Showing papers on "Phase correlation published in 2018"

Accurate and fiducial-marker-free correction for three-dimensional chromatic shift in biological fluorescence microscopy

Abstract: Correction of chromatic shift is necessary for precise registration of multicolor fluorescence images of biological specimens. New emerging technologies in fluorescence microscopy with increasing spatial resolution and penetration depth have prompted the need for more accurate methods to correct chromatic aberration. However, the amount of chromatic shift of the region of interest in biological samples often deviates from the theoretical prediction because of unknown dispersion in the biological samples. To measure and correct chromatic shift in biological samples, we developed a quadrisection phase correlation approach to computationally calculate translation, rotation, and magnification from reference images. Furthermore, to account for local chromatic shifts, images are split into smaller elements, for which the phase correlation between channels is measured individually and corrected accordingly. We implemented this method in an easy-to-use open-source software package, called Chromagnon, that is able to correct shifts with a 3D accuracy of approximately 15 nm. Applying this software, we quantified the level of uncertainty in chromatic shift correction, depending on the imaging modality used, and for different existing calibration methods, along with the proposed one. Finally, we provide guidelines to choose the optimal chromatic shift registration method for any given situation.

A Novel Image Registration Method Based on Phase Correlation Using Low-Rank Matrix Factorization With Mixture of Gaussian

Abstract: Image registration is a critical process for the various applications in the remote sensing community, and its accuracy greatly affects the results of the subsequent applications. Image registration based on phase correlation has been widely concerned due to its robustness to gray differences and efficiency. After calculating the normalized cross-relation matrix $Q$ , the most commonly used approach is fitting the 2-D phase plane that passes through the origin, but it needs to remove contaminated spectrum carefully and the corresponding parameters are empirical. In fact, the phase correlation matrix is rank one for a noise-free translation model. This property simplifies the matching problem to finding the best rank-one approximation of the normalized cross-relation matrix. We develop a novel algorithm that performs the rank-one matrix factorization on the phase correlation matrix by assuming its noise as mixture of Gaussian (MoG) distributions. The MoG model is a general approximator for any continuous distribution, and hence is able to model a wide range of noise distribution. The parameters of the MoG model can be evaluated under the framework of maximum likelihood estimation by using an expectation-maximization method, and the subspace is calculated with standard methods. The advantages of the algorithm, high accuracy, and robustness to aliasing, noise, gray difference, and occlusions are illustrated by a series of simulated and real-image experiments.

Performance Analysis of Vehicle Number Plate Recognition System Using Template Matching Techniques

Abstract: Vehicle number plate recognition (VNPR) system is a digital image processing techniques which is broadly used in vehicle transportation system to identify the vehicle by their number plate. Yet it’s a very challenging problem, due to the diversity of plate formats, different scales, and non-uniform illumination conditions during image acquisition. This research mainly focuses on Nepali vehicle number plate recognition system in which the vehicle plate image is received by the digital cameras and the image was then processed to obtain the number plate information. A real image of a vehicle is captured and processed using various algorithms. Morphological operations, and edge detection, smoothing, filtering, techniques for plate localization and characters segmentation for segment character and these segmented character was cut in to block of 70×70 size and calculate the correlation with the template of database using the template matching algorithm normalized cross correlation and phase correlation and compare this result in term of accuracy. The system was tested by 90 patterns under several conditions. It includes experiment of number plate recognition using phase correlation and normalized cross correlation methods. From the study and analysis of test after applying on number of images of database, the normalized cross correlation method was found more accurate to recognize the number plate then phase correlation method and recognition accuracy of normalized cross correlation was 67.98% and phase correlation was 63.46%.

Fast Fourier Transforms for Nonequispaced Data

Abstract: In this chapter, we describe fast algorithms for the computation of the DFT for d-variate nonequispaced data, since in a variety of applications the restriction to equispaced data is a serious drawback. These algorithms are called nonequispaced fast Fourier transforms and abbreviated by NFFT.

An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

Abstract: Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and efficiency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1 / 10 or even 1 / 100 . However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT.

Comparison of the Efficiency of Some Images Superposition Algorithms Used in Aircraft Map-Matching Navigation Systems

Abstract: The paper compares two fast algorithms, the normalized correlation algorithm and the generalized phase correlation algorithm, used for images superposition in aircraft map-matching navigation systems. It is shown that the normalized correlation algorithm appers to be more resistant to various geometry and brightness distortions.

Phase Correlation Between Lines of Electro-Optical Frequency Combs

Abstract: Simultaneous measurement of the phase noise from 49 electro-optical frequency comb lines show a correlation of over 99.99% between lines. Additionally, the phase noise difference between line pairs is correlated, verifying theoretical predictions.

Modeling the Phase Correlation of Effective Diffuse Scattering From Surfaces for Radio Propagation Prediction With Antennas at Refined Separation

Abstract: Diffuse scattering (DS), mostly caused by macroscopically rough surfaces, can be modeled by an effective roughness (ER) approach. In the ER approach, the object surface is divided into tiles, and the DS field amplitude associated with each tile is given. Assuming that the transmit antenna (or emitted field) is fixed and the phases associated with different tiles are independent, this paper proposes a phase evolution model based on the relative spatial locations of the receive antenna (Rx) to each tile. The proposed model contains two parts: the deterministic part that depends on the variation of the distance from Rx to tile center, and the correlated random part that depends on the variation of the angle between the Rx and the normal vector of tile. The correlation of the tile field phases resulting from the angular variation is modeled by the exponentially damped cosine correlation function. The proposed phase evolution model is evaluated by being applied to the ER approach to predict the DS component of radio channel transfer function (CTF). The predicted DS-CTF is compared with the reference (simulated data by physical optics and measured data in well-controlled environment) in terms of the spatial autocorrelation as well as the spatial Doppler spectrum.

Fast image matching based on fourier-mellin phase correlation for tag-less identification of mass-produced parts

Abstract: This paper proposes a fast technique for matching a query image to numerous database images under geometric variations in rotation, scale, and translation. Our proposed method extracts the Fourier-Mellin phase features from the images for invariant matching. The online matching process in our method is fast because it directly determines identification based on the correlation value between those features without the geometric alignment. Our method also proposes selecting only the low frequency bands of those features to be matched for improving identification accuracies, as the high frequency bands tend to be inaccurate in Fourier-Mellin transform. The benefits of our method were verified in our experiments to match the "fingerprint" images of 1,210 metal shafts. The processing time of the 1-vs.-1,210 matching was only 0.136 seconds on a standard desktop computer, and all individuals were identified with no errors.

Automated Alignment of Multi-Modal Plant Images Using Integrative Phase Correlation Approach

Abstract: Modern facilities for high-throughput phenotyping provide plant scientists with a large amount of multi-modal image data. Combination of different image modalities is advantageous for image segmentation, quantitative trait derivation, and assessment of a more accurate and extended plant phenotype. However, visible light (VIS), fluorescence (FLU), and near-infrared (NIR) images taken with different cameras from different view points in different spatial resolutions exhibit not only relative geometrical transformations but also considerable structural differences that hamper a straightforward alignment and combined analysis of multi-modal image data. Conventional techniques of image registration are predominantly tailored to detection of relative geometrical transformations between two otherwise identical images, and become less accurate when applied to partially similar optical scenes. Here, we focus on a relatively new technical problem of FLU/VIS plant image registration. We present a framework for automated alignment of FLU/VIS plant images which is based on extension of the phase correlation (PC) approach - a frequency domain technique for image alignment, which relies on detection of a phase shift between two Fourier-space transforms. Primarily tailored to detection of affine image transformations between two structurally identical images, PC is known to be sensitive to structural image distortions. We investigate effects of image preprocessing and scaling on accuracy of image registration and suggest an integrative algorithmic scheme which allows to overcome shortcomings of conventional single-step PC by application to non-identical multi-modal images. Our experimental tests with FLU/VIS images of different plant species taken on different phenotyping facilities at different developmental stages, including difficult cases such as small plant shoots of non-specific shape and non-uniformly moving leaves, demonstrate improved performance of our extended PC approach within the scope of high-throughput plant phenotyping.

New matrix solution of the phase-correlation technique in a Brillouin dynamic grating sensor

Abstract: In the phase-correlation measurement technique of Brillouin dynamic grating sensors, two counterpropagating pump waves are modulated by a pseudo-random bit sequence (PRBS) that applies a random phase shift of either 0 or π with a specified period. In order to define the sensing length and spatial resolution, the shape of the correlation peak has to be found. So far, many methods have been used to demonstrate the phase correlation, but they often require several lengthy and sometimes complicated mathematical assumptions and equations. One of the techniques that has the best reported spatial resolution is called the time gated phase-correlation technique. We introduce a novel method based on a matrix solution to show the phase modulation in the phase-correlation technique. It is a straightforward and intuitive pattern to visualize the phase modulation of the pumps and to attain the shape of the phase-correlation peaks. Finally, the results of the matrix method are completely consistent with the previous results.

Event Discrimination Using Phase Correlation in Φ-OTDR System Based on Coherent Detection

Abstract: We report a novel method of event discrimination using phase correlation in Φ-OTDR system based on coherent detection. The phase change demodulated from the Rayleigh backscattering light is the result of the event acting on the optical fiber. Then, the correlation coefficient between the phase change of every point along the fiber and that of the fiber end is obtained. A vibration event acting on the fiber induces the change of the correlation coefficient. So, the number of vibration events is determined by the number of changes of the correlation coefficient. In addition, it proves that our proposed method is able to discriminate two close events even when they are in the range of half-pulsewidth.

Drift-free motion estimation from video images using phase correlation and linear optimization

Abstract: Motion reconstruction from video images is known as an effective way in the autonomous robot guidance or teaching for a particular task. The authors' goal is to reconstruct motion from a video with the image-based visual servoing, and the main topic of this paper is to estimate camera motion on the image plane to make a reference. This paper focuses on a drift-free robust camera motion estimation by utilizing all relationships between every two frames. The proposed method in this paper does not use feature points like well-known bundle adjustment. It enables us to use a linear equation to solve its optimization. Then, brand new solution by using distance matrix is proposed so that it can save computation time and memory against a common pseudo inverse matrix based method. Finally, both qualitative and quantitative evaluations based on image mosaicing technique were achieved to confirm the effectiveness of proposed method.

Application of Unnormalized and Phase Correlation Techniques on Infrared Images

Abstract: Accurate and unambiguous measurement of relative displacement between two images is important in a large number of practical applications. Comparison of a real-time captured image with a stored reference image is one such example. Comparison of two sensed images acquired within a short and accurately known time interval can be used to measure the velocity to height ratio of a moving imaging system. Accurate superposition of two images acquired at different times and their consequent, point by point subtraction, calls for attention to changes which have occurred over time in the real-time scenario. All such applications require measurement of the displacement vector to accuracies within a small fraction of pixel. The phase correlation method is based on the fact that most of the information about the relative displacement vector is contained in the phase of their cross-power spectrum.

Image Reconstruction Using Novel Two-Dimensional Fourier Transform

Abstract: Reconstruction of a signal from its subset is used in various contexts in the field of signal processing. Image reconstruction is one such example which finds widespread application in face recognition, medical imaging, computer vision etc. Image reconstruction is computationally complex, and efficient implementations need to exploit the parallelism inherent in this operation. Discrete Fourier Transform (DFT) is a widely used technique for image reconstruction. Fast Fourier Transform (FFT) algorithms are used to compute DFTs efficiently. In this paper we propose a novel two dimensional (2D) Fast Fourier Transform technique for efficient reconstruction of a 2D image. The algorithm first applies 1D FFT based on radix-\(4^n\) along the rows of the image followed by same FFT operation along columns, to obtain a 2D FFT. Radix-\(4^n\) technique used here provides significant savings in memory required in the intermediate stages and considerable improvement in latency. The proposed FFT algorithm can be easily extended to three dimensional and higher dimensional FFTs. Simulated results for image reconstruction based on this technique are presented in the paper. 64 point FFT based on radix-\(4^3\) has been implemented using 130nm CMOS technology and operates at a maximum clock frequency of 350 MHz.

Image matching method and system

Abstract: The invention relates to the field of image matching technology, in particular to an image matching method and system The image matching method comprises the steps that a, a template image and a to-be-matched image are read, and sparse Fourier transformation is performed on the template image and the to-be-matched image; b, an amplitude spectrum of the template image and an amplitude spectrum ofthe to-be-matched image after sparse Fourier transformation are calculated respectively; c, log-polar transformation is performed on the template image and the to-be-matched image according to the amplitude spectra; and d, phase correlation is performed on the log-polar transformation result of the template image and the to-be-matched image, a rotational angle, a scaling coefficient and a translation quantity are obtained, and matching is performed on the template image and the to-be-matched image according to the obtained rotational angle, scaling coefficient and translation quantity Throughthe image matching method and system, a high-pass filtering step in traditional Fourier-Mellin transformation is omitted, an error brought by high-pass filtering in a traditional image matching algorithm is avoided, phase correlation done in a later period is more precise, and algorithm intelligence is obviously improved

Asymmetric Bilateral Phase Correlation for Optical Flow Estimation in the Frequency Domain

Abstract: We address the problem of motion estimation in images operating in the frequency domain. A method is presented which extends phase correlation to handle multiple motions present in an area. Our scheme is based on a novel Bilateral-Phase Correlation (BLPC) technique that incorporates the concept and principles of Bilateral Filters retaining the motion boundaries by taking into account the difference both in value and distance in a manner very similar to Gaussian convolution. The optical flow is obtained by applying the proposed method at certain locations selected based on the present motion differences and then performing non-uniform interpolation in a multi-scale iterative framework. Experiments with several well-known datasets with and without ground-truth show that our scheme outperforms recently proposed state-of-the-art phase correlation based optical flow methods.

Eliminating Effect of Image Border with Image Periodic Decomposition for Phase Correlation Based Image Registration

Abstract: In remote sensing community, accurate image registration is the basement of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. But the effect of image border corrupted its registration accuracy and success rate. Currently, the main solution is blurring off the border of image by weighting window function with reference and sensed image. However, the approach also inevitably filters out non-border information of an image, which is useful to image registration based on phase correlation. In this paper, another way of thinking to eliminate the effect of image border is proposed, namely decomposing the image into two images, one is periodic image and the other is smooth image. Engulfing the original image by the periodic one has no direct effect on the image border due to applying Fourier Transform. The smooth image is analogous to an error image, which has little information except at the border. The novel algorithm of eliminating the image border can improve the success rate and registration accuracy of phase correlation based image registration. To illustrate its superiority, we showed the corresponding magnitude of Fourier Transform of image visually and compared two measurements with other three state-of-the-art algorithms quantitatively.

An Image Matching Correction Method of Integrating Least Squares and Phase Correlation Using Window Series

Abstract: Matching is the knotty point in photogrammetry and computer vision. Aiming at inaccurate corresponding points after preliminary matching, this paper proposed an image matching correction method of integrating least squares and phase correlation using window series. The method firstly uses least squares and phase correlation matching to correct corresponding points in utilizing of window series, and simultaneously calculates correlation coefficients using windows of different size. And then the correlation coefficients are used as the index of evaluating whether the corresponding image points are accurate or not. So the matching results with the largest correlation coefficients are chosen as the final results. Based on experimental data-set 1 and data-set 2, the experimental results revealed that the use of window series can significantly improve the correction accuracy of preliminary matching results. And the proposed method can correct the corresponding points of preliminary matching effectively and greatly improve the overall matching accuracy, which is better than least squares matching or phase correlation matching using window series and fixed windows.

The optical lens coupled X-ray in-line phase contrast imaging system for the characterization of low Z materials

Abstract: Due to the high spatial resolution and contrast, the optical lens coupled X-ray in-line phase contrast imaging system with the secondary optical magnification is more suitable for the characterization of the low Z materials. The influence of the source to object distance and the object to scintillator distance on the image resolution and contrast is studied experimentally. A phase correlation algorithm is used for the image mosaic of a serial of X-ray phase contrast images acquired with high resolution, the resulting resolution is less than 1.0 μm, and the whole field of view is larger than 1.4 mm. Finally, the geometric morphology and the inner structure of various weakly absorbing samples and the evaporation of water in the plastic micro-shell are in situ characterized by the optical lens coupled X-ray in-line phase contrast imaging system.

A Registration and Change Detection Algorithm Based on Pre-and Post-Strike Image

Abstract: 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.

Practical Approach for Implementation of Phase Correlation Based Image Registration in FPGA

Abstract: This paper presents a practical approach for implementing phase correlation algorithm in presence of image noise. We propose Gradient filter implementation for edge enhancement to retrieve the translational shift using phase correlation method. We have implemented the filter in FPGA in an optimum way. With this improved phase correlation method we are able to achieve the sub pixel shift even in presence of noise and smear with only condition that image should have at least 10% contrast ratio.

Real-Time Registration in Image Stitching Under the Microscope

Abstract: Image registration and fusion has always been an important part of computer vision, but most of the existing algorithms aim at the registration between two images, paying attention to accuracy, while sacrificing the requirements of speed The application scenario of this paper is real-time registration in image stitching under the microscope. That is, while moving the operating platform to observe the cell structure under the microscope, the stitching is performed in real time to obtain an entire spliced image. In order to speed up the image matching process and improve the accuracy, this paper creatively combines the phase correlation algorithm and the feature point matching algorithmfor image registration, and improves them, completing the registration of multiple images. This paper focuses on the real time ability and accuracy of the stitching program.

Asymmetric Bilateral Phase Correlation for Optical Flow Estimation in the Frequency Domain

Abstract: We address the problem of motion estimation in images operating in the frequency domain. A method is presented which extends phase correlation to handle multiple motions present in an area. Our scheme is based on a novel Bilateral-Phase Correlation (BLPC) technique that incorporates the concept and principles of Bilateral Filters retaining the motion boundaries by taking into account the difference both in value and distance in a manner very similar to Gaussian convolution. The optical flow is obtained by applying the proposed method at certain locations selected based on the present motion differences and then performing non-uniform interpolation in a multi-scale iterative framework. Experiments with several well-known datasets with and without ground-truth show that our scheme outperforms recently proposed state-of-the-art phase correlation based optical flow methods.

Digital field-of-view correction for dual-view transport-of-intensity equation-based quantitative microscopy

Abstract: As an ideal way for quantitative live cell imaging, dual view transport of intensity equation (TIE) method can provide both real time imaging, multi-mode observations, simple setup and large field of view (FoV). However, the image recorder installation error reduces the accuracy in both amplitude and phase retrievals, because of the inevitable FoV mismatch between the captured under- and over-focus intensities. In order to obtain higher accuracy amplitude and phase retrievals, the phase correlation based digital FoV correction is introduced into our method, rotation, scale and translation between the under- and over-focus images are compensated by the phase correlation based digital FoV correction. Measurements are implemented using standard sample detection and quantitative live cell imaging, proving that the proposed method can improve the accurate of the amplitude and phase computations.

Image Registration Using Composite Algorithms

Abstract: Image registration, a process of mapping two or more images having different alignment but sharing the same information, is important step in many applications. These applications require perceptible data from different images for comparison, integration or analysis. Image registration plays important role in medical imaging as most of the applications is related to clinical prognosis. Here, four image registration techniques namely geometric transformation, phase correlation, $\text{log}$ polar transform and adaptive polar transform are analyzed. Registration algorithms measure transformation to set correspondence between more than two images. The purpose of this proposal is to give in depth analysis of all the techniques and observe which gives best mapping results with the two images, and also find parameters which gives relevant information which helps to medical experts. Rotation and scaling factor are obtained by log polar transform. But because of non-uniform sampling, LPT is not proper approach for some applications. Therefore, APT is used which maintain the robustness to scale and rotation. For translation property, phase correlation help. However, limitations of all these techniques are still exit.