Fast speedometer identification in dynamic scene based on phase correlation

Speedometer identification has been researched for many years.The common approaches to that problem are usually based on image subtraction,which does not adapt to image offsets caused by camera vibration.To cope with the rapidity,robust and accurate requirements of this kind of work in dynamic scene,a fast speedometer identification algorithm is proposed,it utilizes phase correlation method based on regional entire template translation to estimate the offset between images.In order to effectively reduce unnecessary computation and false detection rate,an improved linear Hough transform method with two optimization strategies is presented for pointer line detection.Based on VC＋＋ 6.0 software platform with OpenCV library,the algorithm performance under experiments has shown that it celerity and precision.

Exploiting Robust Estimators in Phase Correlation of 3D Point Clouds for 6 DoF Pose Estimation

Point cloud registration is a fundamental task in both remote sensing,photogrammetry,and computer vision,which is to align multiple point clouds to the same coordinate frame.Especially in LiDAR odometry,by conducting the transformation between two adjacent scans,the pose of the platform can be estimated.To be specific,the goal is to recover the relative six-degree-of-freedom(6 DoF)pose between the source point cloud and the target point cloud.In this paper,we explore the use of robust estimators in the phase correlation when registering two point clouds,enabling a 6 DoF pose estimation between point clouds in a sub-voxel accuracy.The estimator is a rule for calculating an estimate of a given quantity based on observed data.A robust estimator is an estimation rule that is insensitive to nonnormality and can estimate parameters of a given objective function from noisy observations.The proposed registration method is theoretically insensitive to noise and outliers than correspondence-based methods.Three core steps are involved in the method:transforming point clouds from the spatial domain to the frequency domain,decoupling of rotations and translations,and using robust estimators to estimate phase shifts.Since the estimation of transformation parameters lies in the calculation of phase shifts,robust estimators play a vital role in shift estimation accuracy.In this paper,we have tested the performance of six different robust estimators and provide comparisons and discussions on the contributions of robust estimators in the 3D phase correlation.Different point clouds from two urban scenarios and one indoor scene are tested.Results validate the proposed method can reach performance that predominant rotation and translation errors reaching less than 0.5°and 0.5 m,respectively.Moreover,the performance of various tested robust estimators is compared and discussed.

Phase Correlation Based Iris Image Registration Model

Iris recognition is one of the most reliable personal identification methods. In iris recognition systems, image registration is an important component. Accurately registering iris images leads to higher recognition rate for an iris recognition system. This paper proposes a phase correlation based method for iris image registration with sub-pixel accuracy. Compared with existing methods, it is insensitive to image intensity and can compensate to a certain extent the non-linear iris deformation caused by pupil movement. Experimental results show that the proposed algorithm has an encouraging performance.

Automatic sub-pixel co-registration of Landsat-8 Operational Land Imager and Sentinel-2A Multi-Spectral Instrument images using phase correlation and machine learning based mapping

This study investigates misregistration issues between Landsat-8/Operational Land Imager and Sentinel-2A/Multi-Spectral Instrument at 30 m resolution,and between multi-temporal Sentinel-2A images at 10 m resolution using a phase-correlation approach and multiple transformation functions.Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed.Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart.Overall,misregistration of up to 1.6 pixels at 30 m resolution between Landsat-8 and Sentinel-2A images,and 1.2 pixels and 2.8 pixels at 10 m resolution between multi-temporal Sentinel-2A images from the same and different orbits,respectively,were observed.The non-linear random forest regression used for constructing the mapping function showed best results in terms of root mean square error(RMSE),yielding an average RMSE error of 0.07±0.02 pixels at 30 m resolution,and 0.09±0.05 and 0.15±0.06 pixels at 10 m resolution for the same and adjacent Sentinel-2A orbits,respectively,for multiple tiles and multiple conditions.A simpler 1st order polynomial function(affine transformation)yielded RMSE of 0.08±0.02 pixels at 30 m resolution and 0.12±0.06(same Sentinel-2A orbits)and 0.20±0.09(adjacent orbits)pixels at 10 m resolution.

Sub-pixel motion estimation using phase correlation: comparisons and evaluations

Purpose-Effective and robust motion estimation with sub-pixel accuracy is essential in many image processing and computer vision applications.Due to its computational efficiency and robustness in the presence of intensity changes as well as geometric distortions,phase correlation in the Fourier domain provides an attractive solution for global motion estimation and image registration.The paper aims to discuss these issues.Design/methodology/approach-In this paper,relevant sub-pixel strategies are categorized into three classes,namely,single-side peak interpolation,dual-side peak interpolation and curve fitting.The well-known images“Barbara”and“Pentagon”were used to evaluate the performance of eight typical methods,in which Gaussian noise was attached in the synthetic data.Findings-For eight such typical methods,the tests using synthetic data have suggested that considering dual-side peaks in interpolation or fitting helps to produce better results.In addition,dual-side interpolation outperforms curve fitting methods in dealing with noisy samples.Overall,Gaussian-based dual-side interpolation seems the best in the experiments.Originality/value-Based on the comparisons of eight typical methods,the authors can have a better understanding of the phase correlation for motion estimation.The evaluation can provide useful guidance in this context.

THE CORRELATION BETWEEN ZrO_2 PHASE AND ITS CATALYTIC PROPERTY

In this letter, the preparation of three ZrO2 phases has beed briefly described and their catalytic results have been presented for CO hydrogenation to olefin. These catalysts had a good selectivity for light olefin. Monoclinic ZrO2 catalyst had a high selectivity for isobutene, while the other phase catalysts had not

HIGH-ACCURACY BAND TO BAND REGISTRATION METHOD FOR MULTI-SPECTRAL IMAGES OF HJ-1A/B

Band-to-band registration accuracy is an important parameter of multispectral data. A novel band-to-band registration approach with high precision is proposed for the multi-spectral images of HJ-1A/B. Firstly, the main causes resulted in misregistration are analyzed, and a high-order polynomial model is proposed. Secondly, a phase fringe filtering technique is employed to Phase Correlation Method based on Singular Value Decomposition (SVD-PCM) for reducing the noise in phase difference matrix. Then, experiments are carried out to build nonlinear registration models, and images of green band and red band are aligned to blue band with an accuracy of 0.1 pixels, while near infrared band with an accuracy of 0.2 pixels.

Two new approaches for image registration based onspatial-temporal relationship

How to improve the probability of registration and precision of localization is a hard problem, which is desiderated to solve. The two basic approaches (normalized cross-correlation and phase correlation) for image registration are analysed, two improved approaches based on spatial-temporal relationship are presented. This method adds the correlation matrix according to the displacements in x- cirection and y- directions, and the registration pose is searched in the added matrix. The method overcomes the shortcoming that the probability of registration decreasing with area increasing owing to geometric distortion, improves the probability and the robustness of registration.

Fast and robust image sequence mosaicking of nursery plug tray images

A machine vision based system to estimate the nursery plug tray sowing quantity is necessary in the rice seedling nursery.Because the super hybrid rice is of small diameter,in order to obtain better estimation results of sowing quantity,only part of the tray scene is captured by machine vision system.Therefore,nursery tray image sequence mosaic algorithm is required so as to obtain the whole nursery tray sowing performance.In this paper,a fast nursery plug tray image mosaic technique based on Phase Correlation and Speeded up Robust Features(the PC-SURF)algorithm was introduced.To reduce huge computational complexity in feature extraction and avoid wrong match points from non-overlapping regions between two adjacent images,firstly the proposed method used Phase Correlation to approximately locate the overlapping regions between two sequential images so as to narrow down the processing area for further feature extraction.Then,the SURF algorithm was implemented in the overlapping region of the images to perform image registration and image blending.Image registration and blending were then performed using the RANSAC algorithm and transition smoothing method.Finally,sequential images were warped into a single frame to produce a panoramic tray image with high resolution and better quality.The test results showed that the PC-SURF algorithm greatly outperforms the SURF algorithm in point matching accuracy,time consumption,and image mosaic accuracy.The average feature point matching accuracy of the PC-SURF algorithm was improved by approximately 7.14%.The implementation time was almost three times faster.The Root Mean Squared Error(RMSE)of image blending in the R,G,B channels(RMSEr,RMSEg,and RMSEb)decreased by approximately 8.4%,6.9%,6.9%respectively.The RMSE of image registration RMSEreg decreased by 33.8%.In addition,the speed of the nursery tray image mosaic technology could meet the requirements for real-time implementation in super hybrid rice automated sowing machines.

Mutual Information Optimization Based Dynamic Log-Polar Image Registration

Log-polar transformation(LPT)is widely used in image registration due to its scale and rotation invariant properties.Through LPT,rotation and scale transformation can be made into translation displacement in log-polar coordinates,and phase correlation technique can be used to get the displacement.In LPT based image registration,constant samples in digitalization processing produce less precise and effective results.Thus,dynamic log-polar transformation(DLPT)is used in this paper.DLPT is a method that generates several sample sets in axes to produce several results and only the effective results are used to get the final results by using statistical approach.Therefore,DLPT can get more precise and effective transformation results than the conventional LPT.Mutual information(MI)is a similarity measure to align two images and has been used in image registration for a long time.An optimal transform for image registration can be obtained by maximizing MI between the two images.Image registration based on MI is robust in noisy,occlusion and illumination changing circumstance.In this paper,we study image registration using MI and DLPT.Experiments with digitalizing images and with real image datasets are performed,and the experimental results show that the combination of MI with DLPT is an effective and precise method for image registration.