A Visually Inspired Variational Method for Automatic Image Registration

A method for automatic image registration which is characterized by its insensitivity to scaling, rotation, and intensity changes is described. The method is based on similarity assessment of the structures in the images and on a check of their spatial arrangement. Pairs of structures that correspond to each other provide sets of control points to geometric mapping functions. An application of the method to remote-sensing image alignment with a reference map is presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Automatic image registration is important for many multiframe-based image analysis applications. With an increasing number of images collected every day from different sensors, automated registration of multi-sensor/multi-spectral images has become an important issue. A wide range of registration techniques exists for different types of applications and data sources, however no algorithm is known that can accurately register multi-source images consistently. This research addresses this problem by investigating the development of a fully automatic registration system for remote sensing images. The development of this new automatic image registration method is based on the extraction and matching of common features that are visible in both images. The algorithm involves the following five steps: noise removal, edge extraction, edge linking pattern extraction and pattern matching.

Multisensor systems with optical 3D sensors are frequently employed to capture complete surface information by measuring workpieces from different views. During coarse and fine registration the resulting datasets are afterward transformed into one common coordinate system. Automatic fine registration methods are well established in dimensional metrology, whereas there is a deficit in automatic coarse registration methods. The advantage of a fully automatic registration procedure is twofold: it enables a fast and contact-free alignment and further a flexible application to datasets of any kind of optical 3D sensor. In this paper, an algorithm adapted for a robust automatic coarse registration is presented. The method was originally developed for the field of object reconstruction or localization. It is based on a segmentation of planes in the datasets to calculate the transformation parameters. The rotation is defined by the normals of three corresponding segmented planes of two overlapping datasets, while the translation is calculated via the intersection point of the segmented planes. First results have shown that the translation is strongly shape dependent: 3D data of objects with non-orthogonal planar flanks cannot be registered with the current method. In the novel supplement for the algorithm, the translation is additionally calculated via the distance between centroids of corresponding segmented planes, which results in more than one option for the transformation. A newly introduced measure considering the distance between the datasets after coarse registration evaluates the best possible transformation. Results of the robust automatic registration method are presented on the example of datasets taken from a cutting tool with a fringe-projection system and a focus-variation system. The successful application in dimensional metrology is proven with evaluations of shape parameters based on the registered datasets of a calibrated workpiece.

The geometric correction of images under the scope of remote sensing applications is still mostly a manual work. This is a time and effort consuming task associated with an intra- and inter-operator subjectivity. One of the main reasons may be the lack of a proper evaluation of the different available automatic image registration (AIR) methods, since some of them are only adequate for certain types of applications/data. In order to fulfill a gap in this context, a first reference dataset of pairs of images comprising some types of geometric distortions was created, different spatial and spectral resolutions, and divided according to the Level 1 of CORINE Land Cover nomenclature (European Environment Agency). This dataset will allow for gaining perception of the abilities and limitations of some AIR methods. Some AIR methods were evaluated in this work, including the traditional correlation-based method and the SIFT approach, for which a set of measures for an objective evaluation of the geometric correction process quality was computed for every combination of pair of images/AIR method. The reference dataset is available from an internet address, being expected that it becomes a channel of interaction among the remote sensing community interested in this field.

Fine registration is a fundamental step for further application of remote sensing images. Focused on deficiencies in traditional manual registration, this paper presents a new method for automatic fine registration of multi-spectral images. To make the most of image information, the algorithm detects and matches feature points in the selected bands. Then pick up the common control points which contain more reliability relative to others after eliminating wrong matching points. The last registration model can be built based on common control points and the points selected by common ones. Experimental results with Landsat TM5 images demonstrate that the method is more accurate and suitable for automatic batch processing.

In this paper, we propose an automatic and accurate image registration method for the high resolution. Due to the strong distortion caused by the terrain relief in this kind of images, it cannot be resolved by one set of transformation coefficients for the whole image. So the method mainly consists of two parts: one part is the dense feature point matching, and the other is the faced based differential registration. The matching algorithm integrates the feature point matching, relaxation optimization technique, the Least Square Matching, the coarse-to-fine strategy, and it can provide hundreds of thousands of reliable and accurate control points. With the TIN, these points divide the image into a lot of small triangles. For each triangle, we can assume the local distortion is simple and can be depicted by the affine transformation function. Finally, faced based differential registration is performed to resample the slave image. Experiments have been carried out and satisfactory results have been obtained.

Radiation therapy of prostate cancer requires a high geometric accuracy of dose delivery. Unfortunately, this accuracy can be hardly obtained because of imperfect patients repositioning throughout the preparation and execution of the treatment. In order to correct for such errors we developed methods for online registration of portal images (PI) and digitally reconstructed radiographs (DRR). To improve the preconditions for registration of the Pis and DRRs image preprocessing, such as contrast-limited adaptive histogram equalization (CLAHE) of the Pis, noise and edge filtering of the Pis and the DRRs, has been performed. Then, similarity measures as mean square error, cross correlation and mutual information were investigated. A fully automatic bony-based registration method has been developed. As a reference method a semi-automatic stereometric fiducial-based registration has also been developed. This method uses gold seeds implanted into the prostate as internal fiducial markers for assessment of the similarity measures. Small lead spheres as well were adhered as external fiducial markers on the Alderson phantom. This method was also used for the verification of the automatic bony-based registration. The registration of repositioning was then performed fully automatically in less than 30 s with the Alderson phantom and with the patient data. At the present stage, there remains a registration error of less than 2 mm.

SAR images registration is an important step in SAR interferometry technology,a sub-pixel accuracy of the registration step is necessary to obtain a phase interferogram that truly reflects the elevation or deformation of the target on the ground.This paper introduces the basic conception of external DEM based SAR image registration method and gives an analytical expression of sensitivity analysis in respect of DEM error effect.The paper realizes the processing flow of SAR registration method and implements the sub-pixel precision registration of SAR images.The ERS datum is used as validation,and the results show that 1% pixels registration accuracy can be archieved by using precise orbit and SRTM as external DEM,this image registration method is especially useful in the situation of big temporal baseline and large geometric baseline.

UAV remote sensing is low cost and convenient, but traditional methods for image registration and mosaic are low efficient, since it is hard to find ground control points for large quantity of images with small scale. In this study, adjacency transfer algorithm is used between every two adjacent images to automate the work flow: First SIFT(Scale-invariant feature transform) algorithm is employed to detect correspondence points, then polynomial algorithm is used for geometric correction, and finally all the digital orthophoto images are mosaiced into one image. Developed in VC program environment, this study builds its prototype system, which can produce digital orthophoto images with relative high efficiency. Through image overlay and visual interpretation, no image displacement was found. Adjacency transfer algorithm used in this study provides a new way for efficienct and automatic UAV image registration and mosaic.

As the need for automating registration techniques is recognized, the authors feel that there is a need to survey all the registration methods which may be applicable to Earth and space science problems and to evaluate their performances on a large variety of existing remote sensing data as well as on simulated data of soon-to-be-flown instruments. The authors present the first steps towards this quantitative evaluation: a few automatic image registration algorithms are described and first results of their evaluation are presented for three different datasets.

Attenuation correction of PET activation studies in the presenceof task-related motion

Common feature representation in optical and synthetic aperture radar (SAR) image registration is one of the most challenging tasks due to the significant geometric and radiometric differences. This letter proposed a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prominent structure-guided feature (PSGF)</i> representation for SAR and optical image registration. Firstly, the prominent structure of the image is highlighted based on windowed inherent variations, which is conducive to identifying more accurate and reliable corresponding points. Secondly, the maximum response index filter banks are proposed to extract structure features with multi-orientation convolution results. Then the structure feature-guided representation generated from this filtering map is quantized in histograms. Finally, the descriptor with radiation invariance is employed for feature matching, enabling automatic image registration with high accuracy. Comparative analysis with state-of-the-art methods on diverse terrain data demonstrates the superiority of the proposed PSGF method for SAR and optical image registration.

Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM) to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.

A new method of automatic multisensor image registration based on contour description and contour matching is proposed. It first employs wavelet descriptor moments to represent the contour. Second, a contour matching criteria is presented based on a principle of minimum distance classifier; algorithms of contour matching are given according to closed and unclosed contours. Finally, control points are located according. to matched contours, and consistency checking is employed to remove mismatched control points. The image transformation model and parameter estimation using the rule of least root mean-square error (RMSE) are discussed. The method is accurate, robust and fast, and has been tested on many images. Two experimental results are presented.

Objective To evaluate the effect of setup errors on the 2D image projection and image registration, and then propose an improved registration method based on mutual information. Methods An anthropomorphic head phantom was used to simulate the rotational and translational setup errors. The geometric disparities were reflected by the changes of mutual information. Known setup errors were intentionally introduced to twenty cases divided into two groups demarcated by 3 mm translation error and 3° rotation error: ten cases with larger errors and ten with smaller errors. Then the anterior-posterior and lateral portal images were captured by the electronic portal imaging device (EPID), based on which the setup errors were calculated using two mutual information registration method respectively: the vender provided one, and the improved method as proposed. The calculated errors were compared with the actual setup errors to evaluate robustness of the method. Results For the ten cases with smaller setup errors, the average translational registration disparities using the conventional method were 0.3, 0.4, and 0.3 mm in x, y and z directions respectively. The rotational disagreements were 0.4° in both x and z directions. The average time consumption was 28.7 s. The corresponding discrepancies analyzed using the improved method were 0.3, 0.4, 0.3 mm, 0.5° and 0.4°, respectively. On average, 31.1 s was needed for registration. For the ten cases with larger setup errors, the mean disparities of the conventional method were 0.9, 0.7, 0.8 mm, 0.9° and 0.8°, 29.9 s taken on average. The corresponding result of the improved method was 0.5, 0.4, 0.5 mm, 0.6° and 0.5°, 33.2 s taken on average. Conclusions Regarding smaller setup errors, the two methods showed little difference and both had good performance in image registration accuracy. For larger setup errors, however, the improved mutual information registration method exhibited significantly higher accuracy than the conventional method, at cost of clinically acceptable registration time. Key words: Setup errors; Electronic portal imaging device; Portal image; Image registration