Image registration based on feature point sets

2003 ◽  
Author(s):  
Quanbing Zhang ◽  
Bin Luo ◽  
Sui Wei ◽  
Shangjun Yang
Keyword(s):  
Image Registration ◽  
Feature Point ◽  
Point Sets

scholarly journals AN INTEGRATED RANSAC AND GRAPH BASED MISMATCH ELIMINATION APPROACH FOR WIDE-BASELINE IMAGE MATCHING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 297-300 ◽  
Author(s):  
M. Hasheminasab ◽  
H. Ebadi ◽  
A. Sedaghat
Keyword(s):  
Image Matching ◽  
Graph Transformation ◽  
Feature Point ◽  
Consensus Method ◽  
Feature Descriptor ◽  
Range Image ◽  
Point Sets ◽  
Short Baseline ◽  
Epipolar Constraint ◽  
Baseline Image

In this paper we propose an integrated approach in order to increase the precision of feature point matching. Many different algorithms have been developed as to optimizing the short-baseline image matching while because of illumination differences and viewpoints changes, wide-baseline image matching is so difficult to handle. Fortunately, the recent developments in the automatic extraction of local invariant features make wide-baseline image matching possible. The matching algorithms which are based on local feature similarity principle, using feature descriptor as to establish correspondence between feature point sets. To date, the most remarkable descriptor is the scale-invariant feature transform (SIFT) descriptor , which is invariant to image rotation and scale, and it remains robust across a substantial range of affine distortion, presence of noise, and changes in illumination. The epipolar constraint based on RANSAC (random sample consensus) method is a conventional model for mismatch elimination, particularly in computer vision. Because only the distance from the epipolar line is considered, there are a few false matches in the selected matching results based on epipolar geometry and RANSAC. Aguilariu et al. proposed Graph Transformation Matching (GTM) algorithm to remove outliers which has some difficulties when the mismatched points surrounded by the same local neighbor structure. In this study to overcome these limitations, which mentioned above, a new three step matching scheme is presented where the SIFT algorithm is used to obtain initial corresponding point sets. In the second step, in order to reduce the outliers, RANSAC algorithm is applied. Finally, to remove the remained mismatches, based on the adjacent K-NN graph, the GTM is implemented. Four different close range image datasets with changes in viewpoint are utilized to evaluate the performance of the proposed method and the experimental results indicate its robustness and capability.

Influence of Speckle Noise on Image Registration Based on Feature Point Matching

2017 ◽  
Vol 54 (12) ◽  
pp. 121103
Author(s):  
陈波 Chen Bo ◽  
孙天齐 Sun Tianqi ◽  
刘爱新 Liu Aixin
Keyword(s):  
Image Registration ◽  
Speckle Noise ◽  
Feature Point ◽  
Point Matching ◽  
Feature Point Matching

The Research of Image Registration Method Based on the Genetic Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1916-1918
Author(s):  
Hua Li ◽  
Di Xiao Wu ◽  
Qing Min Li ◽  
Peng Ying Zhang ◽  
Rui Wang
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
Image Registration ◽  
Satellite Remote Sensing ◽  
Search Method ◽  
Feature Point ◽  
Image Morphing ◽  
Registration Method ◽  
State Information

This paper puts forward one image registration method based on the feature point of the contour, and Introducing the search method based on genetic algorithm .the method can deal with image morphing caused by translation, rotation and zooming change. This method is simple, convenient , feasible and good robustness, and is suitable for the aerial of satellite remote sensing, the plane and UUV underwater detection state information which are rich image for registration.

scholarly journals Fine scale image registration in large-scale urban LIDAR point sets

2017 ◽  
Vol 157 ◽  
pp. 90-102 ◽  
Author(s):  
Maximilien Guislain ◽  
Julie Digne ◽  
Raphaëlle Chaine ◽  
Gilles Monnier
Keyword(s):  
Image Registration ◽  
Large Scale ◽  
Fine Scale ◽  
Point Sets

scholarly journals High-Resolution Optical Remote Sensing Image Registration via Reweighted Random Walk based Hyper-Graph Matching

2019 ◽  
Vol 11 (23) ◽  
pp. 2841 ◽  
Author(s):  
Wu ◽  
Di ◽  
Ming ◽  
Lv ◽  
Tan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Graph Matching ◽  
Remote Sensing Image ◽  
Feature Point ◽  
Optical Remote Sensing ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Optical Remote Sensing Image

High-resolution optical remote sensing image registration is still a challenging task due to non-linearity in the intensity differences and geometric distortion. In this paper, an efficient method utilizing a hyper-graph matching algorithm is proposed, which can simultaneously use the high-order structure information and radiometric information, to obtain thousands of feature point pairs for accurate image registration. The method mainly consists of the following steps: firstly, initial matching by Uniform Robust Scale-Invariant Feature Transform (UR-SIFT) is carried out in the highest pyramid image level to derive the approximate geometric relationship between the images; secondly, two-stage point matching is performed to find the matches, that is, a rotation and scale invariant area-based matching method is used to derive matching candidates for each feature point and an efficient hyper-graph matching algorithm is applied to find the best match for each feature point; thirdly, a local quadratic polynomial constraint framework is used to eliminate match outliers; finally, the above process is iterated until finishing the matching in the original image. Then, the obtained correspondences are used to perform the image registration. The effectiveness of the proposed method is tested with six pairs of high-resolution optical images, covering different landscape types—such as mountain area, urban, suburb, and flat land—and registration accuracy of sub-pixel level is obtained. The experiments show that the proposed method outperforms the conventional matching algorithms such as SURF, AKAZE, ORB, BRISK, and FAST in terms of total number of correct matches and matching precision.

scholarly journals Robust Feature Matching with Spatial Smoothness Constraints

2020 ◽  
Vol 12 (19) ◽  
pp. 3158
Author(s):  
Xu Huang ◽  
Xue Wan ◽  
Daifeng Peng
Keyword(s):  
Energy Function ◽  
Feature Matching ◽  
Feature Point ◽  
Limiting Factors ◽  
Feature Points ◽  
Point Sets ◽  
Matching Accuracy ◽  
Matching Problem ◽  
Underlying Assumption ◽  
Speeded Up Robust Features

Feature matching is to detect and match corresponding feature points in stereo pairs, which is one of the key techniques in accurate camera orientations. However, several factors limit the feature matching accuracy, e.g., image textures, viewing angles of stereo cameras, and resolutions of stereo pairs. To improve the feature matching accuracy against these limiting factors, this paper imposes spatial smoothness constraints over the whole feature point sets with the underlying assumption that feature points should have similar matching results with their surrounding high-confidence points and proposes a robust feature matching method with the spatial smoothness constraints (RMSS). The core algorithm constructs a graph structure from the feature point sets and then formulates the feature matching problem as the optimization of a global energy function with first-order, spatial smoothness constraints based on the graph. For computational purposes, the global optimization of the energy function is then broken into sub-optimizations of each feature point, and an approximate solution of the energy function is iteratively derived as the matching results of the whole feature point sets. Experiments on close-range datasets with some above limiting factors show that the proposed method was capable of greatly improving the matching robustness and matching accuracy of some feature descriptors (e.g., scale-invariant feature transform (SIFT) and Speeded Up Robust Features (SURF)). After the optimization of the proposed method, the inlier number of SIFT and SURF was increased by average 131.9% and 113.5%, the inlier percentages between the inlier number and the total matches number of SIFT and SURF were increased by average 259.0% and 307.2%, and the absolute matching accuracy of SIFT and SURF was improved by average 80.6% and 70.2%.

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