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

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
Manipulation and Analysis01:21

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
Levels of Use of a GIS01:29

Levels of Use of a GIS

Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

You might also read

Related Articles

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

Sort by
Same author

Effective optical processor for computing image moments at TV rate: use in handwriting recognition.

Applied optics·2010
Same author

Processing measurements of the directional content of Fourier spectra.

Applied optics·2010
Same author

Optical statistical classifiers using coherent and incoherent light.

Applied optics·2010
Same author

Processing optical data matrices.

Applied optics·2010
Same author

Optical computing and investigations on writing.

Applied optics·2010
Same author

Optical pattern recognition and clustering: Karhunen-Loève analysis.

Applied optics·2010

Related Experiment Video

Updated: Jun 15, 2026

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Statistical spatial filtering: application to aerial photographs.

P Y Baurès, J Duvernoy

    Applied Optics
    |March 6, 2010
    PubMed
    Summary
    This summary is machine-generated.

    Spatial filtering enhances image classification by identifying dominant eigenvectors from spectral data. This method efficiently extracts key statistical features for principal image analysis.

    Related Experiment Videos

    Last Updated: Jun 15, 2026

    Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
    09:44

    Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

    Published on: October 16, 2018

    Area of Science:

    • Image processing and computer vision
    • Statistical pattern recognition
    • Optical physics

    Background:

    • Traditional signal identification relies on deterministic approaches.
    • Image classification often requires complex feature extraction.
    • Spatial filtering is a known technique for signal processing.

    Purpose of the Study:

    • To demonstrate the applicability of spatial filtering to image classification.
    • To introduce a method for extracting dominant eigenvectors from image spectral content.
    • To link statistical properties of Fourier spectra to efficient eigenvector estimation.

    Main Methods:

    • Applying spatial filtering to image classification tasks.
    • Utilizing a classifier to extract dominant eigenvectors from spectral data.
    • Employing optical representations of eigenvectors as spatial filters.
    • Analyzing statistical stability and intrinsic dimensionality of Fourier spectra.

    Main Results:

    • Spatial filtering effectively classifies images based on spectral content.
    • Dominant eigenvectors representing statistical features were successfully extracted.
    • Principal images containing the most information were obtained using eigenvector-based spatial filters.
    • A correlation was found between spectral statistical properties and fast eigenvector estimation.

    Conclusions:

    • Spatial filtering is a versatile tool applicable to both deterministic signal identification and image classification.
    • The proposed method provides an efficient way to extract informative features from images.
    • Understanding the statistical properties of Fourier spectra aids in rapid analysis and feature extraction.