Phase Correlation Algorithm Research Articles

Research on Multi-Target Stable Tracking Algorithm Based on Detection and Tracking Fusion

Vision-based dynamic multi-objective detection and stable tracking has been a hot topic in recent years. This paper proposed a novel multi-target detection and tracking algorithm based on false-alarms elimination with classification and detection tracking fusion. Firstly, adaptive threshold segmentation and multiple morphological processing were used to finish target detecting, and center-peripheral gray scale difference classification method was proposed to remove false alarm target; secondly, the improved spatial phase correlation algorithm was used to complete the tracking of the target one by one; and thirdly, the target detection and tracking fusion algorithm based on a minimum cost function was also proposed to reduce the false alarm rate of the target. Experiment tests show that compared with the traditional method, the target detection accuracy of the detection and tracking fusion algorithm improves by 7% and the algorithm is able to adaptively adjust the tracking frame size and recapture the target after the target is lost. It can satisfy multiple targets tracking at the same time.

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

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.

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 ${\text{240}}.{\text{6}}\times$ .

High-Accuracy Subpixel Image Registration With Large Displacements

This paper aims to achieve computationally efficient and high-accuracy subpixel image registration with large displacements under the rotation–scale–translation model. This paper employs the classical phase correlation algorithm and the Lucas–Kanade (LK) algorithm in a two-stage coarse-to-fine framework, for which the motivation is from the observation that the two algorithms exhibit strong complementary property between convergence range and subpixel accuracy. In this framework, the LK algorithm will also become computationally efficient owing to the small residual displacement. On the other hand, this paper takes into account the residual model with respect to the compensation scheme explicitly, and deduces formulas for the final results combination, which is expected to be closer to the true displacement vector and thus further improve the estimation accuracy. Since the compensation can be applied to either the target image or the reference image, two algorithms are presented accordingly, and analysis as well as comparison are also performed. Finally, both simulations and real image experiments are performed to verify the motivation, and the results are consistent with the analysis.

Position detection method of linear motor mover based on extended phase correlation algorithm

This study aims to explain the real-time and high-precision requirements of linear motor mover position detection. A new sub-pixel displacement estimation based on an extended phase correlation algorithm (PCA) is proposed and applied to measure the position of the linear motor mover. This extended PCA is on the basis of singular value decomposition (SVD), which is a novel application in position measurement of the linear motor mover. First, two adjacent stripe images are captured by a high-speed camera installed in the linear motor. Second, an improved Hanning (flap-top) window function is added to the input fence images to suppress the edge effect and spectral aliasing, before the discrete Fourier transform (DFT). Next, phase correlation matrix is acquired by PCA, and using SVD technique to calculate approximate rank-one matrix for subsequent phase unwrapping. Then, weighted-phase unwrap algorithm is applied to acquire the real horizontal phase data. Finally, least-squares method is utilised to linearly fit the real phase data and to obtain sub-pixel displacement of the linear motor mover. Experiments show that the detection accuracy is 1/100 pixel, and the position measurement of linear motor mover is fast and effective.

Detection, Localization and Quantification of Impact Events on a Stiffened Composite Panel with Embedded Fiber Bragg Grating Sensor Networks.

Nowadays, it is possible to manufacture smart composite materials with embedded fiber optic sensors. These sensors can be exploited during the composites’ operating life to identify occurring damages such as delaminations. For composite materials adopted in the aviation and wind energy sector, delaminations are most often caused by impacts with external objects. The detection, localization and quantification of such impacts are therefore crucial for the prevention of catastrophic events. In this paper, we demonstrate the feasibility to perform impact identification in smart composite structures with embedded fiber optic sensors. For our analyses, we manufactured a carbon fiber reinforced plate in which we embedded a distributed network of fiber Bragg grating (FBG) sensors. We impacted the plate with a modal hammer and we identified the impacts by processing the FBG data with an improved fast phase correlation (FPC) algorithm in combination with a variable selective least squares (VS-LS) inverse solver approach. A total of 164 impacts distributed on 41 possible impact locations were analyzed. We compared our methodology with the traditional P-Inv based approach. In terms of impact localization, our methodology performed better in 70.7% of the cases. An improvement on the impact time domain reconstruction was achieved in of the cases.

Застосування алгоритму фазової кореляції та його розширення в оптичному стабілізаторі безпілотних літальних апаратів

The use of the phase correlation algorithm as the basis of unmanned aerial vehicles (UAV) Optical Stabilizer on purpose to determine the displacement of the housing relative to the surface is described. Also, the possibility of application of the phase correlation algorithm extensions to increase accuracy and reduce computing costs is considered. The algorithm implementation in bot MATLAB language and C is provided. The computational complexity of the algorithm implementation in C was experimentally analyzed in the scope of embedded systems. The proposed algorithm gives not only an auxiliary possibility to stabilize the aircraft position but also to calculate absolute position of the aircraft in space.

Estimation of glacier surface motion by robust phase correlation and point like features of SAR intensity images

For monitoring of glacier surface motion in pole and alpine areas, radar remote sensing is becoming a popular technology accounting for its specific advantages of being independent of weather conditions and sunlight. In this paper we propose a method for glacier surface motion monitoring using phase correlation (PC) based on point-like features (PLF). We carry out experiments using repeat-pass TerraSAR X-band (TSX) and Sentinel-1 C-band (S1C) intensity images of the Taku glacier in Juneau icefield located in southeast Alaska. The intensity imagery is first filtered by an improved adaptive refined Lee filter while the effect of topographic reliefs is removed via SRTM-X DEM. Then, a robust phase correlation algorithm based on singular value decomposition (SVD) and an improved random sample consensus (RANSAC) algorithm is applied to sequential PLF pairs generated by correlation using a 2D sinc function template. The approaches for glacier monitoring are validated by both simulated SAR data and real SAR data from two satellites. The results obtained from these three test datasets confirm the superiority of the proposed approach compared to standard correlation-like methods. By the use of the proposed adaptive refined Lee filter, we achieve a good balance between the suppression of noise and the preservation of local image textures. The presented phase correlation algorithm shows the accuracy of better than 0.25 pixels, when conducting matching tests using simulated SAR intensity images with strong noise. Quantitative 3D motions and velocities of the investigated Taku glacier during a repeat-pass period are obtained, which allows a comprehensive and reliable analysis for the investigation of large-scale glacier surface dynamics.

An Improved Super-resolution Image Reconstruction Algorithm

The paper introduces the Keren registration method and points out its disadvantage which means it will become inaccuracy on the large scale parameters. To reduce the error on large scale parameters of Keren registration, a two step method is proposed, which the phase correlation algorithm is used to estimate the large translation and rotation angle roughly and the improved Keren algorithm is used to estimate accurately the small translation and rotation angle. The experimental results show that the two step method makes less absolute error of angle than Keren method in the situation of large translation and rotation angle. A new method of estimating the standard deviation of noise is introduced to the robust certainty function, which reduces the impact of noise in the process of interpolation using normalized convolution algorithm. By the edge detection of fusion image in the first stage of the interpolation process of normalized convolution algorithm, a calculation method of long axis and short axis of the structure self-adaptive function is improved. The experimental results show that the proposed interpolation method can improve the performance of the original algorithm and enhance the effect of image super-resolution reconstruction.

Phase correlation applied to the 3D registration of CT and CBCT image volumes

PurposeIn this study, a 3D phase correlation algorithm was investigated to test feasibility for use in determining the anatomical changes that occur throughout a patient's radiotherapy treatment. The algorithm determines the transformations between two image volumes through analysis in the Fourier domain and has not previously been used in radiotherapy for 3D registration of CT and CBCT volumes. MethodsVarious known transformations were applied to a patient's prostate CT image volume to create 12 different test cases. The mean absolute error and standard deviation were determined by evaluating the difference between the known contours and those calculated from the registration process on a point-by-point basis. Similar evaluations were performed on images with increasing levels of noise added. The improvement in structure overlap offered by the algorithm in registering clinical CBCT to CT images was evaluated using the Dice Similarity Coefficient (DSC). ResultsA mean error of 2.35 (σ = 1.54) mm was calculated for the 12 deformations applied. When increasing levels of noise were introduced to the images, the mean errors were observed to rise up to a maximum increase of 1.77 mm. For CBCT to CT registration, maximum improvements in the DSC of 0.09 and 0.46 were observed for the bladder and rectum, respectively. ConclusionsThe Fourier-based 3D phase correlation registration algorithm investigated displayed promising results in CT to CT and CT to CBCT registration, offers potential in terms of efficiency and robustness to noise, and is suitable for use in radiotherapy for monitoring patient anatomy throughout treatment.

Improvement of automated image stitching system for DR X-ray images

The full bone structure of X-ray images cannot be captured in a single scan with Digital radiography (DR) system. The stitching method of X-ray images is very important for scoliosis or lower limb malformation diagnosing and pre-surgical planning. Based on the image registration technology, this paper proposes a new automated image stitching method for full-spine and lower limb X-ray images. The stitching method utilized down-sampling to decrease the size of image and reduce the amount of computation; improved phase correlation algorithm was adopted to find the overlapping region; correlation coefficient was used to evaluate the similarity of overlapping region; weighted blending is brought in to produce a panorama image. The performance of the proposed method was evaluated by 40 pairs of images from patients with scoliosis or lower limb malformation. The stitching method was fully automated without any user input required. The experimental results were compared with previous methods by analyzing the same database. It is demonstrated that the improved phase correlation has higher accuracy and shorter average stitching time than previous methods. It could tackle problems including image translation, rotation and small overlapping in image stitching.

Dynamic Strain Measurements on Automotive and Aeronautic Composite Components by Means of Embedded Fiber Bragg Grating Sensors

The measurement of the internal deformations occurring in real-life composite components is a very challenging task, especially for those components that are rather difficult to access. Optical fiber sensors can overcome such a problem, since they can be embedded in the composite materials and serve as in situ sensors. In this article, embedded optical fiber Bragg grating (FBG) sensors are used to analyze the vibration characteristics of two real-life composite components. The first component is a carbon fiber-reinforced polymer automotive control arm; the second is a glass fiber-reinforced polymer aeronautic hinge arm. The modal parameters of both components were estimated by processing the FBG signals with two interrogation techniques: the maximum detection and fast phase correlation algorithms were employed for the demodulation of the FBG signals; the Peak-Picking and PolyMax techniques were instead used for the parameter estimation. To validate the FBG outcomes, reference measurements were performed by means of a laser Doppler vibrometer. The analysis of the results showed that the FBG sensing capabilities were enhanced when the recently-introduced fast phase correlation algorithm was combined with the state-of-the-art PolyMax estimator curve fitting method. In this case, the FBGs provided the most accurate results, i.e., it was possible to fully characterize the vibration behavior of both composite components. When using more traditional interrogation algorithms (maximum detection) and modal parameter estimation techniques (Peak-Picking), some of the modes were not successfully identified.

The Illumination Robustness of Phase Correlation for Image Alignment

Phase correlation (PC) is a well-established image matching algorithm. It is robust to the variation of image contrast and brightness, but whether it is invariant to local illumination change, particularly sun angle change, is yet to be investigated. This paper presents our study to prove and demonstrate the robustness of illumination invariance of the PC algorithm via mathematical analysis and image matching experiments. First, a 3-D space named slope-aspect-intensity (SAI) is proposed to characterize the 3-D relationship between image intensity and terrain slope/aspect angles for a given illumination geometry. Based on the SAI space, the mathematical relationship between PC cross-power spectra and illumination direction (e.g., solar azimuth and zenith angles) is analyzed. The impact of illumination angle variation between two images for matching is then rigorously investigated via experiments using simulated terrain shading images from a digital elevation model and real optical images taken under different sun illumination conditions. Our study confirms that PC is robustly invariant to illumination and therefore can achieve reliable matching between images taken under different solar illumination conditions for various remote sensing applications.

Quaternion Wiener Deconvolution for Noise Robust Color Image Registration

In this letter, we propose a global method for registering color images with respect to translation. Our approach is based on the idea of representing translations as convolutions with unknown shifted delta functions, and performing Wiener deconvolution in order to recover the shift between two images. We then derive a quaternionic version of the Wiener deconvolution filter in order to register color images. The use of Wiener filter also allows us to explicitly take into account the effect of noise. We prove that the well-known algorithm of phase correlation is a special case of our method, and we experimentally demonstrate the advantages of our approach by comparing it to other known generalizations of the phase correlation algorithm.

Applicability analysis of the phase correlation algorithm for stabilization of video frames sequences for capillary blood flow

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.

A Novel Frequency Domain Iterative Image Registration Algorithm Based on Local Region Extraction

Because of the differences of imaging time, position between sensor and target position, scaling, rotation, translation, and other transformations between the series of images will be generated by the imaging system. The conventional phase correlation algorithm has been widely applied because of its advantages of high speed, precision, and weak influence of the geometric distortion when computing these changing parameters. However, when the scaling factor and the rotation angle are too large, it is difficult to use the conventional phase correlation method for high precision registration. To solve this problem, this paper presents a novel method, which combines the speeded up robust features algorithm and the phase correlation method under the log polar. Through local region extraction and reusing a two-step iterative phase correlation algorithm, this method avoids excessive computation and the demand of characteristics of the image and effectively improves the accuracy of registration. A plurality of visible light image simulation verifies that this is a fast, accurate, and robust algorithm, even when the image has large angle rotation and large multiple scaling.

Influence of fiber Bragg grating spectrum degradation on the performance of sensor interrogation algorithms.

The working principle of fiber Bragg grating (FBG) sensors is mostly based on the tracking of the Bragg wavelength shift. To accomplish this task, different algorithms have been proposed, from conventional maximum and centroid detection algorithms to more recently-developed correlation-based techniques. Several studies regarding the performance of these algorithms have been conducted, but they did not take into account spectral distortions, which appear in many practical applications. This paper addresses this issue and analyzes the performance of four different wavelength tracking algorithms (maximum detection, centroid detection, cross-correlation and fast phase-correlation) when applied to distorted FBG spectra used for measuring dynamic loads. Both simulations and experiments are used for the analyses. The dynamic behavior of distorted FBG spectra is simulated using the transfer-matrix approach, and the amount of distortion of the spectra is quantified using dedicated distortion indices. The algorithms are compared in terms of achievable precision and accuracy. To corroborate the simulation results, experiments were conducted using three FBG sensors glued on a steel plate and subjected to a combination of transverse force and vibration loads. The analysis of the results showed that the fast phase-correlation algorithm guarantees the best combination of versatility, precision and accuracy.

Research on the Recursive and Step-by-Step Algorithm for Extraction of Point Laser Spot Center

Whentesting the flatness of plate iron, if there is no disturbance in the spotdetection, the traditional centroid method and circle fitting method can bewell applied to extract the point laser spot center. However, the disturbanceof droplets, dusts, oil droplet and other factors are unavoidably in thepractical projects, and then the detection precision will falls sharply. In orderto solve the problem, the recursive and step-by-step method of extracting pointlaser spot center is proposed. There are two steps in this method.The thresholdcentroid method is used as the first step to extract the spots which containthe black blocks. The second step is to construct the template whose center hasa black block, then extract the black block center by running phase correlationalgorithm and the center is just the center of the point laser spot. Theresults of experiments verify that the precision of the spot center coordinateextracted by the proposed method is obviously higher than that extracted by thecentroid method and the circle fitting method with the disturbance of droplets.

An improvement of image registration based on phase correlation

This paper is dedicated to studying the extended phase correlation method that applies to the sub-pixel evaluation of translational displacement between images. On the basis of experiments, this paper proposes an improved version of classical Hanning window, with involving parameters optimization in the process. We add a window function to the input images in the space domain and a weighting function to the spectrum in the frequency domain. The statistics and distribution of errors prove the applicability of the proposal. The experimental results of the proposed extended phase correlation algorithm show the registration accuracy of 1/100 pixel.

Evaluation of a novel susceptibility weighted imaging MRI sequence for neonate brain scanning

Evaluation of a novel susceptibility weighted imaging MRI sequence for neonate brain scanning