An Improved Phase Correlation Method Based on 2-D Plane Fitting and the Maximum Kernel Density Estimator

When estimating the disparity of remote sensing images, known phase correlation (PC)-based disparity estimation methods are not fast and robust, such as hierarchical structure PC method and fixed window PC method. To tackle this problem, a parallel PC-based hierarchical framework is proposed, which includes two ideas: first, a weighted PC peak fitting algorithm is introduced for estimating the high precise disparity matrix efficiently and stably; second, a graphics processing unit-based parallel PC algorithm is integrated into the hierarchical framework for fast and robustly estimating high precise disparity map. Additionally, many stages of hierarchical framework, such as padding and reliable evaluation stages, are improved for improving the computational efficiency of disparity estimation system. In a large number of experiments, the results have shown that the efficiency of the proposed algorithm is on average 24 times faster than the compared state-of-the-art methods. Meanwhile, the precision of the proposed algorithm is also superior to or very close to the compared algorithms. The proposed algorithm has been successfully used in a unmanned aerial vehicle three-dimensional retrieval system, and the practice effect has also been verified.

Subpixel translation estimation using phase correlation is a fundamental task for numerous applications in the remote sensing community. The major drawback of the existing subpixel phase correlation methods lies in their sensitivity to corruption, including aliasing and noise, as well as the poor performance in the case of practical remote sensing data. This paper presents a novel subpixel phase correlation method using singular value decomposition (SVD) and the unified random sample consensus (RANSAC) algorithm. In the proposed method, SVD theoretically converts the translation estimation problem to one dimensions for simplicity and efficiency, and the unified RANSAC algorithm acts as a robust estimator for the line fitting, in this case for the high accuracy, stability, and robustness. The proposed method integrates the advantages of Hoge's method and the RANSAC algorithm and avoids the corresponding shortfalls of the original phase correlation method based only on SVD. A pixel-to-pixel dense matching scheme on the basis of the proposed method is also developed for practical image registration. Experiments with both simulated and real data were carried out to test the proposed method. In the simulated case, the comparative results estimated from the generated synthetic image pairs indicate that the proposed method outperforms the other existing methods in the presence of both aliasing and noise, in both accuracy and robustness. Moreover, the pixel locking effect that commonly occurs in subpixel matching was also investigated. The degree of pixel locking effect was found to be significantly weakened by the proposed method, as compared with the original Hoge's method. In the real data case, experiments using different bands of ZY-3 multispectral sensor-corrected images demonstrate the promising performance and feasibility of the proposed method, which is able to identify seams of the image stitching between sub-charge-coupled device units.

Videocapillaroscopy is a convenient and non-invasive method of blood flow parameters recovery in the capillaries. Capillaries position can vary at recorded video sequences due to the registration features of capillary blood flow. Stabilization algorithm of video capillary blood flow based on phase correlation is proposed and researched. This algorithm is compared to the known algorithms of video frames stabilization with full-frame superposition and with key points. Programs, based on discussed algorithms, are compared under processing the experimentally recorded video sequences of human capillaries and under processing of computer-simulated sequences of video frames with the specified offset. The full-frame superposition algorithm provides high quality of stabilization; however, the program based on this algorithm requires significant computational resources. Software implementation of the algorithm based on the detection of the key points is characterized by good performance, but provides low quality of stabilization for video sequences capillary blood flow. Algorithm based on phase correlation method provides high quality of stabilization and program realization of this algorithm requires minimal computational resources. It is shown that the phase correlation algorithm is the most useful for stabilization of video sequences for capillaries blood flow. Obtained findings can be used in the software for biomedical diagnostics.

This article describes the aircraft image recognition system implemented using the phase correlation technique in Matlab environment. The phase correlation is computed by using the normalized cross-power spectrum between the database and the template test image. The main objective of this article is to develop methods for static analysis of aircraft images. An unknown fighter aircraft is recognized by comparing its static image with those from a database of images of aircraft. This work is a research initiative involving the use of image processing techniques to detect three-dimensional (3D) aircraft object based on their 2D images, providing feedback information for strategic purposes. The phase correlation technique is found to give a better recognition result for the set of database and test images considered, compared with the invariant moments. The phase correlation method is also used in other areas such as image registration. Aircraft images used include those from AeroIndia 2011 held at Bangalore, India.

Analyzing and interpreting of thermograms have been increasingly employed in the diagnosis and monitoring of diseases thanks to its non-invasive, non-harmful nature and low cost. This paper presents a thermal image analysis system based on image registration for morphoea disease diagnosis. A novel system is proposed to improve the diagnosis and monitoring of morphoea based on integration with the published lines of Blaschko. In this application, image registration based on global and local registration methods are found inevitable. A modified normalized gradient cross-correlation (NGC) method to reduce large geometrical differences between two multimodal images of different subjects that are represented by smooth gray edge maps is proposed for the global registration approach. It is shown in this paper that the NGC method outperforms phase correlation (PC) method by a lower rate of misregistration. This demonstrates that by using the gradients of the gray edge maps, the performance of the PC based image registration method can be greatly improved.

Frequency domain subpixel registration using HOG phase correlation

We study an experimental setup to perform pattern recognition by correlation methods. This setup implements the Pure Phase Correlation (PPC) method, let us say, it only takes into account the phase distributions of the Fourier transform of the scene and the target. An experimental study of a PPC method is presented. A phase measurement interferometric technique is used to extract the phase of the Fourier transform of the correlation function. Some aspects related to the intermodulation are examined. Experimental PPC with improved discrimination capability obtained with binary masks is also considered. The experiments are carried out with segmented scenes and with multiobject scenes.

The relationship between visual comfort and parallax distribution for stereoscopic HDTV has been studied. In this study, we first examined a method for measuring this parallax distribution. As it is important to understand the characteristics of the distribution in a frame or temporal changes of the characteristics, rather than having detailed information on the parallax at every point, we propose a method to measure the parallax based on the phase correlation. It includes a way of reducing the measurement error depending on the phase correlation method. The method was used to measure stereoscopic HDTV images with good results. Secondly, we conducted a subjective evaluation test of visual comfort and sense of presence using 48 different stereoscopic HDTV pictures, and compared the results with the parallax distributions in these pictures measured by the proposed method. The comparison showed that the range of parallax distribution and the average parallax distribution significantly affect visual comfort when viewing stereoscopic HDTV images. It is also suggested that the range of parallax distribution in many of the images that were judged comfortable to view is located within approximate 0.3Diopter.

This paper shows that the accuracy of azimuth angle measurement for an interferometric localization system used to locate tags in its Line-of-Sight (LoS) can be improved by exploiting Impulse Radio-Ultra WideBand (IR-UWB) signals and without increasing the frequency bandwidth. This solution uses a Phase Correlation (PC) method, initially applied for Continuous Wave (CW) signals, adapted for Ultra WideBand (UWB) pulse signals. The obtained results are compared to those computed by a classical Energy Detection (ED) method where it becomes impossible to estimate azimuth angles for tag positions close to the orthogonal centered axis of the localization system baseline.

In this paper, signal processing techniques are employed for electromagnetic sub-wavelength imaging. Three signal processing algorithms are investigated, including phase conjugation (PC), correlation method (CM) and maximum search and removal method (MSRM).

Correlation-extremal navigation and guidance systems are widely used in controlling the moving objects. The paper considers the aircraft correlation-extreme navigation algorithm designed for the aviation complexes. This algorithm provides radio identification and guidance for long-range detection of the enemy objects, as well as guidance of the destruction and interception weaponry against it. Classification of methods of the correlation-extreme navigation is presented, and each of the methods is being considered separately. Using the example of an airfield identified from the air, the error between the current and the reference images is determined as the distance between two points in the original image pixel coordinate system. When moving, the object image could be distorted by geometric, luminance and noise transformations. Using the found image, error dependencies on alterations in brightness, rotation angle and noise level of the image fragment, as well as the error dependencies on the selected navigation element, are investigated. Results are presented in the form of graphs. It is shown that the phase correlation method is more resistant to brightness and noise distortions of the image fragment, and the gradient correlation method is more resistant to the geometric transformations. It was found that navigation of heterogeneous and diverse navigation elements (cities) is better than the monotonous and homogeneous ones (roads and forests).

For pt.II see ibid., vol.16, no.16, p.2871 (1983). The circular orbit method (COM) which has been proposed in parts I and II is extended to coupled oscillator systems. The authors also develop a new method (they call it the phase correlation method (PCM)) which enables one to calculate a particular state manifold. They apply the COM and PCM to calculations of state energy in the Henon-Heiles and Barbanis potential systems and examine the efficiency of them.

We present a new image registration algorithm that employs Phase Correlation Method (PCM) to align rotated and translated 3D images. First, we use PCM and cylindrical coordinate transform to estimate rotation angle that aligns two images that are mutually rotated around known axis. An improvement of this technique is given to eliminate influence of noise and image differences in non-ideal conditions. Finally, an iterative optimization procedure is proposed which uses these techniques in rigid body registration tasks. We study the performance of the algorithm in simulated experiment on MRI brain image.KeywordsImage RegistrationPhase CorrelationImage Registration MethodImage Registration AlgorithmCorrelation SurfaceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Sub-pixel registration is critical in object tracking and image super-resolution. Motion segmentation algorithms using the gradient can be applied prior to image registration to improve its accuracy and computational runtime. This paper proposes a new segmentation method that is adaptive variation segmentation in the form of local variances taken at different block sizes to be applied to the sum of absolute image differences. In this paper, two motion segmentation and four image registration methods are tested to optimize the registration accuracy in visible and thermal imagery. Two motion segmentation methods, flux tensor and adaptive variation segmentation, are quantitatively tested by comparing calculated regions of movement with accepted areas of motion. Four image registration methods, including two optical flow, feature correspondence, and correlation methods, are tested in two steps: gross shift and sub-pixel shift estimations. Gross shift estimation accuracy is assessed by comparing estimated shifts against a ground truth. Sub-pixel shift estimation accuracy is assessed by simulated, down-sampled images. Evaluations show that the best segmentation results are achieved using either the flux tensor or adaptive segmentation methods. For well-defined objects, feature correspondence and correlation registration produce the most accurate gross shift registrations. For not well-defined objects, the correlation method produces the most accurate gross and sub-pixel shift registration.

Biometric technology deals with recognizing the uniqueness of individuals based on their exclusive Physical or behavioral characteristics. The developments in science & technology have made it possible to use biometrics in application when it is required to establish (or) confirm the identity of persons. Amongst a range of method, Iris recognition is a hastily intensifying technique of biometric technology. A large range of identification methods is used either verify or decide the identity of person request their processes. This Paper provides the inspiration for image acquisition, segmentation, normalization standed on the human being Iris imaging. A combination method is used to fragment the eye's digital image. The mined Iris region was then standardized by using Image registration method. A phase correlation method is installed for this Iris image registration principle. Investigational consequences demonstrate that our system is reasonably efficient and gives hopeful performance.