RANSAC Parameter Estimation Program - MATLAB Implementation
A MATLAB implementation of the RANSAC parameter estimation algorithm with comprehensive testing images for validation and demonstration purposes
Explore MATLAB source code curated for "Matlab实现" with clean implementations, documentation, and examples.
A MATLAB implementation of the RANSAC parameter estimation algorithm with comprehensive testing images for validation and demonstration purposes
This is an efficient MATLAB implementation of the Burg algorithm for digital signal processing, featuring adjustable parameters that deliver excellent performance in spectral analysis and parameter estimation.
Complete MATLAB implementation of a full-featured spectrum detection algorithm with theoretical foundations, executable code, and experimental results. Includes signal preprocessing, frequency domain analysis, peak detection mechanisms, and parameter optimization techniques.
Implementation of Zernike subpixel recognition algorithm using MATLAB with code-oriented enhancements
MATLAB implementation of a fisheye image correction algorithm - a custom-developed application
Implementation of Expectation-Maximization (EM) algorithm for Gaussian Mixture Models (GMM) with detailed MATLAB code and theoretical explanations
Official MATLAB implementation of Compressive Sensing recovery algorithms with detailed PDF documentation describing the algorithm's principles, complete with code examples for practical understanding and implementation guidance.
This MATLAB implementation demonstrates Gabor wavelet feature extraction with comprehensive image testing capabilities. Gabor wavelets have gained significant popularity among researchers for image feature extraction due to their effectiveness in capturing essential characteristic information from images. The implementation includes multiple Gabor filter configurations with adjustable parameters for optimal feature detection.
MATLAB implementation of the Expectation-Maximization (EM) algorithm, a classic method for parameter training in stochastic process models like Hidden Markov Models (HMMs), featuring code structure and key function explanations
Implementation of LDA (Linear Discriminant Analysis) classifier using MATLAB, along with PCA technique for dimensionality reduction and comparative analysis