A Novel Linear Time Corner Detection Algorithm

Sarfraz, Muhammad; Rasheed, Abdul Imran; Muzaffar, Zeeshan

doi:10.1109/cgiv.2005.7

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…Arrebola and Sandoval [30] used the hierarchical computation of a multi-resolution structure to detect corners. Sarfraz et al [31] applied the change of sign of slope of the curve along the contour to present a simple, robust with respect to noise, accurate and efficient corner detector which works for smooth and irregular planner shapes. Mokhtarian and Mohanna [2] extended curvature scale space corner detector [26], which also worked on multiple scales.…”

Section:  Boundary-based Corner Detectorsmentioning

confidence: 99%

An Improvement to a Class of Intensity based Spatial Domain Corner Detection Algorithms using Image Fission and Fusion

2019

IJEAT

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Corners of an object are important as features for the representation and analysis of its shape in computer vision. Corner detection, particularly in real scenes, is still a challenge. Most of the corner detectors found in the literature generate a number of false corners, which is not acceptable in real-life applications. In this paper, an improvement to a class of corner detection algorithms is presented using image fission/fusion. In this approach, a grayscale image is first divided into several bit-planes. A corner detector is applied on all the bit-planes simultaneously and a threshold (bitplane) is obtained using the concept of information gain. Finally, all the higher bit-plane corners are recombined (up to some thresholded bit-plane) to obtain the final set of corners. Here the corner detection algorithm is considered as a binary classification problem. Experimental results show that this improved approach reduces the number of erroneous corner detection relative to existing spatial domain corner detection algorithms. The improvements are established with the help of a number of performance measures proposed by various researchers. The proposed approach works better with respect to computational time also. This approach can easily be utilized in different low-level image processing applications.

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Section:  Boundary-based Corner Detectorsmentioning

confidence: 99%

An Improvement to a Class of Intensity based Spatial Domain Corner Detection Algorithms using Image Fission and Fusion

2019

IJEAT

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“…This paper proposes a new corner detection algorithm using boundary-based approach. It is different from ordinary techniques [3,5,6,[8][9][10][11][12][13][14][15][16]20] which detect cosine angle for change in the boundary curve. The proposed algorithm defines its setting of geometry as a rectangle (R) and two ellipses (EE) for global, intermediate and local views respectively.…”

Section: Introductionmentioning

confidence: 99%

“…These points are used in multi-signal representation, shapes illustration, stereo system visualization, to track the movement of objects, image matching, construction of 2D mosaics [17], and the early phase of contour drawing systems. Many corner detection algorithms have been proposed in the existing literature [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Mining Corner Points on the Generic Shapes

Sarfraz¹,

Swati²

2013

OJAppS

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This paper designs and implements a corner detection algorithm for mining corner points on the generic shapes. The proposed corner detector detects corners by using combination of one rectangle and two ellipses (REE) with different parameter settings in their descriptions. REE combination slides along the boundary of the shape and records number of boundary points in each rectangle and ellipses. REE setup represents both local and global views of the image outline. The proposed technique presents a natural corners detection methodology to detect all true corners accurately. This technique is consistent with human vision system

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Revision of Using Eigenvalues of Covariance Matrices in Boundary-Based Corner Detection

Horng

Chen

2009

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we present a revision of using eigenvalues of covariance matrices proposed by Tsai et al. as a measure of significance (i.e., curvature) for boundary-based corner detection. We first show the pitfall of Tsai et al.'s approach. We then further investigate the properties of eigenvalues of covariance matrices of three different types of curves and point out a mistake made by Tsai et al.'s method. Finally, we propose a modification of using eigenvalues as a measure of significance for corner detection to remedy their defect. The experiment results show that under the same conditions of the test patterns, in addition to correctly detecting all true corners, the spurious corners detected by Tsai et al.'s method disappear in our modified measure of significance.

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A Novel Linear Time Corner Detection Algorithm

Cited by 4 publications

References 25 publications

An Improvement to a Class of Intensity based Spatial Domain Corner Detection Algorithms using Image Fission and Fusion

An Improvement to a Class of Intensity based Spatial Domain Corner Detection Algorithms using Image Fission and Fusion

Mining Corner Points on the Generic Shapes

Revision of Using Eigenvalues of Covariance Matrices in Boundary-Based Corner Detection

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