MOCC: a fast and robust correlation-based method for interest point matching under large scale changes

Authors:
Feng Zhao;Qingming Huang;Hao Wang;Wen Gao
Affiliations:
School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China and Nokia Research Center, Beijing, China;School of Information Science and Engineering, Graduate University of Chinese Academy of Sciences, Beijing, China;Nokia Research Center, Beijing, China;School of Electronics Engineering and Computer Science, Peking University, Beijing, China
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2010

Citing 24
Cited 1

A survey of image registration techniques

ACM Computing Surveys (CSUR)
Recursive implementation of the Gaussian filter

Signal Processing
A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry

Artificial Intelligence - Special volume on computer vision
Local Grayvalue Invariants for Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection

International Journal of Computer Vision
Evaluation of Interest Point Detectors

International Journal of Computer Vision - Special issue on a special section on visual surveillance
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Multiple view geometry in computer vision

Multiple view geometry in computer vision
Saliency, Scale and Image Description

International Journal of Computer Vision
Image matching with scale adjustment

Computer Vision and Image Understanding
Matching Widely Separated Views Based on Affine Invariant Regions

International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors

International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Affine Correlation

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Multi-Image Matching Using Multi-Scale Oriented Patches

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors

International Journal of Computer Vision
Speeded-Up Robust Features (SURF)

Computer Vision and Image Understanding
Towards automatic visual obstacle avoidance

IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Scale-space filtering

IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Faster and Better: A Machine Learning Approach to Corner Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-invariant shape features for recognition of object categories

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Image matching by multiscale oriented corner correlation

ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I

A new approach to medical image stitching using minimum average correlation energy filter and peak to side-lobe ratio

International Journal of Imaging Systems and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Similarity measures based on correlation have been used extensively for matching tasks. However, traditional correlation-based image matching methods are sensitive to rotation and scale changes. This paper presents a fast correlation-based method for matching two images with large rotation and significant scale changes. Multiscale oriented corner correlation (MOCC) is used to evaluate the degree of similarity between the feature points. The method is rotation invariant and capable of matching image pairs with scale changes up to a factor of 7.Moreover, MOCC is much faster in comparison with the state-of-the-art matching methods. Experimental results on real images show the robustness and effectiveness of the proposed method.