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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...
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In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...
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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.
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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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In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
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2D Tsallis Entropy for Image Segmentation Based on Modified Chaotic Bat Algorithm.

Zhiwei Ye1, Juan Yang1, Mingwei Wang2

  • 1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel 2D Tsallis entropy method for image segmentation, enhanced by a modified chaotic Bat algorithm. This approach effectively addresses noise and computational costs, outperforming existing methods for accurate image analysis.

Keywords:
2D Tsallis entropyBat algorithmLevy flightchaotic processimage segmentation

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Area of Science:

  • Computer Vision
  • Image Analysis
  • Computational Intelligence

Background:

  • Image segmentation is crucial in computer vision.
  • Entropy-based methods, like Tsallis entropy, are effective but 1D versions lack spatial correlation and are noise-sensitive.
  • Existing 2D entropy methods and meta-heuristics (GA, PSO, ACO, DE) have limitations in computational cost or performance.

Purpose of the Study:

  • To propose an improved 2D Tsallis entropy method for image segmentation.
  • To enhance computational efficiency using a meta-heuristic algorithm.
  • To evaluate the proposed method against various established techniques.

Main Methods:

  • Utilizing 2D Tsallis entropy as a constrained optimization problem.
  • Employing a modified chaotic Bat algorithm (MCBA) to find optimal thresholds by maximizing an objective function.
  • Comparing MCBA-optimized 2D Tsallis entropy with 1D/2D Fisher, maximum entropy, cross entropy, fuzzy entropy, and other meta-heuristics (PSO, GA, ACO, DE).

Main Results:

  • The proposed MCBA-based 2D Tsallis entropy method achieves superior performance in image segmentation.
  • The method effectively incorporates spatial information and mitigates noise sensitivity.
  • Demonstrated feasibility and effectiveness on actual and infrared images.

Conclusions:

  • The modified chaotic Bat algorithm significantly accelerates and optimizes 2D Tsallis entropy for image segmentation.
  • This novel approach offers a robust and efficient solution compared to existing methods.
  • The MCBA-based 2D Tsallis entropy is a promising technique for advanced image analysis applications.