Explore MATLAB source code curated for "熵" with clean implementations, documentation, and examples.
A comprehensive computational program for calculating entropy, joint entropy, conditional entropy, and average mutual information with detailed algorithmic implementations
Entropy (including Approximate Entropy and Sample Entropy) concepts explained for beginners. Comprehensive coverage of theoretical foundations and practical code implementation approaches.
Extract five key texture features using Gray Level Co-occurrence Matrix (GLCM): entropy, homogeneity, correlation, energy, and contrast
This internationally sourced MATLAB toolbox efficiently computes mutual information and entropy for random variables, featuring probabilistic distribution functions, stochastic process modeling, and signal processing applications with comprehensive documentation.
Common image quality assessment metrics include entropy, weighted peak signal-to-noise ratio (WPSNR), weighted structural similarity (WSSIM), structural similarity index (SSIM), cross-entropy, and spatial frequency - these represent widely adopted evaluation criteria in image processing applications.
Implementation of arithmetic coding in MATLAB with calculations for average code length, entropy, and coding efficiency metrics
Adaptive Particle Swarm Optimization algorithm introduces entropy and average particle distance concepts to standard PSO, significantly improving convergence speed while reducing local optimum entrapment, making it more effective for solving complex optimization problems. Implementation typically involves dynamic inertia weight adjustments and diversity maintenance mechanisms through entropy-based calculations.
Calculating entropy from four directional gray-level co-occurrence matrices (GLCM), deriving mean entropy values to construct feature matrices, and implementing texture segmentation through cluster analysis for image processing applications.
MATLAB Nonlinear Chaotic Analysis Toolbox: Comprehensive implementation for time delay estimation, embedding dimension calculation, maximum Lyapunov exponent determination, correlation dimension analysis, and entropy computation. Eliminates the need for custom programming with built-in functions for chaotic time series analysis.
Huffman coding implementation for digital image processing. This program performs Huffman encoding on input images, displays the original image's entropy and average code length after encoding, and reconstructs images from the encoded data. Built using C++/MATLAB hybrid programming with pre-compiled MATLAB library files, requiring no additional C++ linking. Includes source code, original test images, and encoded result images.