Comparative study on image matching methods

1998 ◽  
Author(s):  
Jie-Gu Li ◽  
Jun Zhang ◽  
Qian-bang Yang
Keyword(s):  
Comparative Study ◽  
Image Matching ◽  
Matching Methods

scholarly journals Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

2020 ◽  
Vol 12 (4) ◽  
pp. 696 ◽  
Author(s):  
Zhen Ye ◽  
Yusheng Xu ◽  
Hao Chen ◽  
Jingwei Zhu ◽  
Xiaohua Tong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Comparative Study ◽  
Image Matching ◽  
Systematic Errors ◽  
Disparity Estimation ◽  
Sensing Applications ◽  
Subpixel Accuracy ◽  
Dense Image ◽  
Remote Sensing Applications ◽  
Practical Analysis

Dense image matching is a crucial step in many image processing tasks. Subpixel accuracy and fractional measurement are commonly pursued, considering the image resolution and application requirement, especially in the field of remote sensing. In this study, we conducted a practical analysis and comparative study on area-based dense image matching with subpixel accuracy for remote sensing applications, with a specific focus on the subpixel capability and robustness. Twelve representative matching algorithms with two types of correlation-based similarity measures and seven types of subpixel methods were selected. The existing matching algorithms were compared and evaluated in a simulated experiment using synthetic image pairs with varying amounts of aliasing and two real applications of attitude jitter detection and disparity estimation. The experimental results indicate that there are two types of systematic errors: displacement-dependent errors, depending on the fractional values of displacement, and displacement-independent errors represented as unexpected wave artifacts in this study. In addition, the strengths and limitations of different matching algorithms on the robustness to these two types of systematic errors were investigated and discussed.

scholarly journals Global Distribution Adjustment and Nonlinear Feature Transformation for Automatic Colorization

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Terumasa Aoki ◽  
Van Nguyen
Keyword(s):  
Image Matching ◽  
Feature Vector ◽  
Color Image ◽  
Computational Cost ◽  
Texture Feature ◽  
Feature Descriptor ◽  
Matching Methods ◽  
Nonlinear Feature ◽  
Efficient Matching ◽  
Descriptive Feature

Automatic colorization is generally classified into two groups: propagation-based methods and reference-based methods. In reference-based automatic colorization methods, color image(s) are used as reference(s) to reconstruct original color of a gray target image. The most important task here is to find the best matching pairs for all pixels between reference and target images in order to transfer color information from reference to target pixels. A lot of attractive local feature-based image matching methods have already been developed for the last two decades. Unfortunately, as far as we know, there are no optimal matching methods for automatic colorization because the requirements for pixel matching in automatic colorization are wholly different from those for traditional image matching. To design an efficient matching algorithm for automatic colorization, clustering pixel with low computational cost and generating descriptive feature vector are the most important challenges to be solved. In this paper, we present a novel method to address these two problems. In particular, our work concentrates on solving the second problem (designing a descriptive feature vector); namely, we will discuss how to learn a descriptive texture feature using scaled sparse texture feature combining with a nonlinear transformation to construct an optimal feature descriptor. Our experimental results show our proposed method outperforms the state-of-the-art methods in terms of robustness for color reconstruction for automatic colorization applications.

The Method of Round Image Matching Based on Log-Polar Transform*

2011 ◽  
Vol 467-469 ◽  
pp. 1024-1029
Author(s):  
Zhi Jia Zhang ◽  
Jing Chen
Keyword(s):  
Image Matching ◽  
Matching Method ◽  
Matching Problem ◽  
Matching Methods ◽  
Polar Coordinate ◽  
Angle Rotation ◽  
Image Transform ◽  
Traditional Image

Traditional image matching methods are not proper for the situation of the angle rotation. Aiming at this problem, This paper proposes an round image matching method based on log-polar transform. Log-polar image transform can convert the rotation changes in Descartes coordinate to shift changes in log-polar coordinate, which had such trait as invariance to rotation. Results show that this algorithm based on log-polar transform can match the images with rotation changes, Compared with existing algorithms, the new algorithm not only had higher matching rate and stronger adaptability, but also can effectively solve matching problem under the circumstance of brightness changes, contrast changes or noise interferences.

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