scholarly journals Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6596
Author(s):  
María-Baralida Tomás ◽  
Belén Ferrer ◽  
David Mas
Keyword(s):  
Cross Correlation ◽  
Gaussian Function ◽  
Local Domain ◽  
Alternative Analysis ◽  
Correlation Peak ◽  
Sample Distribution ◽  
Peak Fitting ◽  
Relevant Role ◽  
Low Pass ◽  
Peak Location

A known technique to obtain subpixel resolution by using object tracking through cross-correlation consists of interpolating the obtained correlation function and then refining peak location. Although the technique provides accurate results, peak location is usually biased toward the closest integer coordinate. This effect is known as the peak-locking error and it strongly limits this calculation technique’s experimental accuracy. This error may differ depending on the scene and algorithm used to fit and interpolate the correlation peak, but in general, it may be attributed to a sampling problem and the presence of aliasing. Many studies in the literature analyze this effect in the Fourier domain. Here, we propose an alternative analysis on the spatial domain. According to our interpretation, the peak-locking error may be produced by a non-symmetrical sample distribution, thus provoking a bias in the result. According to this, the peak interpolant function, the size of the local domain and low-pass filters play a relevant role in diminishing the error. Our study explores these effects on different samples taken from the DIC Challenge database, and the results show that, in general, peak fitting with a Gaussian function on a relatively large domain provides the most accurate results.

Errors in Cross-Correlation Peak Location

1982 ◽  
Vol 104 (2) ◽  
pp. 194-199 ◽  
Author(s):  
G. D. Lassahn ◽  
A. G. Baker
Keyword(s):  
Fluid Flow ◽  
Flow Velocity ◽  
Measurement Technique ◽  
Cross Correlation ◽  
Correlation Peak ◽  
Location Uncertainty ◽  
Fluid Flow Velocity ◽  
Peak Location ◽  
In Transit ◽  
Velocity Turbulence

A method for determining the uncertainty in the location of a cross-correlation peak is derived and two examples are examined. When a cross-correlation peak location is used in transit time flowmeters as a measurement technique for fluid-flow velocity, turbulence in the fluid increases the peak location uncertainty. This effect is examined for two models of turbulence. Also examined is the bias or shift in the peak location due to time response or filter mismatch.

Application of cross correlation to HREM images of surfaces

1987 ◽  
Vol 45 ◽  
pp. 754-755
Author(s):  
D. E. Luzzi ◽  
L. D. Marks ◽  
M. I. Buckett
Keyword(s):  
Cross Correlation ◽  
A Priori ◽  
Matrix Material ◽  
Correlation Method ◽  
Accurate Knowledge ◽  
Complex Image ◽  
Fourier Filtering ◽  
The Matrix ◽  
Low Pass ◽  
Data Reduction Technique

As the HREM becomes increasingly used for the study of dynamic localized phenomena, the development of techniques to recover the desired information from a real image is important. Often, the important features are not strongly scattering in comparison to the matrix material in addition to being masked by statistical and amorphous noise. The desired information will usually involve the accurate knowledge of the position and intensity of the contrast. In order to decipher the desired information from a complex image, cross-correlation (xcf) techniques can be utilized. Unlike other image processing methods which rely on data massaging (e.g. high/low pass filtering or Fourier filtering), the cross-correlation method is a rigorous data reduction technique with no a priori assumptions.We have examined basic cross-correlation procedures using images of discrete gaussian peaks and have developed an iterative procedure to greatly enhance the capabilities of these techniques when the contrast from the peaks overlap.

scholarly journals More about solar g modes

2018 ◽  
Vol 612 ◽  
pp. L1 ◽  
Author(s):  
E. Fossat ◽  
F. X. Schmider
Keyword(s):  
Cross Correlation ◽  
Previous Analysis ◽  
Model Parameters ◽  
Real Spectrum ◽  
Space Observatory ◽  
Correlation Peak ◽  
Similar Frequency ◽  
Precise Estimation ◽  
The One ◽  
Additional Constraints

Context. The detection of asymptotic solar g-mode parameters was the main goal of the GOLF instrument onboard the SOHO space observatory. This detection has recently been reported and has identified a rapid mean rotation of the solar core, with a one-week period, nearly four times faster than all the rest of the solar body, from the surface to the bottom of the radiative zone. Aim. We present here the detection of more g modes of higher degree, and a more precise estimation of all their parameters, which will have to be exploited as additional constraints in modeling the solar core. Methods. Having identified the period equidistance and the splitting of a large number of asymptotic g modes of degrees 1 and 2, we test a model of frequencies of these modes by a cross-correlation with the power spectrum from which they have been detected. It shows a high correlation peak at lag zero, showing that the model is hidden but present in the real spectrum. The model parameters can then be adjusted to optimize the position (at exactly zero lag) and the height of this correlation peak. The same method is then extended to the search for modes of degrees 3 and 4, which were not detected in the previous analysis.Results. g-mode parameters are optimally measured in similar-frequency bandwidths, ranging from 7 to 8 μHz at one end and all close to 30 μHz at the other end, for the degrees 1 to 4. They include the four asymptotic period equidistances, the slight departure from equidistance of the detected periods for l = 1 and l = 2, the measured amplitudes, functions of the degree and the tesseral order, and the splittings that will possibly constrain the estimated sharpness of the transition between the one-week mean rotation of the core and the almost four-week rotation of the radiative envelope. The g-mode periods themselves are crucial inputs in the solar core structure helioseismic investigation.

scholarly journals A nonlinear edge –preserving smoothing filter for edge detection on color and gray satellite images

2019 ◽  
Vol 13 (26) ◽  
pp. 29-37
Author(s):  
Suhad A. Hamdan
Keyword(s):  
Edge Detection ◽  
Gaussian Noise ◽  
Satellite Images ◽  
Cross Correlation ◽  
Nonlinear Filter ◽  
Edge Preserving ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Smoothing Filter ◽  
Low Pass

A nonlinear filter for smoothing color and gray imagescorrupted by Gaussian noise is presented in this paper. The proposedfilter designed to reduce the noise in the R,G, and B bands of thecolor images and preserving the edges. This filter applied in order toprepare images for further processing such as edge detection andimage segmentation.The results of computer simulations show that the proposedfilter gave satisfactory results when compared with the results ofconventional filters such as Gaussian low pass filter and median filterby using Cross Correlation Coefficient (ccc) criteria.

Vector Positioning for Cross Correlation PIV

2002 ◽  
Author(s):  
C. N. Young ◽  
R. Gilbert ◽  
D. A. Johnson ◽  
E. J. Weckman
Keyword(s):  
Cross Correlation ◽  
Full Range ◽  
Reduction Process ◽  
Particle Number Density ◽  
Particle Dispersion ◽  
Correlation Peak ◽  
Measured Velocity ◽  
Velocity Gradients ◽  
Positional Bias ◽  
The Cross

Continuing advances in digital imaging technology stimulate greater interest in applying particle image velocimetry (PIV) over increasingly larger fields of view. Unfortunately when larger fields of view are analyzed, velocity gradients in the image become more localized. In addition, non-uniformities in image illumination and particle number density become more prevalent. These factors, coupled with the requirement that large areas of interest (AOIs) must be employed to measure the full range of velocity, cause degradation of correlation results (i.e. broadening and/or splintering of the cross correlation peak) which leads to positional bias errors in the measured velocity field. More advanced super resolution strategies that employ an iterative AOI reduction process inherently reduce positional bias in PIV results but these strategies can break down in complex flows where velocity gradients are steep and particle dispersion does not remain uniformly random. To mitigate these problems a simple but effective technique is presented that enables individual velocity vectors to be placed within an AOI at locations toward which the cross correlation plane is biased. The method involves analysis of the correlation plane to extract the dominant features that are matched in two successive AOIs. To demonstrate the utility of the methodology results obtained from synthetic images are compared against results obtained using the conventional PIV approach.

Burst Firing and Modulation of Functional Connectivity in Cat Striate Cortex

1998 ◽  
Vol 80 (2) ◽  
pp. 730-744 ◽  
Author(s):  
R. K. Snider ◽  
J. F. Kabara ◽  
B. R. Roig ◽  
A. B. Bonds
Keyword(s):  
Functional Connectivity ◽  
Cross Correlation ◽  
Striate Cortex ◽  
Temporal Integration ◽  
Burst Firing ◽  
Correlation Peak ◽  
Cortical Cells ◽  
Instantaneous Rate ◽  
Cross Correlation Analysis ◽  
Cat Striate Cortex

Snider, R. K., J. F. Kabara, B. R. Roig, and A. B. Bonds. Burst firing and modulation of functional connectivity in cat striate cortex. J. Neurophysiol. 80: 730–744, 1998. We studied the influences of the temporal firing patterns of presynaptic cat visual cortical cells on spike generation by postsynaptic cells. Multiunit recordings were dissected into the activity of individual neurons within the recorded group. Cross-correlation analysis was then used to identify directly coupled neuron pairs. The 22 multiunit groups recorded typically showed activity from two to six neurons, each containing between 1 and 15 neuron pairs. From a total of 241 neuron pairs, 91 (38%) had a shifted cross-correlation peak, which indicated a possible direct connection. Only two multiunit groups contained no shifted peaks. Burst activity, defined by groups of two or more spikes with intervals of ≤8 ms from any single neuron, was analyzed in terms of its effectiveness in eliciting a spike from a second, driven neuron. We defined effectiveness as the percentage of spikes from the driving neuron that are time related to spikes of the driven neuron. The effectiveness of bursts (of any length) in eliciting a time-related response spike averaged 18.53% across all measurements as compared with the effectiveness of single spikes, which averaged 9.53%. Longer bursts were more effective than shorter ones. Effectiveness was reduced with spatially nonoptimal, as opposed to optimal, stimuli. The effectiveness of both bursts and single spikes decreased by the same amount across measurements with nonoptimal orientations, spatial frequencies and contrasts. At similar firing rates and burst lengths, the decrease was more pronounced for nonoptimal orientations than for lower contrasts, suggesting the existence of a mechanism that reduces effectiveness at nonoptimal orientations. These results support the hypothesis that neural information can be emphasized via instantaneous rate coding that is not preserved over long intervals or over trials. This is consistent with the integrate and fire model, where bursts participate in temporal integration.

scholarly journals Screen-Cam Robust Image Watermarking with Feature-Based Synchronization

2020 ◽  
Vol 10 (21) ◽  
pp. 7494
Author(s):  
Weitong Chen ◽  
Na Ren ◽  
Changqing Zhu ◽  
Qifei Zhou ◽  
Tapio Seppänen ◽  
...  
Keyword(s):  
Image Watermarking ◽  
Gaussian Function ◽  
Geometric Distortion ◽  
Low Pass ◽  
Feature Based ◽  
Robust Image ◽  
Watermarking Scheme ◽  
Watermark Synchronization ◽  
Desynchronization Attacks ◽  
Traditional Image

The screen-cam process, which is taking pictures of the content displayed on a screen with mobile phones or cameras, is one of the main ways that image information is leaked. However, traditional image watermarking methods are not resilient to screen-cam processes with severe distortion. In this paper, a screen-cam robust watermarking scheme with a feature-based synchronization method is proposed. First, the distortions caused by the screen-cam process are investigated. These distortions can be summarized into the five categories of linear distortion, gamma tweaking, geometric distortion, noise attack, and low-pass filtering attack. Then, a local square feature region (LSFR) construction method based on a Gaussian function, modified Harris–Laplace detector, and speeded-up robust feature (SURF) orientation descriptor is developed for watermark synchronization. Next, the message is repeatedly embedded in each selected LSFR by an improved embedding algorithm, which employs a non-rotating embedding method and a preprocessing method, to modulate the discrete Fourier transform (DFT) coefficients. In the process of watermark detection, we fully utilize the captured information and extract the message based on a local statistical feature. Finally, the experimental results are presented to illustrate the effectiveness of the method against common attacks and screen-cam attacks. Compared to the previous schemes, our scheme has not only good robustness against screen-cam attack, but is also effective against screen-cam with additional common desynchronization attacks.

Estimation of Uncertainty Bounds From Cross Correlation Peak Ratio for Individual PIV Measurements

2012 ◽  
Author(s):  
John J. Charonko ◽  
Pavlos P. Vlachos
Keyword(s):  
Image Quality ◽  
Confidence Interval ◽  
Cross Correlation ◽  
Fluid Velocity ◽  
Synthetic Data ◽  
Data Sets ◽  
Correlation Peak ◽  
Individual Measurement ◽  
Quantitative Measurements ◽  
Piv Analysis

Numerous studies have established firmly that particle image velocimetry (PIV) is a robust method for non-invasive, quantitative measurements of fluid velocity, and that when carefully conducted, typical measurements can accurately detect displacements in digital images with a resolution well below a single pixel (in some cases well below a hundredth of a pixel). However, previously these estimates have only been able to provide guidance on the expected error for an average measurement under specific image quality and flow conditions. This paper demonstrates a new method for estimating the uncertainty bounds to within a given confidence interval for a specific, individual measurement. Here, the ratio of primary to secondary peak heights in a phase-only generalized cross-correlation is shown to correlate strongly with the range of observed error values for a given measurement, regardless of flow condition or image quality. Using an analytical model of the relationship derived from synthetic data sets, the uncertainty bounds at a 95% confidence interval are then computed for several artificial and experimental flow fields, and the resulting errors are shown to match closely to the predicted uncertainties. While this method stops short of being able to predict the true error for a given measurement, knowledge of the uncertainty level for a PIV experiment should provide great benefits when applying the results of PIV analysis to engineering design studies and CFD (computational fluid dynamics) validation efforts.

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