Jove
Visualize
Contact Us
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 Concept Videos

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
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...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

You might also read

Related Articles

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

Sort by
Same author

Resolution improvements in integral microscopy with Fourier plane recording.

Optics express·2016
Same author

Free-depths reconstruction with synthetic impulse response in integral imaging.

Optics express·2015
Same author

Digital holographic testing of biconvex lenses.

Applied optics·2014
Same author

Random resampling masks: a non-Bayesian one-shot strategy for noise reduction in digital holography.

Optics letters·2013
Same author

Multi-wavelengths digital holography: reconstruction, synthesis and display of holograms using adaptive transformation.

Optics letters·2012
Same author

High-resolution far-field integral-imaging camera by double snapshot.

Optics express·2012
Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026
Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026
Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026
Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026
Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026
Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

Nonlinear matched filter based optical correlation.

B Javidi

    Applied Optics
    |June 18, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel nonlinear matched filter for optical correlation. This advanced filter enhances pattern recognition by applying nonlinear transformations to standard linear filters.

    More Related Videos

    Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
    11:22

    Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

    Published on: January 30, 2018

    Related Experiment Videos

    Last Updated: Jun 12, 2026

    Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
    14:58

    Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

    Published on: June 2, 2010

    Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
    11:22

    Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

    Published on: January 30, 2018

    Area of Science:

    • Optics and Photonics
    • Signal Processing
    • Pattern Recognition

    Background:

    • Optical correlation is a powerful technique for pattern recognition.
    • Linear matched filters are widely used but have limitations in certain applications.
    • Nonlinear transformations can potentially improve filter performance.

    Purpose of the Study:

    • To present a new nonlinear matched filter for optical correlation.
    • To investigate the performance benefits of nonlinear transformations in optical correlation.

    Main Methods:

    • A nonlinear transformation is applied to a linear matched filter.
    • The resulting nonlinear filter is implemented and tested in an optical correlation system.

    Main Results:

    • The nonlinear matched filter demonstrates improved correlation performance compared to linear filters.
    • Specific nonlinear transformations lead to enhanced discrimination capabilities.

    Conclusions:

    • Nonlinear matched filters offer a promising advancement for optical correlation.
    • This approach can lead to more robust and accurate pattern recognition systems.