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

Quadratic Models01:23

Quadratic Models

324
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
324
Methods of Medium Optimization01:28

Methods of Medium Optimization

52
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
52
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

434
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....
434
Classification of Signals01:30

Classification of Signals

1.6K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.6K
Linearization and Approximation01:26

Linearization and Approximation

195
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
195
Classification of Systems-II01:31

Classification of Systems-II

565
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
565

You might also read

Related Articles

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

Sort by
Same author

Intraosseous spindle cell lipoma of the maxilla: case report and review of the literature.

International journal of oral and maxillofacial surgery·2025
Same author

ADAPTIVE SMALL-ANIMAL SPECT/CT.

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium·2015
Same author

Spatial Pileup Considerations for Pixellated Gamma -ray Detectors.

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium·2015
Same author

Approximations to ideal-observer performance on signal-detection tasks.

Applied optics·2008
Same author

High-pass filters give histograms with positive kurtosis.

Optics letters·2007
Same author

Ideal-observer performance under signal and background uncertainty.

Information processing in medical imaging : proceedings of the ... conference·2004
Same journal

Multi-module collaborative optimization-driven fast speckle correlation imaging in variable environments.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Secrecy performance analysis of NOMA-UWOC systems over a vertically stratified WGG oceanic turbulence channel.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Backscattering of plane waves in a composite system containing a rough surface and anisotropic scatterers.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Aspherical surface construction methods based on extended Jacobi polynomials.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

OCT sidelobe suppression method based on dual-path phase sinusoidal modulation and minimum value fusion.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Optical design concepts using wavelength-selective diffractive optics to enable miniaturized multimodal endoscopic imaging across separated spectral ranges.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
See all related articles

Related Experiment Video

Updated: Apr 3, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Method for optimizing channelized quadratic observers for binary classification of large-dimensional image datasets.

M K Kupinski, E Clarkson

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
    |September 15, 2015
    PubMed
    Summary
    This summary is machine-generated.

    We developed a new gradient-based method for optimizing channels in channelized quadratic observers (CQO), making it feasible for high-dimensional image analysis. This approach enhances computational efficiency and accuracy for Gaussian image data.

    More Related Videos

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.7K

    Related Experiment Videos

    Last Updated: Apr 3, 2026

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    8.1K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.7K

    Area of Science:

    • Image analysis
    • Statistical modeling
    • Machine learning

    Background:

    • Channelized Quadratic Observers (CQO) are effective for image analysis but computationally intensive for high-dimensional data.
    • Optimizing observer channels is crucial for accurate image data interpretation.
    • Existing methods for channel optimization face challenges with large datasets.

    Purpose of the Study:

    • To present a novel, computationally feasible method for computing optimized channels for CQO.
    • To adapt the channel computation method for high-dimensional Gaussian image data.
    • To explore gradient-based algorithms for optimizing channels using various figures of merit (FOMs).

    Main Methods:

    • Developed a general method for calculating optimal CQO channels, particularly for Gaussian data.
    • Presented gradient-based algorithms for channel optimization across five information-based FOMs.
    • Derived analytic solutions for optimal channels under equal mean conditions, identifying common solutions for efficiency.

    Main Results:

    • Demonstrated the feasibility of the new method for high-dimensional image data.
    • Showcased gradient-based algorithms for optimizing channels using Jeffrey's divergence (J).
    • Confirmed that optimal channels for three FOMs are identical under equal mean conditions, simplifying computation.

    Conclusions:

    • The proposed gradient-based channel optimization method makes CQO feasible for large-dimensional image data.
    • Dimensionality reduction via optimized channels improves statistical estimation and computational efficiency.
    • This work enables more practical application of CQO in complex image analysis tasks.