FFT-based estimation of large motions in images: a robust gradient-based approach

A fast and robust gradient-based motion estimation technique which operates in the frequency domain is presented. The algorithm combines the natural advantages of a good feature selection offered by gradient-based methods with the robustness and speed provided by FFT-based correlation schemes. Experimentation with real images taken from a popular database showed that, unlike any other Fourier-based techniques, the method was able to estimate translations, arbitrary rotations and scale factors in the range 4-6

FFT-based estimation of large motions in images: a robust gradient-based approach

A fast and robust gradient-based motion estimation technique which operates in the frequency domain is presented. The algorithm combines the natural advantages of a good feature selection offered by gradient-based methods with the robustness and speed provided by FFT-based correlation schemes. Experimentation with real images taken from a popular database showed that, unlike any other Fourier-based techniques, the method was able to estimate translations, arbitrary rotations and scale factors in the range 4-6

Estimation of Large Scalings in Images Based on Multilayer Pseudopolar Fractional Fourier Transform

Accurate estimation of the Fourier transform in log-polar coordinates is a major challenge for phase-correlation based motion estimation. To acquire better image registration accuracy, a method is proposed to estimate the log-polar coordinates coefficients using multilayer pseudopolar fractional Fourier transform (MPFFT). The MPFFT approach encompasses pseudopolar and multilayer techniques and provides a grid which is geometrically similar to the log-polar grid. At low coordinates coefficients the multilayer pseudopolar grid is dense, and at high coordinates coefficients the grid is sparse. As a result, large scalings in images can be estimated, and better image registration accuracy can be achieved. Experimental results demonstrate the effectiveness of the presented method

Three-dimensional alignment and merging of confocal microscopy stacks

pre-printWe describe an efficient, robust, automated method for image alignment and merging of translated, rotated and flipped confocal microscopy stacks. The samples are captured in both directions (top and bottom) to increase the SNR of the individual slices. We identify the overlapping region of the two stacks by using a variable depth Maximum Intensity Projection (MIP) in the z dimension. For each depth tested, the MIP images gives an estimate of the angle of rotation between the stacks and the shifts in the x and y directions using the Fourier Shift property in 2D. We use the estimated rotation angle, shifts in the x and y direction and align the images in the z direction. A linear blending technique based on a sigmoidal function is used to maximize the information from the stacks and combine them. We get maximum information gain as we combine stacks obtained from both directions

Tools for creating wide-field views of the human retina using Optical Coherence Tomography

Optical Coherence Tomography (OCT) has allowed in-vivo viewing of details of retinal layers like never before. With the development of spectral domain OCT (SD-OCT) details of nearly 2µm axial resolution and higher imaging speed have been reported. Nevertheless, a single volume scan of the retina is typically restricted to 6mm x 6mm in size. Having a larger field of view of the retina will definitely enhance the clinical utility of the OCT. A tool was developed for creating wide-field thickness maps of the retina by combining the use of already available tools like i2k Retina (DualAlign, LLC, Clifton Park, NY) and the thickness maps from Cirrus HD-OCT research browser (Carl Zeiss Meditec, Dublin, California, USA). Normal subjects (n=20) were imaged on Zeiss Cirrus HD-OCT using 512x128 Macular Cube scanning protocol. Sixteen overlapping volumetric images were obtained by moving the internal fixation target around such that the final stitched maps were 12mm x 14mm in size. The thickness maps were corrected for inter-individual differences in axial lengths measured using Zeiss IOL Master and averaged to obtain a normative map. An algorithm was also developed for montaging 3-D volume scans. Using this algorithm two OCT volume scans can be registered and stitched together to obtain a larger volume scan. The algorithm can be described as a two step process involving 3-D phase-correlation and 2-D Pseudo-polar Fourier transform (PPFT). In the first step, 3-D phase-correlation provides translation values in the x, y and z axis. The second step involves applying PPFT on each overlapping pair of B-scans to find rotation in the x-y plane. Subsequent volumes can be stitched to obtain a large field of view. We developed a simple and robust method for creating wide-field views of the retina using existing SD-OCT hardware. As segmentation algorithms improve, this method could be expanded to produce wide-field maps of retinal sub-layers, such as the outer nuclear layer or retinal nerve fiber layer. These wide-field views of the retina may prove useful in evaluating retinal diseases involving the peripheral retina (e.g., retinitis pigmentosa and glaucoma)

2x1D Image Registration and Comparison

This paper presents a novel 2x1D phase correlation based image registration method for verification of printer emulator output. The method combines the basic phase correlation technique and a modified 2x1D version of it to achieve both high speed and high accuracy. The proposed method has been implemented and tested using images generated by printer emulators. Over 97% of the image pairs were registered correctly, accurately dealing with diverse images with large translations and image cropping

GPU Accelerated FFT-Based Registration of Hyperspectral Scenes

Registration is a fundamental previous task in many applications of hyperspectrometry. Most of the algorithms developed are designed to work with RGB images and ignore the execution time. This paper presents a phase correlation algorithm on GPU to register two remote sensing hyperspectral images. The proposed algorithm is based on principal component analysis, multilayer fractional Fourier transform, combination of log-polar maps, and peak processing. It is fully developed in CUDA for NVIDIA GPUs. Different techniques such as the efficient use of the memory hierarchy, the use of CUDA libraries, and the maximization of the occupancy have been applied to reach the best performance on GPU. The algorithm is robust achieving speedups in GPU of up to 240.6×This work was supported in part by the Consellería de Cultura, Educacion e Ordenación Universitaria under Grant GRC2014/008 and Grant ED431G/08 and in part by the Ministry of Education, Culture and Sport, Government of Spain under Grant TIN2013-41129-P and Grant TIN2016-76373-P. Both are cofunded by the European Regional Development Fund. The work of A. Ordóñez was supported by the Ministry of Education, Culture and Sport, Government of Spain, under an FPU Grant FPU16/03537S

Automatic 4-D Registration in Dynamic MR Renography Based on Over-complete Dyadic Wavelet and Fourier Transforms

Dynamic contrast-enhanced 4-D MR renography has the potential for broad clinical applications, but suffers from respiratory motion that limits analysis and interpretation. Since each examination yields at least over 10-20 serial 3-D images of the abdomen, manual registration is prohibitively labor-intensive. Besides in-plane motion and translation, out-of-plane motion and rotation are observed in the image series. In this paper, a novel robust and automated technique for removing out-of-plane translation and rotation with sub-voxel accuracy in 4-D dynamic MR images is presented. The method was evaluated on simulated motion data derived directly from a clinical patient's data. The method was also tested on 24 clinical patient kidney data sets. Registration results were compared with a mutual information method, in which differences between manually co-registered time-intensity curves and tested time-intensity curves were compared. Evaluation results showed that our method agreed well with these ground truth data

Estimation of Translation, Rotation, and Scaling between Noisy Images Using the Fourier–Mellin Transform

In this paper we focus on extended Euclidean registration of a set of noisy images. We provide an appropriate statistical model for this kind of registration problems, and a new criterion based on Fourier-type transforms is proposed to estimate the translation, rotation and scaling parameters to align a set of images. This criterion is a two step procedure which does not require the use of a reference template onto which aligning all the images. Our approach is based on M-estimation and we prove the consistency of the resulting estimators. A small scale simulation study and real examples are used to illustrate the numerical performances of our procedure

Statistical shape analysis in a Bayesian framework; The geometric classification of fluvial sand bodies.

We present a novel shape classification method which is embedded in the Bayesian paradigm. We focus on the statistical classification of planar shapes by using methods which replace some previous approximate results by analytic calculations in a closed form. This gives rise to a new Bayesian shape classification algorithm and we evaluate its efficiency and efficacy on available shape databases. In addition we apply our results to the statistical classification of geological sand bodies. We suggest that our proposed classification method, that utilises the unique geometrical information of the sand bodies, is more substantial and can replace ad-hoc and simplistic methods that have been used in the past. Finally, we conclude this work by extending the proposed classification algorithm for shapes in three-dimensions