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

Probability Distributions01:32

Probability Distributions

13.0K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
13.0K
Binomial Probability Distribution01:15

Binomial Probability Distribution

16.4K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
16.4K
Probability Histograms01:17

Probability Histograms

13.6K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
13.6K
Applications of Integration to Probability Density Functions01:27

Applications of Integration to Probability Density Functions

99
Continuous probability distributions are used to model random variables that can take on any real value within a specified range. These variables do not take on isolated or countable values but rather exist on a continuum. For example, the height of an individual can be measured with increasing precision—such as 163.5 or 165.25 centimeters—demonstrating that height is a continuous random variable.The behavior of such variables is described using a probability density function (PDF),...
99
Aggregates Classification01:29

Aggregates Classification

1.1K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.1K
Classification of Systems-II01:31

Classification of Systems-II

547
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,
547

You might also read

Related Articles

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

Sort by
Same author

Automatic left ventricle volume and mass quantification from 2D cine-MRI: Investigating papillary muscle influence.

Medical engineering & physics·2024
Same author

An Innovative Possibilistic Fingerprint Quality Assessment (PFQA) Filter to Improve the Recognition Rate of a Level-2 AFIS.

Entropy (Basel, Switzerland)·2023
Same author

A novel pre-processing approach based on colour space assessment for digestive neuroendocrine tumour grading in immunohistochemical tissue images.

Polish journal of pathology : official journal of the Polish Society of Pathologists·2022
Same author

Optic disc detection and segmentation using saliency mask in retinal fundus images.

Computers in biology and medicine·2022
Same author

A New Collaborative Classification Process for Microcalcification Detection Based on Graphs and Knowledge Propagation.

Journal of digital imaging·2022
Same author

A new conditional region growing approach for microcalcification delineation in mammograms.

Medical & biological engineering & computing·2021

Related Experiment Video

Updated: Mar 19, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.3K

Iterative Refinement of Possibility Distributions by Learning for Pixel-Based Classification.

Bassem Alsahwa, Basel Solaiman, Shaban Almouahed

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 16, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an iterative refinement of possibility distributions by learning (IRPDL) method for pixel-based image classification. IRPDL achieves high recognition rates, comparable to state-of-the-art methods, with reduced complexity.

    More Related Videos

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.0K
    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

    Related Experiment Videos

    Last Updated: Mar 19, 2026

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.3K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.0K
    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

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Pixel-based image classification is crucial for medical imaging analysis.
    • Existing methods like region growing and Markov random fields have limitations.
    • Possibilistic reasoning offers a framework for incorporating expert knowledge.

    Purpose of the Study:

    • To propose and evaluate a novel iterative refinement of possibility distributions by learning (IRPDL) approach.
    • To enhance pixel-based image classification accuracy and efficiency.
    • To leverage possibilistic reasoning and expert knowledge for improved classification.

    Main Methods:

    • The IRPDL approach utilizes possibilistic reasoning and ground possibilistic seeds learning.
    • Possibility distributions are incrementally updated and refined to construct a set of seeds.
    • Performance is evaluated on synthetic and RIDER Breast MRI datasets.

    Main Results:

    • IRPDL achieved a recognition rate of 87.3%, closely matching the reference Markovian method (88.8%).
    • IRPDL outperformed region growing (83.9%) and semi-supervised fuzzy pattern matching (84.7%).
    • The method requires fewer parameters and exhibits reduced computational complexity.

    Conclusions:

    • IRPDL is a competitive and efficient method for pixel-based image classification.
    • The approach effectively integrates expert knowledge through possibilistic reasoning.
    • IRPDL shows significant potential for applications in medical image analysis.