Explore MATLAB source code curated for "纹理特征" with clean implementations, documentation, and examples.
Comprehensive feature extraction methods including color features, texture features, shape features, and SIFT features, accompanied by a compact CBIR retrieval system implementation code and detailed explanatory presentation materials.
The extraction methods for Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Gradient Co-occurrence Matrix (GLGCM) represent significant texture feature extraction techniques implemented using MATLAB, with detailed code structure and algorithmic explanations.
Implementation of texture feature extraction methodologies through Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Gradient Co-occurrence Matrix (GLGCM) with algorithmic enhancements
Image Inpainting Algorithm and Simulation Code - 2011 Research on "Adaptive Image Inpainting Algorithm Based on Texture Features" with MATLAB Implementation
This wavelet transform-based approach extracts texture eigenvalues and can be modified to extract other feature vectors, featuring MATLAB implementations of multi-level decomposition and energy calculation algorithms suitable for beginners.
The LTP operator extracts texture features from images, demonstrating significant effectiveness against illumination variations through its three-valued encoding approach.
Source code for grayscale image texture feature detection based on wavelet multi-scale analysis, featuring customizable wavelet parameters for algorithm optimization
Feature extraction process that captures texture and shape features from distinct labeled regions within images, enabling advanced image analysis through computational methods.
A MATLAB-based image processing program designed for extracting texture and color features from images, featuring algorithms for feature analysis and pattern recognition.
Content-Based Image Retrieval technology with MATLAB implementations for color and texture feature extraction, including experimental results