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Related Concept Videos

Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

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...
Entropy and the Second Law of Thermodynamics01:26

Entropy and the Second Law of Thermodynamics

Consider an isolated system in which a hot object is placed in contact with a cold one. This is an irreversible process that eventually leads both objects to reach the same equilibrium temperature. It is crucial to note that the constituents of any substance exhibit increased disorder at higher temperatures. As a cold substance absorbs heat, its constituents become more disordered. The energy transfer from a hotter object to a cooler one increases the system's disorder or randomness. This...
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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.
Entropy02:39

Entropy

Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
Entropy01:18

Entropy

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...
Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...

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Related Experiment Video

Updated: Jun 28, 2026

Building Up Skin Models for Numerous Applications - from Two-Dimensional (2D) Monoculture to Three-Dimensional (3D) Multiculture
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Entropy-optimized texture models.

Sebastian Zambal1, Katja Bühler, Jirí Hladůvka

  • 1VRVis Research Center for Virtual Reality and Visualization.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 6, 2008
PubMed
Summary

This study introduces an entropy-optimized texture model (ETM) for robustly matching statistical models to images. ETMs outperform active appearance models (AAMs), reducing errors and handling variations from different scanners and modalities.

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Published on: July 25, 2013

Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Computational Anatomy

Background:

  • Robust statistical model fitting to unseen images is essential for accurate analysis.
  • Traditional methods like active appearance models (AAMs) struggle with low contrast, fuzzy features, and intensity variations.
  • Dense texture sampling improves robustness but is insufficient alone.

Purpose of the Study:

  • To introduce a novel entropy-optimized texture model (ETM) for improved image-model matching.
  • To enhance the robustness of statistical shape and appearance models in medical imaging.
  • To address limitations of existing texture-based fitting methods.

Main Methods:

  • Developed an entropy-optimized texture model (ETM) by mapping gray values for optimal information entropy representation.
  • Employed Bayes' law for matching the ETM to unseen images.
  • Validated the ETM using diverse training datasets including cardiac and spinal structures.

Main Results:

  • ETMs demonstrated superior performance compared to traditional AAMs.
  • Reduced average point-to-contour error in image segmentation tasks.
  • Showcased enhanced ability to manage significant texture variations across different scanners and imaging modalities.

Conclusions:

  • The proposed ETM offers a more robust and accurate approach for statistical model fitting in medical image analysis.
  • ETMs provide better adaptability to variations in image acquisition, enhancing clinical applicability.
  • This method improves the reliability of shape and appearance model matching in challenging imaging scenarios.