This paper proposes a novel method to estimate non-integer shift of images based on least squares approximation in the phase region. Conventional methods based on Phase Only Correlation (POC) take correlation between an image and its shifted image, and then estimate the non-integer shift by fitting the model equation. The problem when estimating using POC is that the estimated peak of the fitted model equation may not match the true peak of the POC function. This causes error in non-integer shift estimation. By calculating the phase difference directly in the phase region, the proposed method allows the estimation of sub-pixel shift through least squares approximation. Also by utilizing the characteristics of natural images, the proposed method limits adoption range for least squares approximation. By these improvements, the proposed method achieves high accuracy, and we validate through some examples.

This paper presents a method for registering retinal images. Retinal image registration is crucial for the diagnoses and treatments of various eye conditions and diseases such as myopia and diabetic retinopathy. Retinal image registration is challenging because the images have non-uniform contrasts and intensity distributions, as well as having large homogeneous non-vascular regions. This paper provides a new retinal image registration method by effectively combining expectation maximization principal component analysis based mutual information (EMPCA-MI) with salient features. Experimental results show that our method is more efficient and robust than the conventional EMPCA-MI method.

Registration consistency (RC) stands out as a widely-used automatic measure from existing image registration evaluation measures. However the original RC neglects the influence brought by the image intensity variation, leading to several problems. This letter proposes a rectified registration consistency, which takes both image intensity variation and geometrical transformation into consideration. Therefore the geometrical transformation is evaluated more by decreasing the influence of intensity variation. Experiments on real image pairs demonstrated the superiority of the proposed measure over the original RC.

Gaussian mixture model (GMM) has recently been applied for image registration given its robustness and efficiency. However, in previous GMM methods, all the feature points are treated identically. By incorporating local class features, this letter proposes a multiple Gaussian mixture models (M-GMM) method for image registration. The proposed method can achieve higher accuracy results with less registration time. Experiments on real image pairs further proved the superiority of the proposed method.

This paper presents a faster one-dimensional (1-D) phase-only correlation (POC)-based method for estimations of translations, rotation, and scaling in images. The proposed method is to project two-dimensional (2-D) images horizontally and vertically onto 1-D signals, and uses 1-D POCs of the 1-D signals to estimate the translations in images. Combined with a log-polar transform, the proposed method is extended to scaling and rotation estimations. Compared with conventional 2-D and 1-D POC-based methods, the proposed method performs in a lower computational cost. Experimental results demonstrate that the proposed method is capable of estimating large translations, rotation and scaling in images, and its accuracy is comparable to those of the conventional POC-based methods. The experimental results also show that the computational cost of the proposed method is much lower than those of the conventional POC-based methods.

The time efficiency of aerial video stitching is still an open problem due to the huge amount of input frames, which usually results in prohibitive complexities in both image registration and blending. In this paper, we propose an efficient framework aiming to stitch aerial videos in real time. Reasonable distortions are allowed as a tradeoff for acceleration. Instead of searching for globally optimized solutions, we directly refine frame positions with sensor data to compensate for the accumulative error in alignment. A priority scan method is proposed to select pixels within overlapping area into the final panorama for blending, which avoids complicated operations like weighting or averaging on pixels. Experiments show that our method can generate satisfying results at very competitive speed.

Accurate registration is crucial for medical image analysis. In this letter, we proposed an improved Demons technique (IDT) for medical image registration. The IDT improves registration quality using orthogonal gradient information. The advantage of the proposed IDT is assessed using 14 medical image pairs. Experimental results show that the proposed technique provides about 8% improvement over existing Demons-based techniques in terms of registration accuracy.

Image registration is an important topic in medical image analysis. It is usually used in 2D mosaics to construct the whole image of a biological specimen or in 3D reconstruction to build up the structure of an examined specimen from a series of microscopic images. Nevertheless, owing to a variety of factors, including microscopic optics, mechanisms, sensors, and manipulation, there may be great differences between the acquired image slices even if they are adjacent. The common differences include the chromatic aberration as well as the geometry discrepancy that is caused by cuts, tears, folds, and deformation. They usually make the registration problem a difficult challenge to achieve. In this paper, we propose an efficient registration method, which consists of a feature-based registration approach based on analytic robust point matching (ARPM) and a refinement procedure of the feature-based Levenberg-Marquardt algorithm (FLM), to automatically reconstruct 3D vessels of the rat brains from a series of microscopic images. The registration algorithm could speedily evaluate the spatial correspondence and geometric transformation between two point sets with different sizes. In addition, to achieve subpixel accuracy, an FLM method is used to refine the registered results. Due to the nonlinear characteristic of FLM method, it converges much faster than most other methods. We evaluate the performance of proposed method by comparing it with well-known thin-plate spline robust point matching (TPS-RPM) algorithm. The results indicate that the ARPM algorithm together with the FLM method is not only a robust but efficient method in image registration.

Image sequence registration has attracted increasing attention due to its significance in image processing and computer vision. In this paper, we put forward a new kernel based image registration approach, combining both feature-based and intensity-based methods. The proposed algorithm consists of two steps. The first step utilizes feature points to roughly estimate a motion parameter between successive frames; the second step applies our kernel based idea to align all the frames to the reference frame (typically the first frame). Experimental results using both synthetic and real image sequences demonstrate that our approach can automatically register all the image frames and be robust against illumination change, occlusion and image noise.

The problem of recovering a high-resolution frame from a sequence of low-resolution frames is considered. In general, video frames cannot be related through global parametric transformation due to the arbitrary individual pixel movement between frame pairs. To overcome this problem, we propose to employ region-matching technique for motion estimation with a modified model for frame alignment. To do that, the reference frame is segmented into arbitrary-shaped regions which are further matched with that of the other frames. Then, the frame alignment is accomplished by optimizing the cost function that consists of L1-norm of the difference between the interpolated low-resolution (LR) frames and the simulated LR frames. The experimental results demonstrate that using region matching in motion estimation step with the modified alignment model works better than other motion models such as affine, block matching, and optical flow motion models.

This paper presents a technique for high-accuracy estimation of image rotation using 1D Phase-Only Correlation (POC). The rotation angle between two images is estimated as follows: (i) compute the amplitude spectra of the given images, (ii) transform the coordinate system of amplitude spectra from Cartesian coordinates to polar coordinates, and (iii) estimate the translational displacement between the polar-mapped amplitude spectra to obtain the rotation angle. While the conventional approach is to employ 2D POC for high-accuracy displacement estimation in (iii), this paper proposes the use of 1D POC with an adaptive line selection scheme. The proposed technique makes possible to improve the accuracy of rotation estimation for low contrast images of artificial objects with regular geometric shapes and to reduce the total computation cost by 50%.

In mass disasters such as earthquakes, fire disasters, tsunami, and terrorism, dental records have been used for identifying victims due to their processing time and accuracy. The greater the number of victims, the more time the identification tasks require, since a manual comparison between the dental radiograph records is done by forensic experts. Addressing this problem, this paper presents an efficient dental radiograph recognition system using Phase-Only Correlation (POC) for human identification. The use of phase components in 2D (two-dimensional) discrete Fourier transforms of dental radiograph images makes possible to achieve highly robust image registration and recognition. Experimental evaluation using a set of dental radiographs indicates that the proposed system exhibits efficient recognition performance for low-quality images.

Image Registration can be seen as an optimization problem to find a cost function and then use an optimization method to get its minimum. Normalized mutual information is a widely-used robust method to design a cost function in medical image registration. Its calculation is based on the joint histogram of the fixed and transformed moving images. Usually, only a discrete joint histogram is considered in the calculation of normalized mutual information. The discrete joint histogram does not allow the cost function to be explicitly differentiated, so it can only use non-gradient based optimization methods, such as Powell's method, to seek the minimum. In this paper, a parzen-window based method is proposed to estimate the continuous joint histogram in order to make it possible to derive the close form solution for the derivative of the cost function. With this help, we successfully apply the gradient-based optimization method in registration. We also design a new kernel for the parzen-window based method. Our designed kernel is a second order polynomial kernel with the width of two. Because of good theoretical characteristics, this kernel works better than other kernels, such as a cubic B-spline kernel and a first order B-spline kernel, which are widely used in the parzen-window based estimation. Both rigid and non-rigid registration experiments are done to show improved behavior of our designed kernel. Additionally, the proposed method is successfully applied to a clinical CT-MR non-rigid registration which is able to assist a magnetic resonance (MR) guided microwave thermocoagulation of liver tumors.

This paper describes a modified ICP registration system of facial point data with range-scanning equipment for medical Augmented Reality applications. The reference facial point data are extracted from the pre-stored CT images; the floating facial point data are captured from range-scanning equipment. A modified soft-shape-context ICP including an adaptive dual AK-D tree for searching the closest point and a modified shape-context objective function is used to register the floating data to reference data to provide the geometric relationship for a medical assistant system and pre-operative training. The adaptive dual AK-D tree searches the closest-point pair and discards insignificant control coupling points by an adaptive distance threshold on the distance between the two returned closest neighbor points which are searched by using AK-D tree search algorithm in two different partition orders. In the objective function of ICP, we utilize the modified soft-shape-context information which is one kind of projection information to enhance the robustness of the objective function. Experiment results of using touch and non-touch capture equipment to capture floating point data are performed to show the superiority of the proposed system.

We propose a unified view to deal with two formulations of image distortion and a method for estimating the distortion parameters for both of the formulations; So far the formulations have been developed separately. The proposed method is based on image registration and consists of nonlinear optimization to estimate parameters including view change and radial distortion. Experimental results demonstrate that our approach can deal with the two formulations simultaneously.

This paper presents a high-accuracy image registration technique using a Phase-Only Correlation (POC) function. Conventional techniques of phase-based image registration employ heuristic methods in estimating the location of the correlation peak, which corresponds to image displacement. This paper proposes a technique to improve registration performance by fitting the closed-form analytical model of the correlation peak to actual two-dimensional numerical data. This method can also be extended to a spectrum weighting POC technique, where we modify cross-phase spectrum with some weighting functions to enhance registration accuracy. The proposed method makes possible to estimate image displacements with 1/100-pixel accuracy.

We propose a method for camera calibration based on image registration. This method registers two images; one is a real image captured by a camera with a calibration object with known shape and texture, and the other is a synthetic image containing the object. The proposed method estimates the parameters of the rotation and translation of the object by using the depth information of the synthetic image. The Gauss-Newton method is used to minimize the residuals of intensities of the two images. The proposed method does not depend on initial values of the minimization, and is applicable to images with much noise. Experimental results using real images demonstrate the robustness against initial state and noise on the image.

We propose a method for compensating distortion of image by calibrating intrinsic camera parameters by image registration which does not need point-to-point correspondence. The proposed method divides the registration between a calibration pattern and a distorted image observed by a camera into two steps. The first step is the straightforward registration from the pattern in order to correct the displacement due to projection. The second step is the backward registration from the observed image for compensating the distortion of the image. Both of the steps use Gauss-Newton method, a nonlinear optimization technique, to minimize residuals of intensities so that the pattern and the observed image become the same. Experimental results show the usefulness of the proposed method. Finally we discuss the convergence of the proposed method which consists of the two registration steps.

This paper proposes an automatic method for reconstructing a realistic 3D facial image from CT (computer tomography) and three color photographs: front, left and right views, which can be linked easily with the underlying bone and soft tissue models. This work is the first part of our final goal, "the prediction of patient's facial appearance after cancer surgery" such as removal of a part of bone or soft tissues. The 3D facial surface derived from CT by the marching cubes algorithm is obviously colorless. Our task is to add the color texture of the same patient actually taken with a digital camera to the colorless 3D surface. To do this it needs an accurate registration between the 3D facial image and the color photograph. Our approach is to set up a virtual camera around the 3D facial surface to register the virtual camera images with the corresponding color photographs by automatically adjusting seven parameters of the virtual camera. The camera parameters consists of three rotations, three translations and one scale factor. The registration algorithm has been developed based upon Besl and McKay's iterative closest point (ICP) algorithm.

A method for reconstructing realistic 3D human faces from computer tomography images and color photographs is proposed in this paper. This can be linked easily with the underlying bone and soft tissue models. An iteration algorithm has been developed for automatically estimating the virtual camera parameters to match the projected 3D CT image with 2D color photographs using known point correspondence. An approach has been proposed to select landmarks using a mouse with minimum error. Six landmarks from each image have been selected for front face matching and five for each side face matching.