Matching between Different Image Domains

@inproceedings{Toth2011MatchingBD,
  title={Matching between Different Image Domains},
  author={Charles K. Toth and Hui Ju and Dorota A. Grejner-Brzezinska},
  booktitle={PIA},
  year={2011}
}
Most of the image registration/matching methods are applicable to images acquired by either identical or similar sensors from various positions. Simpler techniques assume some object space relationship between sensor reference points, such as near parallel image planes, certain overlap and comparable radiometric characteristics. More robust methods allow for larger variations in image orientation and texture, such as the Scale-Invariant Feature Transformation (SIFT), a highly robust technique… 
Automatic Co-Registration of Optical Satellite Images and Airborne Lidar Data Using Relative and Absolute Orientations
  • T. Teo, Shih-Han Huang
  • Environmental Science, Mathematics
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • 2013
TLDR
The proposed method performs image matching between stereo images for relative orientation modeling and generates a matched 3-D surface model that may reach subpixel accuracy while the absolute orientation may reach 1 pixel accuracy.
THERMAL 3D MAPPING FOR OBJECT DETECTION IN DYNAMIC SCENES
TLDR
This paper presents a fully automatic methodology consisting of a radiometric correction, a geometric calibration, a robust approach for detecting reliable feature correspondences and a co-registration of 3D point cloud data and thermal information via a RANSAC-based EPnP scheme and demonstrates that it outperforms other recent approaches in terms of both accuracy and applicability.
A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud Dataset
TLDR
A fine registration method based on a novel concept of tie plane that registers the inaccurate image network to the accurate point cloud data, indicating ∼23% to 40% average accuracy improvement compared to the existing methods.
Co-Registration of 2D Imagery and 3D Point Cloud Data
In this chapter, we address the fact that particularly thermal information offers many advantages for scene analysis, since people may easily be detected as heat sources in typical indoor or outdoor
Automatic Registration of Aerial Images with 3D LiDAR Data Using a Hybrid Intensity-Based Method
TLDR
A new hybrid intensity-based approach that utilizes both statistical and functional relationships between images, particularly in the case of registering aerial images and 3D point clouds, is presented.
Automatic registration of optical imagery with 3d lidar data using local combined mutual information
TLDR
A new multivariable MI approach that exploits complementary information of inherently registered LiDar DSM and intensity data to improve the robustness of registering optical imagery and LiDAR point cloud, is presented.
Automatic co-registration of satellite imagery and LiDAR data using local Mutual Information
TLDR
A new intensity-based approach built on local MI principles is presented, which decreases the complexity of higher dimension optimization by measuring local MI on well-distributed tie points and improves the reliability of registration.
Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations
TLDR
The method for registering close-range optical images with terrestrial LiDAR data is extended to a variety of large-scale aerial optical images and airborne LiDar data and is proved to be more accurate, feasible, efficient, and practical.
Matching Algorithm of Binocular Dynamic Vision Measurement
TLDR
An algorithm of multiple restriction matching which is for two homologous images matching of feature points, based on epipolar line constrain, can get 100% of the matching accuracy rate and satisfy the need of binocular dynamic vision measurement system.
...
...

References

SHOWING 1-10 OF 15 REFERENCES
Robust Scale-Invariant Feature Matching for Remote Sensing Image Registration
TLDR
Experimental results for multidate, multispectral, and multisensor remote images indicate that the proposed scale-orientation joint restriction criteria improves the match performance compared to intensity- and SIFT-based methods in terms of correct-match rate and aligning accuracy.
Invariant Features from Interest Point Groups
TLDR
This work introduces a family of features which use groups of interest points to form geometrically invariant descriptors of image regions to ensure robust matching between images in which there are large changes in viewpoint, scale and illumi- nation.
Object recognition from local scale-invariant features
  • D. Lowe
  • Computer Science
    Proceedings of the Seventh IEEE International Conference on Computer Vision
  • 1999
TLDR
Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations
TLDR
A novel technique to recover large similarity transformations (rotation/scale/translation) and moderate perspective deformations among image pairs and achieves subpixel accuracy through the use of nonlinear least squares optimization.
Robust image registration using log-polar transform
  • G. Wolberg, Siavash Zokai
  • Physics
    Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101)
  • 2000
TLDR
The algorithm estimates the affine transformation parameters necessary to register any two digital images misaligned due to rotation, scale, shear, and translation using a variation of the Levenberg-Marquadt nonlinear least squares optimization method, which yields a robust solution that precisely registers images with subpixel accuracy.
SURF: Speeded Up Robust Features
In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously
Speeded-Up Robust Features (SURF)
Multi-spectral remote image registration based on SIFT
TLDR
Scale restriction criteria for keypoint matching are proposed, and experimental results demonstrate that it will greatly improve match performance.
Kernel-Based Object Tracking
TLDR
A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed, which employs a metric derived from the Bhattacharyya coefficient as similarity measure, and uses the mean shift procedure to perform the optimization.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TLDR
New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
...
...