Parallel algorithm for corner finding on digital curves

Cheng, Fang‐Hsuan; Hsu, Wen‐Hsing

doi:10.1016/0167-8655(88)90023-2

Cited by 39 publications

(7 citation statements)

References 13 publications

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“…Corners can also be found by analyzing edge properties in the window scanned along the edge [29]. A generalized Hough transform [30] can be used, which replaces each edgel with a line segment, and corners can be found where lines intersect, i.e., at large maxima in Hough space [31].…”

Section: Edge-based Corner Detectorsmentioning

confidence: 99%

Faster and Better: A Machine Learning Approach to Corner Detection

Rosten

Porter

Drummond

2010

IEEE Trans. Pattern Anal. Mach. Intell.

1,612

812

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The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection and, using machine learning, we derive a feature detector from this which can fully process live PAL video using less than 5 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that, despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and of very high quality.

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Section: Edge-based Corner Detectorsmentioning

confidence: 99%

Faster and Better: A Machine Learning Approach to Corner Detection

Rosten

Porter

Drummond

2010

IEEE Trans. Pattern Anal. Mach. Intell.

1,612

812

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“…The second is a measure of the lengths, t and t2, of the curves with little or no curvature on either side of a point. At a point a , i and t2 are defined as tl = max{t : E (-,z),V1 V t} (3) t2 max{i : e (-s, z),V1 < v < t} (4) where =tan-J-j (5) Freeman and Davis measure the prominence of a corner as a function of the slope discontinuity, , and the lengths, ii and t2, of the discontinuity free regions to either side of the corner given by (6) 3 PROPOSED CORNER DETECTION ALGORITHM The chain coded representation of a boundary with a corner cannot be distinguished from that of a boundary with very high curvature. When detecting corners from chain coded curves, corners as well as very high curvature in the original image will cause detections.…”

Section: Freeman and Davis Corner Detectionmentioning

confidence: 99%

Corner detection for chain-coded curves

Koplowitz

Plante

1993

SPIE Proceedings

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A corner detection scheme for chain coded curves is proposed that significantly improves on the performance of current algorithms. The proposed scheme measures the number of links to either side of a point that can produce the largest digital straight line. That value is used as an indication of curvature at that point with very high curvature being indicative of a corner. A modification of the proposed algorithm can further reduce the false detection rate with virtually no affect on the number of corners missed by omitting certain patterns that can arise from contours with arbitrarily small curvature.

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“…The turning point detection methods discussed in the literature can be broadly divided into two types: edge-based shape detection methods, which include Medioni-Yasumoto's method, 7 Beus-Tiu's method, 8 Rosenfeld-Johnston's method, 9 Rosenfeld-Weszka's method, 10 and weight type k-curvature method; 11 these methods calculate the curvature value at each point of the edge coordinates after edge detection processing, thus determining the turning point of all objects in an image; and grayscale value-based detection methods. 12 This article adopted Rosenfeld-Johnston's method to detect the turning points in an image. The practice compares the angle of edge point vectors of each object to determine the position of the turning point and calculates approximate curvature values.…”

Section: Introductionmentioning

confidence: 99%

Combining turning point detection and Dijkstra’s algorithm to search the shortest path

Tsai

et al. 2017

Advances in Mechanical Engineering

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In this study, image processing was combined with path-planning object-avoidance technology to determine the shortest path to the destination. The content of this article comprises two parts: in the first part, image processing was used to establish a model of obstacle distribution in the environment, and boundary sequence permutation method was used to conduct orderly arrangement of edge point coordinates of all objects, to determine linking relationship between each edge point, and to individually classify objects in the image. Then, turning point detection method was used to compare the angle size between vectors before and after each edge point and to determine vertex coordinates of polygonal obstacles. In the second part, a modified Dijkstra's algorithm was used to turn vertices of convex-shaped obstacles into network nodes, to determine the shortest path by a cost function, and to find an obstacle avoidance path connecting the start and end points. In order to verify the feasibility of the proposed architecture, an obstacle avoidance path simulation was made by the graphical user interface of the programming language MATLAB. The results show that the proposed method in path planning not only is feasible but can also obtain good results.

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Parallel algorithm for corner finding on digital curves

Cited by 39 publications

References 13 publications

Faster and Better: A Machine Learning Approach to Corner Detection

Faster and Better: A Machine Learning Approach to Corner Detection

Corner detection for chain-coded curves

Combining turning point detection and Dijkstra’s algorithm to search the shortest path

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