Jove
Visualize
Contact Us

Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
Convolution Properties II01:17

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
Convolution Properties I01:20

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Spectral fusing Gabor domain optical coherence microscopy based on FPGA processing.

Applied optics·2021
Same author

Spectral fusing Gabor domain optical coherence microscopy.

Optics letters·2016
Same author

Wavelet filter for improving detection performance of compression-based joint transform correlator.

Applied optics·2010
Same author

Detection performance of wavelet-based joint transform correlation.

Applied optics·2007
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 19, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Experimental verifications of a joint transform correlator using compressed reference images.

Joewono Widjaja1, Ubon Suripon

  • 1Institute of Science, Suranaree Univesity of Technology, Thailand. widjaja@sut.ac.th

Applied Optics
|October 28, 2006
PubMed
Summary

This study demonstrates the feasibility of using compressed reference images in a joint transform correlator (JTC) for accurate single and multiple target detection. While noise impacts performance, compression is viable for JTC applications.

More Related Videos

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
07:50

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Published on: January 27, 2023

Related Experiment Videos

Last Updated: Jul 19, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
07:50

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Published on: January 27, 2023

Area of Science:

  • Optics and Photonics
  • Image Processing
  • Signal Processing

Background:

  • Joint Transform Correlators (JTC) are optical systems used for pattern recognition.
  • Compressing reference images can reduce storage and processing demands.
  • Evaluating the impact of image compression on JTC performance is crucial for practical applications.

Purpose of the Study:

  • To experimentally verify the performance of a JTC using compressed reference images for target detection.
  • To assess both single-target and multiple-target detection capabilities.
  • To compare the effects of image compression versus additive noise on detection accuracy.

Main Methods:

  • Implementation of a joint transform correlator (JTC) system.
  • Utilizing compressed reference images for target matching.
  • Employing two high-contrast test scenes with varying spatial frequencies.
  • Analyzing the impact of additive noise on system performance.

Main Results:

  • Successful experimental verification of single and multiple target detection using JTC with compressed reference images.
  • Demonstrated that additive noise has a more significant negative impact on detection performance than image compression.
  • Confirmed the feasibility of using compressed reference images in JTC systems.

Conclusions:

  • Joint Transform Correlators (JTC) can effectively utilize compressed reference images for target detection tasks.
  • Image compression is a viable strategy for JTCs, with noise being a more critical factor affecting performance.
  • The experimental findings support the practical implementation of JTCs with compressed data.