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Related Concept Videos

Entropy01:18

Entropy

2.7K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
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Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
3.0K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.7K
Entropy and Solvation02:05

Entropy and Solvation

7.1K
The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Entropy within the Cell01:22

Entropy within the Cell

11.0K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
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Classification of Leukocytes01:30

Classification of Leukocytes

2.2K
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...
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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Learning and Entropy-Based Texture Features for Color Image Classification.

Emma Lhermitte1, Mirvana Hilal1, Ryan Furlong2

  • 1Univ Angers, LARIS, SFR MATHSTIC, F-49000 Angers, France.

Entropy (Basel, Switzerland)
|November 11, 2022
PubMed
Summary

We introduce a novel multivariate entropy measure for RGB image texture analysis. This new method shows promising results when compared to deep learning approaches in image classification tasks.

Keywords:
RGB imagesbiomedical dataclassificationdeep learningentropytexture

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Area of Science:

  • Computer Vision
  • Image Analysis
  • Texture Classification

Background:

  • Entropy measures irregularity and is effective for texture analysis in grayscale images.
  • Existing entropy measures are limited for color images, and comparative studies with deep learning are scarce.
  • Machine learning classifiers benefit from entropy-based texture analysis for high accuracy.

Purpose of the Study:

  • To propose a new multivariate entropy-based measure for RGB color image texture analysis.
  • To compare the performance of the proposed entropy measure against modern deep learning methods for image classification.
  • To address the gap in comparative analyses of entropy-based and deep learning methods for RGB images.

Main Methods:

  • Developed a novel multivariate entropy measure for RGB images, extending existing unidimensional signal methods.
  • Applied the proposed entropy measure to RGB image classification.
  • Compared classification results with several state-of-the-art deep learning methods.

Main Results:

  • The proposed multivariate entropy-based method achieved promising results in RGB image classification.
  • Demonstrated the potential of entropy-based approaches for color image texture analysis.
  • Highlighted the need for further research into color space extensions.

Conclusions:

  • The novel multivariate entropy measure offers a viable approach for RGB image texture classification.
  • The proposed method shows competitive performance against deep learning techniques.
  • Future work should explore extending this measure to other color spaces.