Aircraft Image Recognition System Using Phase Correlation Method

Motion estimation techniques are widely used in today's video processing systems. The frequently used techniques are frequency-domain motion estimation methods, most notably phase correlation (PC). If the image frames are corrupted by Gaussian noises, then cross-correlation and related techniques do not work well. In this paper, however, we have studied this topic from a viewpoint different from the above. Our scheme is based on the bispectrum method for sub-pixel motion estimation of noisy image sequences. Experimental results show that our proposed method performs significantly better than PC technique.

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.

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.

The H.264 video coding standard exhibits high performance in terms of compression and image quality compared to the other existing standard such as H.263, MPEGX. This improved performance is achieved due to the mainly enormous computations in multiple mode motion estimation and compensation. Recent research tried to reduce the computational time using predictive motion estimation, early zero motion vector detection, fast motion estimation, fast mode decision etc. These approaches successfully reduce the computational time by degrading the image quality. Phase correlation technique is used to find the shift between two pictures. In this paper we used phase correlation technique to indicate the motion information between current and reference block and then we devise an algorithm to predict the motion estimation block size. Using phase correlation we are able to successfully predict the motion estimation mode directly instead of using exhaustive motion estimation by all possible modes, thus we save a huge amount of computational time. The experimental results show that we can save around 50% time in motion estimation without degrading the image quality.

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.

The use of 3D discrete orthogonal invariant moments as descriptors of images constitutes one of the hot topics in the field of 3D image analysis, especially in the recognition and classification of deformed objects, due to their high numerical precision and low computing complexity. The TSR (translation, scale and rotation) invariant functions of discrete orthogonal moments can be obtained by expressing them as a linear combination of the corresponding invariant of geometric moments. This classical method is very expensive in time due to the long time allocated to compute the polynomial coefficients when complex or large 3D images are used. This main drawback eliminates the use of these moments in the recognition and classification fields of 3D objects. In this article, we propose a fast and efficient method for calculating 3D discrete orthogonal invariant moments of Hahn, Tchebichef, Krawtchouk, Meixner and Charlier of 3D image. This proposed method is based on the extraction of the 3D discrete orthogonal invariant moments from the 3D geometric invariant moments of the entire 3D image, using the 3D image cuboid representation (ICR) strategy, where each cuboid is treated independently. The proposed method has the advantage of accelerating the process of extracting the 3D discrete invariant moments compared with the classical method. The performance of the proposed method is evaluated in terms of the calculation time and classification accuracy using a set of 3D images. Instead of some examples of using the discrete orthogonal invariant moments of Hahn, Tchebichef, Krawtchouk, Meixner and Charlier as pattern features for pattern classification are also provided.

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.

Frequency domain subpixel registration using HOG phase correlation

Purpose: To enhance the speed and accuracy of certain treatment plan in radiation therapy, a real‐time dose calculation strategy is performed based on Fast Fourier Transformation (FFT) and CT image registration. For this purpose, different registration methods are achieved by using Insight Segmentation and Registration Toolkit (ITK) in MATLAB. Method and materials: Our group developed an efficient method for kernel calculation for pencil beam convolution‐superposition based on FFT. In this paper, different image registration methods are developed in MATLAB by using ITK, such as rigid 3D, similarity 3D, Demons deformation and BSpline based Free Form. As ITK is written and used in a C++ template, smart pointer and generic structure, an open source wrapper MATITK is modified to facilitate the use of its image processing capabilities while working in the high‐level environment of MATLAB. The image registration methods are demonstrated on head and neck (H&N) CT image sets of 6 patients. The results of evaluation and visualization for the registration are presented. Results and discussion: Transformation similarity and computation complexity as evaluation criteria are compared between rigid and deformable registration methods. Judging from the mutual information histogram and mean square metric, the deformable registration will get more improvement on similarity than the rigid registration. Checkerboard composites of the reference and registered images are displayed. The computational time of deformable registration are 10∼20 times longer with the whole set of images because of the pre‐processing, complicated transformation matrix and more parameters. A similarity3D registration method is adopted for the image registration of H&N radiation therapy to trade off between registration effect and the time consumption. Conclusion: The image registration can greatly contribute to the automatic contour adjustment and dose deformation for adaptive radiation therapy. A GUI program will be designed for the customized deformation region to speed up deformable registration.

Image registration is a fundamental procedure in image processing that aligns two or more images of the same scene taken from different times, different viewpoints, or even different sensors. It is reasonable to orientate two images by matching corresponding pixels or regions that are considered identical. Based on this concept, this paper proposes a novel image registration method that applies the information theorem on intensity difference data. An entropy-based objective function is then developed according to the histogram of the intensity difference. The intensity difference represents the absolute gray-level difference of the corresponding pixels between the reference and sensed images over the overlapped region. The proposed registration method is to align the sensed image onto the reference image by minimizing the entropy of the intensity difference through iteratively updating the parameters of the similarity transformation. For performance evaluation, the proposed method is compared with the two exiting registration methods in terms of eight test image sets. The experiment is divided into two scenarios. One is to investigate the sensitivity (i.e., robustness) of the objective functions in these three different methods; the other is to verify the effectiveness of the proposed method. Through the experimental results, the proposed method is shown to be very effective in image registration and outperforms the other two methods over the test image sets.

In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.

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.

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.

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.

Image-based damage assessment requires processing and analysis of two images taken at the same location and at different times. For the registration of this type of image, the traditional method based on gray information is not applicable due to the change of the image itself. Image registration based on Fourier-Mellin Transform is a global and phase correlation method which is carried on Cartesian coordinate and Log-polar coordinate. This method extracts the extreme value of the impulse function of the image phase in the frequency domain, and obtains the transformation parameters of the image. In combination with the SIFT registration method, the effect of image registration with a large rotation angle is improved. Experiments show that the algorithm is effective and feasible. Based on the registration, the difference method is used to extract the image change area. The adaptive threshold method is used to preliminarily determine the interested targets, which provides a basis for the establishment of the damage model in the next step.