There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

http://library.utia.cas.cz/prace/20030125.pdf

Image registration methods: a survey

A method is presented for automated best-matching alignment of three-dimensional models represented by ensembles of points. A normalized spatial discrepancy (NSD) is introduced as a proximity measure between three-dimensional objects. Starting from an inertia-axes alignment, the algorithm minimizes the NSD; the final value of the NSD provides a quantitative estimate of similarity between the objects. The method is implemented in a computer program. Simulations have been performed to test its performance on model structures with specified numbers of points ranging from a few to a few thousand. The method can be used for comparative analysis of structural models obtained by different methods, e.g. of high-resolution crystallographic atomic structures and low-resolution models from solution scattering or electron microscopy.

/pdf/automated-matching-of-high-and-low-resolution-structural-1jhe9r463w.pdf

Automated matching of high‐ and low‐resolution structural models

The Iterative Closest Point (ICP) algorithm is one of the most widely used methods for point-set registration. However, being based on local iterative optimization, ICP is known to be susceptible to local minima. Its performance critically relies on the quality of the initialization and only local optimality is guaranteed. This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the $L_2$ error metric defined in ICP. The Go-ICP method is based on a branch-and-bound scheme that searches the entire 3D motion space $SE(3)$ . By exploiting the special structure of $SE(3)$ geometry, we derive novel upper and lower bounds for the registration error function. Local ICP is integrated into the BnB scheme, which speeds up the new method while guaranteeing global optimality. We also discuss extensions, addressing the issue of outlier robustness. The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization. Go-ICP can be applied in scenarios where an optimal solution is desirable or where a good initialization is not always available.

Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration

The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas

Template Matching Techniques in Computer Vision: Theory and Practice

Clustering is central to many image processing and remote sensing applications. ISODATA is one of the most popular and widely used clustering methods in geoscience applications, but it can run slowly, particularly with large data sets. We present a more efficient approach to ISODATA clustering, which achieves better running times by storing the points in a kd-tree and through a modification of the way in which the algorithm estimates the dispersion of each cluster. We also present an approximate version of the algorithm which allows the user to further improve the running time, at the expense of lower fidelity in computing the nearest cluster center to each point. We provide both theoretical and empirical justification that our modified approach produces clusterings that are very similar to those produced by the standard ISODATA approach. We also provide empirical studies on both synthetic data and remotely sensed Landsat and MODIS images that show that our approach has significantly lower running times.

/pdf/a-fast-implementation-of-the-isodata-clustering-algorithm-1hdtasybko.pdf

A fast implementation of the isodata clustering algorithm

https://www.cs.umd.edu/users/mount/Papers/pr99-robust.pdf

Efficient algorithms for robust feature matching

Foreword Jon A. Benediktsson Part I. The Importance of Image Registration for Remote Sensing: 1. Introduction Jacqueline Le Moigne, Nathan S. Netanyahu and Roger D. Eastman 2. Influence of image registration on validation efforts Bin Tan and Curtis E. Woodcock 3. Survey of image registration methods Roger D. Eastman, Nathan S. Netanyahu and Jacqueline Le Moigne Part II. Similarity Metrics for Image Registration: 4. Fast correlation and phase correlation Harold S. Stone 5. Matched filtering techniques Qin-Sheng Chen 6. Image registration using mutual information Arlene A. Cole-Rhodes and Pramod K. Varshney Part III. Feature Matching and Strategies for Image Registration: 7. Registration of multiview images A. Ardeshir Goshtasby 8. New approaches to robust, point-based image registration David M. Mount, Nathan S. Netanyahu and San Ratanasanya 9. Condition theory for image registration and post-registration error estimation Charles S. Kenney, B. S. Manjunath, Marco Zuliani and Kaushal Solanki 10. Feature-based image to image registration Venu M. Govindu and Rama Chellappa 11. On the use of wavelets for image registration Jacqueline Le Moigne, Ilya Zavorin and Harold S. Stone 12. Gradient descent approaches to image registration Arlene A. Cole-Rhodes and Roger D. Eastman 13. Bounding the performance of image registration Min Xu and Parmod K. Varshney Part IV. Applications and Operational Systems: 14. Multi-temporal and multi-sensor image registration Jacqueline Le Moigne, Arlene A. Cole-Rhodes, Roger D. Eastman, Nathan S. Netanyahu, Harold S. Stone, Ilya Zavorin and Jeffrey T. Morisette 15. Georegistration of meteorological images James L. Carr 16. Challenges, solutions, and applications of accurate multi-angle image registration: lessons learned from MISR Veljko M. Jovanovic, David J. Diner and Roger Davies 17. Automated AVHRR image navigation William J. Emery, R. Ian Crocker and Daniel G. Baldwin 18. Landsat image geocorrection and registration James C. Storey 19. Automatic and precise orthorectification of SPOT images Simon Baillarin, Aurelie Bouillon and Marc Bernard 20. Geometry of the VEGETATION sensor Sylvia Sylvander 21. Accurate MODIS global geolocation through automated ground control image matching Robert E. Wolfe and Masahiro Nishihama 22. SeaWIFS operational geolocation assessment system Frederick S. Patt Part V. Conclusion: 23. Concluding remarks Jacqueline Le Moigne, Nathan S. Netanyahu and Roger D. Eastman Glossary Index.

/pdf/image-registration-for-remote-sensing-1t6sc8vidy.pdf

Image registration for remote sensing

Due to the increasing amount and diversity of remotely sensed data, image registration is becoming one of the most important issues in remote sensing. In the near future, remote sensing systems will provide large amounts of data representing multiple- time or simultaneous observations of the same features by different sensors. The combination of data from coarse-resolution satellite sensors designed for large-area survey and from finer- resolution sensors for more detailed studies will allow better analysis of each type of data as well as validation of global low-resolution data analysis by the use of local high-resolution data analysis. This integration of information from multiple sources starts with the registration of the data. The most common approach to image registration is to choose, in both input image and reference image, some well-defined ground control points (GCPs), and then to compute the parameters of a deformation model. The main difficulty lies in the choice of the GCPs. In our work, a parallel implementation of decomposition and reconstruction by wavelet transforms has been developed on a single-instruction multiple-data (SIMD) massively parallel computer, the MasPar MP-1. Utilizing this framework, we show how maxima of wavelet coefficients, which can be used for finding ground control points of similar resolution remotely sensed data, can also form the basis of the registration of very different resolution data, such as data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and from the Landsat/Thematic Mapper (TM).

Parallel registration of multisensor remotely sensed imagery using wavelet coefficients

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard but are sometimes not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered and then performs least-squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, although approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared with wavelet features alone.

/pdf/automatic-image-registration-of-multimodal-remotely-sensed-5exsoyhhel.pdf

Jacqueline Le Moigne

Papers

A fast implementation of the isodata clustering algorithm

Efficient algorithms for robust feature matching

Image registration for remote sensing

Parallel registration of multisensor remotely sensed imagery using wavelet coefficients

Automatic Image Registration of Multimodal Remotely Sensed Data With Global Shearlet Features