Indexed by: Journal paper

First Author: Liang Xie

Correspondence Author: ZHANG JUN

Co-author: Shu Chen,TIAN JIN WEN

Journal: Journal of Image and Graphics

Discipline: Engineering

First-Level Discipline: Computer Science and Technology

Document Type: J

Volume: 20

Issue: 8

Page Number: 1017-1025

Key Words: matched pairs purified; Hough transform; outliers; parameter space; voting

DOI number: 10.11834/jig.20150804

Date of Publication: 2015-08-07

Abstract: Objective Outliers inevitably exist in image matching， and they may result in a significantly high mismatching ratio when the overlapping region of two images is small． Some normal matching algorithms cause a high mismatching ratio． A robust purifying algorithm for rough matched pairs can be designed to solve this problem by reducing difficulties in image matching． Since Hough transform was proposed， it has been used to detect certain kinds of curves． It establishes a mathematical model for curves and votes in the para-meter space and determines exact parameters of the curve via maximum value in the parameter space． Based on the same voting idea， Hough transform is introduced in this paper to purify rough matched pairs． Method First， we assume that those truly matched pairs obey a certain transform model equation． Then， a common transform model can be established， and Hough transform is used to obtain parameters of model equation． In particular， each matched pair votes on the corresponding hypersurface， which is in the parameter space and determined by Hough transform． Thus， parameters of the transform model equation can be determined by the global maximum value in the parameter space． Then， all matched pairs that obey model equation are saved． Thus， the rough matched pairs can be purified in this way． Ｒesult Compared with traditional algorithms， such as random sample consensus， the proposed algorithm is not only robust to outliers with a good recall ratio but also more efficient． Moreover， experimental results indicate that the proposed method can be robust when the ratio of outliers is as high as 85% ， and even when the ratio is up to 95% ， it still can work very well with a probability of 50% ． Conclusion Hough transform can be applied to purify matched pairs， and many experiments prove its feasibility． Corresponding models should be chosen to obtain a good performance when aiming at rigidbody transformation and affine transformation． However， the proposed method is not suitable when many parameters ( more than four) exist in the model equation， given that a high-dimensional space determined by parameters of the model equation is memory expensive and time consuming when searching and voting in the high dimensional parameters space．