanding of the concept of a straight line. Attention is paid to whether students of various ages work with only part of a straight line shown or if they are aware that it can be extended. The presented results were obtained by a qualitative analysis of tests given to nearly 1,500 Czech students. The paper introduces the statistics of students’ solutions, and discusses the students’ thought processes. The results show that most of the tested students, even after completing upper secondary school, are not aware that a straight line can be extended. Finally, we present some recommendations for fostering the appropriate concept of a straight line in mathematics teaching.
 Subject Classification: 97C30, 97D70, 97G40

Cited by 1Open access

Line Segment Intersections

Michael Hoffmann · 2009

in R 2, does P describe the boundary of a simple polygon? Problem 2 (Polygon Intersection) Given two simple polygons P and Q in R 2 as a (counterclockwise) sequence of their vertices, is P \\ Q =;? Problem 3 (Segment Intersection Test) Given a set of n closed line segments in R 2, do any two of them intersect? When do two line segments intersect? If one endpoint lies on the other? If two segments share an endpoint? What about overlapping segments and segments of length zero? For the time being, let us count all these as intersections. Except in case of simple polygons, where we do not want to consider the shared endpoint between two consecutive edges of the boundary. Problem 4 (Segment Intersections) Given a set of n closed line segments in R 2, compute all pairs of segments that intersect. Problem 5 (Segment Arrangement) Given a set of n closed line segments in R 2, construct the arrangement induced by S, that is, the subdivision of R 2 induced by S. Problem 6 (Map Overlay) Given two sets S and T of n and m, respectively, pairwise interior disjoint line segments in R 2, construct the arrangement induced by

Cited by 0

Line Segment Intersection

Today Am Going · 2007

by a (2; 4)-tree or an equivalent structure. The X-structure stores events to the right of the sweep line, i.e., points where we can add to G and need to update the X- and y-structure. The X-structure is organized as a priority queue by x-coordinate. It contains at least ffl all segment endpoints to the right of the sweep line ffl all intersections to the right of the sweep line between segments that are adjacent in the Y -structure. Intersections between segments that are not adjacent may or may not be part of the X-structure. Some will be, some will be not. Lemma 1 Let p be the vertex of G closest to the sweep line and to the right of the sweep line. Then p is contained in the X-structure. Proof: If p is an endpoint of a segment, the claim is certainly true, since the X-structure contains all endpoints ahead of the sweep line. So assume that p is an intersection, say between segments s 1 and s 2 . Both segments must intersect the sweep line, as otherwise there would be an endpo

Cited by 0

Length = length of the line segment, dist = distance to line segment

Thaddeus Beier, Shawn Neely · 2013

Morphing is turning one image into another through a seamless transition Michael Jackson’s “Black or White” Cross-fading Image morphing image #1 cross-fading image #2 warp morphing warp Image morphing Morphing = warping + cross-dissolving shape (geometric) color

Cited by 0

Bisection of line segment

Schreiber, Michael · Acervo Digital da Universidade Estadual Paulista (Universidade Estadual Paulista) · 2013

Cited by 0

Recognising Line Segment Strokes

Patrick C Hew · 1999

We build a classifer which decides whether a sequence of pixels can be adequately modelled as a line segment. 1 Introduction The purpose of this investigation is to determine whether a given stroke may be adequately modelled by a line segment. We work with strokes extracted by [5], however we should be able to work with in any situation where the stroke is supplied as a sequence of pixels traversing from end to end. 2 Line Segment Modelling We should note from the outside that we may fit a line segment to any set of data. The real question is how well the model fits the data, and hence whether the model is a good replacement for the data. For unordered data, we would probably use linear regression through central moments [1]; that is, fit a Gaussian model and take the principal axis from the mean. However, since we have a sequence of pixels recording pixel traversal, it is easier for us to take the line segment joining the first pixel to the last. 1 3 Tie to Model Our first attemp...

Cited by 0

A line segment detector based on subanchor point and line segment combination method

Fuyou Fan, Yuanhui Jiang, Ke Han et al. · 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) · 2022

Linear segment features contains rich geometric information and it is crucial to perform accurate linear segment detection. We propose a temporal line segment detector based on subanchor point and line segment combination approach, which gives more accurate results without adjusting the parameters. The speed remains essentially the same as the fastest running line segment detectors (EDLines) existing and is 89’ faster compared to the conventional LSD line extraction algorithm. The proposed algorithm introduces bilateral filtering and line segment combination methods based on EDLines. The bilateral filter is used after the image input to remove the noise and retain the edge information of the image well. The short line segments that exist homologous are combined to generate clearer long line segments, which overcomes the problem of broken lines in LSD algorithm and excessive filtering of short line segments in EDLines algorithm. We validate the effectiveness of the proposed algorithm in experiments of line feature extraction for multiple sets of images in different scenes.

Cited by 1

LSD: a Line Segment Detector

Rafael Grompone von Gioi, Jérémie Jakubowicz, Jean‐Michel Morel et al. · Image Processing On Line · 2012

LSD is a linear-time Line Segment Detector giving subpixel accurate results. It is designed to work on any digital image without parameter tuning. It controls its own number of false detections: On average, one false alarm is allowed per image. The method is based on Burns, Hanson, and Riseman's method, and uses an a-contrario validation approach according to Desolneux, Moisan, and Morel's theory. The version described here includes some further improvement over the one described in the original article.

Cited by 807Open access

Is a Line Segment Circular?

Milica Stojmenović · 2010

This paper is interested in circularity definitions and algorithms which most precisely model human sentiment regarding what it means to be circular. Existing research surprisingly does not define the concept of circularity, it assumes it is apparent like the linearity concept. This paper limits its discussion to simple open shapes that can be drawn in a single stroke (edges). Can a line be regarded as part of a circle with a very large radius? Although what humans or computers may perceive to be circularly shaped seems trivial, existing computer measures disagree about the answer, giving opposite measures. Similar disagreement was observed among humans. Circularity was tested on a set of 20 open curves (edges), using both human participants and existing computer algorithms. Human measurements correlate with computer measurements based on center of gravity but not with those based on the true center.

Cited by 0

The Poisson line segment model

Chapman & Hall/CRC machine learning & pattern recognition/Chapman & Hall/CRC machine learning & pattern recognition series · 2005

Cited by 0

Contrast-Guided Line Segment Detection

Zikai Wang, Baojiang Zhong, Dongxu Han et al. · IEEE Signal Processing Letters · 2023

Due to the effects of quantization error and image noise, detecting ‘meaningful’ line segments from an image with high continuity is a challenging task. To pursue this goal, a novel line segment detector, called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contrast-guided line segment detector</i> (CGLSD), is proposed in this paper. Our basic idea is to integrate a low-level image attribute, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge contrast</i> , into the line segment detection process for improving line continuity. After applying an edge detector to the input image, the edge contrast is exploited to guide the growth of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">line-support region</i> for each line segment individually. This is achieved by evaluating edge pixels as well as those non-edge pixels that are nearby the edges. As a result, some of the non-edge pixels are re-considered as ‘edge’ pixels and included for establishing the support region. Reversely, certain edge pixels might be treated as ‘non-edge’ pixels instead and excluded from the region. Since each support region is supposed to yield only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one</i> line segment, each formed support region needs to have a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">refinement</i> by removing those edge pixels that do not belong to it. Lastly, the support region is required to pass through a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">validation</i> check that might lead to a complete discard of the line segment due to its low confidence. Extensive experiments are conducted and compared with multiple state-of-the-arts on two datasets, including the one from us with manually-annotated ground truth. The results have shown that the proposed CGLSD can deliver superior performance in nearly all test cases.

Cited by 4

Parallel line segment intersection reporting

Rueb, C. (Saarland Univ., Saarbruecken (Germany). Fachrichtung Informatik), Saarland Univ., Saarbruecken (Germany). Sonderforschungsbereich 124 VLSI Entwurfsmethoden und Parallelitaet, Kaiserslautern Univ. (Germany). Sonderforschungsbereich 124 VLSI Entwurfsmethoden und Parallelitaet · OpenGrey (Institut de l'Information Scientifique et Technique) · 1987

Cited by 0