Abstract

Most existing feature-matching methods utilize texture correlation for feature matching, which is usually sensitive to contrast changes. This paper proposes a new feature-point matching algorithm that does not rely on the image texture. Instead, only the smoothness assumption, which states that the displacement field in a neighborhood is coherent (smooth), is used. In the proposed method, the collected correspondences of a group of feature points within a neighborhood are efficiently determined such that the coherence measure of the displacement field in the neighborhood is maximized. The experimental results show that the proposed method is invariant to contrast changes and significantly outperforms the conventional block-matching technique

Keywords:
Artificial intelligence Pattern recognition (psychology) Invariant (physics) Matching (statistics) Computer science Computer vision Feature (linguistics) Contrast (vision) Point set registration Coherence (philosophical gambling strategy) Smoothness Feature extraction Mathematics Feature matching Point (geometry) Geometry Statistics

Metrics

3
Cited By
0.90
FWCI (Field Weighted Citation Impact)
7
Refs
0.76
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering

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