scholarly journals Phase correlation functions: FFT vs. FHT

ACTA IMEKO ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 87 ◽  
Author(s):  
Giuseppe Schirripa Spagnolo ◽  
Lorenzo Cozzella ◽  
Fabio Leccese
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Correlation Function ◽  
Pattern Matching ◽  
Phase Correlation ◽  
Critical Element ◽  
Hartley Transform ◽  
Subpixel Accuracy ◽  
2D Fft ◽  
Good Substitute

<p><span lang="EN-GB">The ability to process an image is a crucial skill in many measurement activities. In image processing or pattern recognition, Fast Fourier Transform (FFT) is widely used. In particular, the Phase Only Correlation (POC) method demonstrates high robustness and subpixel accuracy in pattern matching. However, there is a disadvantage in the required memory machine because of the calculation of 2D-FFT. In applications in which the use of memory is a critical element, Fast Hartley Transform (FHT) seems to be a good substitute. In this context, the use of Hartley’s transform can be of interest for apps implemented on portable systems e.g. smartphones. In this article, we present a comparison of the implementations of the phase correlation function using FFT and FHT. Particular attention is given to the analytical steps necessary to implement the POC by means of the Hartley transform.</span></p>

Pattern Recognition for Classification and Matching of Car Tires

2005 ◽  
Vol 33 (1) ◽  
pp. 2-17 ◽  
Author(s):  
D. Colbry ◽  
D. Cherba ◽  
J. Luchini
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Pattern Matching ◽  
Fourier Transforms ◽  
Two Dimensions ◽  
Multiple Sources ◽  
Matching Process ◽  
Visual Searches ◽  
New Feature ◽  
2D Fft

Abstract Commercial databases containing images of tire tread patterns are currently used by product designers, forensic specialists and product application personnel to identify whether a given tread pattern matches an existing tire. Currently, this pattern matching process is almost entirely manual, requiring visual searches of extensive libraries of tire tread patterns. Our work explores a first step toward automating this pattern matching process by building on feature analysis techniques from computer vision and image processing to develop a new method for extracting and classifying features from tire tread patterns and automatically locating candidate matches from a database of existing tread pattern images. Our method begins with a selection of tire tread images obtained from multiple sources (including manufacturers' literature, Web site images, and Tire Guides, Inc.), which are preprocessed and normalized using Two-Dimensional Fast Fourier Transforms (2D-FFT). The results of this preprocessing are feature-rich images that are further analyzed using feature extraction algorithms drawn from research in computer vision. A new, feature extraction algorithm is developed based on the geometry of the 2D-FFT images of the tire. The resulting FFT-based analysis allows independent classification of the tire images along two dimensions, specifically by separating “rib” and “lug” features of the tread pattern. Dimensionality of (0,0) indicates a smooth treaded tire with no pattern; dimensionality of (1,0) and (0,1) are purely rib and lug tires; and dimensionality of (1,1) is an all-season pattern. This analysis technique allows a candidate tire to be classified according to the features of its tread pattern, and other tires with similar features and tread pattern classifications can be automatically retrieved from the database.

High‐resolution velocity determination: Statistical phase correlation and image processing

Geophysics ◽  
1996 ◽  
Vol 61 (4) ◽  
pp. 1115-1127 ◽  
Author(s):  
Igor B. Morozov ◽  
Scott B. Smithson
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Phase Correlation ◽  
Statistical Hypothesis ◽  
Original Form ◽  
Velocity Spectrum ◽  
Statistical Hypothesis Testing ◽  
Simple Estimate ◽  
Optimal Velocity ◽  
Velocity Spectra

We address three areas of the problem of the stacking velocity determination: (1) the development of a new high‐resolution velocity determination technique, (2) the choice of an optimal velocity trial scenario, and (3) a unified approach to the comparison of time‐velocity spectra produced by various methods. We present a class of high‐resolution coherency measures providing five‐eight times better velocity resolution than conventional measures. The measure is based on the rigorous theory of statistical hypothesis testing and on the statistics of directional data. In its original form, our method analyzes only the phase distributions of the data, thus making unnecessary careful spherical divergence corrections and other normalization procedures. Besides the statistical one, we develop an “instantaneous” version of the conventional coherency measure. This measure is based on the concept of the trace envelope, thus eliminating the need for an averaging procedure. Finally, we design a hybrid high‐resolution coherency measure, incorporating the latter and the statistical one. Carrying out a systematic comparison of various measures of coherency, we present a simple estimate of an attainable velocity resolution. Based on this estimate, we define an optimal velocity grid, providing uniform coverage of all details of the time‐velocity spectrum. To facilitate quantitative comparisons of different coherency functions, we develop a unified normalization approach, based on techniques known in image processing. Described methods are tested on synthetic and field data. In both cases, we obtained a remarkable improvement in the time‐velocity resolution. The methods are general, very simple in implementation, and robust and reliable in application.

A Modified S-Transform for EEG Signals Analysis

2015 ◽  
Vol 12 (03) ◽  
pp. 1550021 ◽  
Author(s):  
M. A. Al-Manie ◽  
W. J. Wang
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Energy Concentration ◽  
Biomedical Data ◽  
Short Time Fourier Transform ◽  
Phase Information ◽  
Eeg Signals ◽  
Time Frequency ◽  
S Transform ◽  
Short Time

Due to the advantages offered by the S-transform (ST) distribution, it has been recently successfully implemented for various applications such as seismic and image processing. The desirable properties of the ST include a globally referenced phase as the case with the short time Fourier transform (STFT) while offering a higher spectral resolution as the wavelet transform (WT). However, this estimator suffers from some inherent disadvantages seen as poor energy concentration with higher frequencies. In order to improve the performance of the distribution, a modification to the existing technique is proposed. Additional parameters are proposed to control the window's width which can greatly enhance the signal representation in the time–frequency plane. The new estimator's performance is evaluated using synthetic signals as well as biomedical data. The required features of the ST which include invertability and phase information are still preserved.

A Robust Generic Method for Grid Detection in White Light Microscopy Malassez Blade Images in the Context of Cell Counting

2014 ◽  
Vol 21 (1) ◽  
pp. 239-248 ◽  
Author(s):  
Ambroise Marin ◽  
Emmanuel Denimal ◽  
Stéphane Guyot ◽  
Ludovic Journaux ◽  
Paul Molin
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Light Microscopy ◽  
White Light ◽  
Hough Transform ◽  
High Variability ◽  
Cell Counting ◽  
Cell Segmentation ◽  
Primary Measurement

AbstractIn biology, cell counting is a primary measurement and it is usually performed manually using hemocytometers such as Malassez blades. This work is tedious and can be automated using image processing. An algorithm based on Fourier transform filtering and the Hough transform was developed for Malassez blade grid extraction. This facilitates cell segmentation and counting within the grid. For the present work, a set of 137 images with high variability was processed. Grids were accurately detected in 98% of these images.

Evaluation of optical properties of fluorescent nanofiber using image-processing technique

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ali Shams Nateri ◽  
Laleh Asadi
Keyword(s):  
Image Processing ◽  
Optical Properties ◽  
Fourier Transform ◽  
Processing Technique ◽  
Fluorescent Dye ◽  
Fluorescence Microscope ◽  
Color Histogram ◽  
Image Processing Technique ◽  
Content Type ◽  
Fluorescence Microscope Image

Purpose The purpose of this study is evaluate the optical properties of polyacrylonitrile (PAN) nanofibers containing fluorescent agents such as fluorescent dye and carbon quantum dots (CQDs) by using image-processing technique of Fluorescence microscope image. Design/methodology/approach The fluorescence microscope image of the pure PAN, PAN/CQDs and PAN/fluorescent dye nanofibers composite was analyzed using several image-processing techniques such as color histogram, lookup table (LUT), Fourier transform, RGB profile and surface plot analysis. Findings The fluorescence microscope image indicates that the fluorescence emission of nanocomposites depends on the type of fluorescent agent. The fluorescence intensity of nanofiber containing CQDs is more than nanofiber containing fluorescent dye. Various image-processing methods provide similar results for optical property of nanocomposites. Analyzing the LUT, the blue value of CQDs/PAN nanocomposite image was significantly higher than other nanocomposites. This was confirmed by other methods such as Fourier transform, color histogram and 3D topography of the electrospun nanofibers. According to analysis of colorimetric parameters, higher negative value of b* indicates bluer color for CQDs/PAN nanofibers than other nanocomposites. The obtained results indicate that the image-processing technique can be used to evaluate the optical property of fluorescent nanocomposite. Originality/value This study evaluates the optical properties of fluorescent nanocomposites by using image-processing techniques such as Fourier transform, color histogram, RGB profiles, LUT, surface plot and histogram analysis.

scholarly journals APPLICATION OF WEIGHTING WINDOWS FOR SPECTRAL DENSITY ESTIMATION

2021 ◽  
Author(s):  
Vera Meerson ◽  
O. Khorolsky
Keyword(s):  
Fourier Transform ◽  
Correlation Function ◽  
Spectral Density ◽  
Density Estimation ◽  
Time Windows ◽  
Spectral Density Estimation ◽  
Important Time

The article discusses the application of some of the most important time windows for spectral density estimation determined by the correlogram method (from correlation function) and the periodogram method (from direct fourier transform).

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