Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Signals

Classification of Signals

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...

¹H NMR Signal Multiplicity: Splitting Patterns

¹H NMR Signal Multiplicity: Splitting Patterns

When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...

Methods of Classification and Identification

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Systems-II

Classification of Systems-II

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,

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single stretching vibration...

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

Statistical spatial filtering: application to aerial photographs.

Applied optics·2010

Same author

Processing optical data matrices.

Applied optics·2010

Same author

Optical computing and investigations on writing.

Applied optics·2010

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

Search research articles

Related Experiment Video

Updated: Jun 16, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Optical pattern recognition and clustering: Karhunen-Loève analysis.

|February 19, 2010

Summary

This summary is machine-generated.

The Karhunen-Loève transform of Fourier spectra enhances optical data analysis for scriptor recognition and text dating. This method creates a classifying space for improved clustering and understanding data evolution.

More Related Videos

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Jun 16, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Published on: February 23, 2024

Area of Science:

Computer Vision
Pattern Recognition
Digital Image Processing

Background:

Fourier transforms offer initial dimensional reduction for optical data but lack sensitivity to statistical variations crucial for classification.
Statistical variations in data are key for clustering and recognition tasks, which are not adequately addressed by Fourier analysis alone.

Purpose of the Study:

To develop a more effective method for clustering and recognizing optical data, specifically scriptors.
To explore the utility of the Karhunen-Loève transform in conjunction with Fourier spectra for advanced data analysis.
To demonstrate the capability of this approach for dating texts based on the inner evolution of data.

Main Methods:

Applying a Fourier transform to optical data to obtain an initial description.
Utilizing a Karhunen-Loève transform on the resulting Fourier spectra to create a more classifying feature space.
Testing the method on examples of writings for scriptor recognition and text dating.

Main Results:

Clustering of optical data, particularly scriptor recognition, is successfully achieved within a 2-D Karhunen-Loève space.
The inner evolution of data within a specific class can be described in a 3-D Karhunen-Loève space.
The proposed method demonstrates potential for dating historical texts.

Conclusions:

The Karhunen-Loève transform applied to Fourier spectra provides a powerful tool for optical data classification and recognition.
This technique significantly improves upon standard Fourier analysis by incorporating statistical variations for enhanced clustering.
The dimensionality reduction and feature extraction capabilities are valuable for applications such as scriptor identification and historical document analysis.