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

Classification of Systems-II01:31

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,
Classification of Signals01:30

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...
Methods of Classification and Identification01:28

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 Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
Classification of Systems-I01:26

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:
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by
Same author

Texture discrimination based upon an assumed stochastic texture model.

IEEE transactions on pattern analysis and machine intelligence·2011
Same author

Cluster validation for unsupervised stochastic model-based image segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Operational rate-distortion performance for joint source and channel coding of images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Reliable transmission of high-quality video over ATM networks.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·1994
Same author

Adaptive entropy coded subband coding of images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·1992

Related Experiment Video

Updated: May 29, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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

Published on: August 30, 2013

A maximum likelihood approach to texture classification.

A L Vickers1, J W Modestino

  • 1Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12181.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary

A novel texture classification method utilizes spatial gray-level co-occurrence probability matrices. This approach approximates optimal classifiers and shows effectiveness on real-world image data.

More Related Videos

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Related Experiment Videos

Last Updated: May 29, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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

Published on: August 30, 2013

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Texture classification is crucial for image analysis.
  • Existing methods may not fully leverage statistical properties of textures.
  • The spatial gray-level co-occurrence probability matrix offers rich textural information.

Purpose of the Study:

  • To introduce a new texture classification approach.
  • To utilize spatial gray-level co-occurrence probability matrices for enhanced classification.
  • To approximate a statistically optimum classifier for texture analysis.

Main Methods:

  • Measurement of spatial gray-level co-occurrence probability matrices.
  • Application of assumed stochastic models for texture.
  • Approximation to the maximum likelihood classifier.

Main Results:

  • Demonstration of the approach's efficacy.
  • Experimental validation using real-world texture data.
  • Successful texture classification based on co-occurrence probabilities.

Conclusions:

  • The proposed method provides an effective approach to texture classification.
  • Spatial gray-level co-occurrence probability matrices are valuable features for texture analysis.
  • The method offers a practical approximation to optimal statistical classification.