Modelling and simulation: An improved RANSAC algorithm based on the relative angle information of samples

Chengbo Liu; Qiang Shen; Hai Pan; Miao Li

doi:10.1504/IJMIC.2017.085939

Modelling and simulation: An improved RANSAC algorithm based on the relative angle information of samples

Chengbo Liu^*, Qiang Shen, Hai Pan, Miao Li

^*Corresponding author for this work

School of Mechatronical Engineering

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

Random sample consensus (RANSAC) algorithm is the most widely used one in the field of computer vision. In order to reduce the high complexity of RANSAC, this paper proposes a novel method which can reject samples before calculating the homography matrix. This algorithm can eliminate random samples that may be wrong through calculating the relative angle information of the random samples, and then, use the correct samples for the next step. The algorithm can ensure the accuracy of the premise while greatly reducing the computational complexity. Not only that, the improved algorithm can also be combined with the existing RANSAC extensions to improve the computational efficiency.

Original language	English
Pages (from-to)	144-152
Number of pages	9
Journal	International Journal of Modelling, Identification and Control
Volume	28
Issue number	2
DOIs	https://doi.org/10.1504/IJMIC.2017.085939
Publication status	Published - 2017

Keywords

Homography matrix
RANSAC
Random sample consensus
Reject samples
Relative angle
Verify model

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10.1504/IJMIC.2017.085939

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title = "Modelling and simulation: An improved RANSAC algorithm based on the relative angle information of samples",

abstract = "Random sample consensus (RANSAC) algorithm is the most widely used one in the field of computer vision. In order to reduce the high complexity of RANSAC, this paper proposes a novel method which can reject samples before calculating the homography matrix. This algorithm can eliminate random samples that may be wrong through calculating the relative angle information of the random samples, and then, use the correct samples for the next step. The algorithm can ensure the accuracy of the premise while greatly reducing the computational complexity. Not only that, the improved algorithm can also be combined with the existing RANSAC extensions to improve the computational efficiency.",

keywords = "Homography matrix, RANSAC, Random sample consensus, Reject samples, Relative angle, Verify model",

author = "Chengbo Liu and Qiang Shen and Hai Pan and Miao Li",

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AU - Liu, Chengbo

AU - Shen, Qiang

AU - Pan, Hai

AU - Li, Miao

PY - 2017

Y1 - 2017

N2 - Random sample consensus (RANSAC) algorithm is the most widely used one in the field of computer vision. In order to reduce the high complexity of RANSAC, this paper proposes a novel method which can reject samples before calculating the homography matrix. This algorithm can eliminate random samples that may be wrong through calculating the relative angle information of the random samples, and then, use the correct samples for the next step. The algorithm can ensure the accuracy of the premise while greatly reducing the computational complexity. Not only that, the improved algorithm can also be combined with the existing RANSAC extensions to improve the computational efficiency.

AB - Random sample consensus (RANSAC) algorithm is the most widely used one in the field of computer vision. In order to reduce the high complexity of RANSAC, this paper proposes a novel method which can reject samples before calculating the homography matrix. This algorithm can eliminate random samples that may be wrong through calculating the relative angle information of the random samples, and then, use the correct samples for the next step. The algorithm can ensure the accuracy of the premise while greatly reducing the computational complexity. Not only that, the improved algorithm can also be combined with the existing RANSAC extensions to improve the computational efficiency.

KW - Homography matrix

KW - RANSAC

KW - Random sample consensus

KW - Reject samples

KW - Relative angle

KW - Verify model

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JO - International Journal of Modelling, Identification and Control

JF - International Journal of Modelling, Identification and Control

IS - 2

ER -

Modelling and simulation: An improved RANSAC algorithm based on the relative angle information of samples

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Keywords

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