RANSAC versus CS-RANSAC

Geun Sik Jo, Kee Sung Lee, Devy Chandra, Chol Hee Jang, Myung Hyun Ga

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

A homography matrix is used in computer vision field to solve the correspondence problem between a pair of stereo images. RANSAC algorithm is often used to calculate the homography matrix by randomly selecting a set of features iteratively. CS-RANSAC algorithm in this paper converts RANSAC algorithm into two-layers. The first layer is addressing sampling problem which we can describe our knowledge about degenerate features by mean of Constraint Satisfaction Problems (CSP). By dividing the input image into a N×N grid and making feature points into discrete domains, we can model the image into the CSP model to efficiently filter out degenerate features. By expressing the knowledge about degenerate feature samples using CSP in the first layer, so that computer has knowledge about how to skip computing the homography matrix in the model estimation step for the second layer. The experimental results show that the proposed CS-RANSAC algorithm can outperform the most of variants of RANSAC without sacrificing its execution time.

Original languageEnglish
Title of host publicationProceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
PublisherAI Access Foundation
Pages1350-1356
Number of pages7
ISBN (Electronic)9781577357001
StatePublished - 1 Jun 2015
Event29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 - Austin, United States
Duration: 25 Jan 201530 Jan 2015

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Conference

Conference29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
Country/TerritoryUnited States
CityAustin
Period25/01/1530/01/15

Bibliographical note

Publisher Copyright:
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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