Image Segmentation Based on Bandelet Algorithm
MATLAB implementation for image segmentation using Bandelet transform, including required toolboxes and sample images for input/output processing
Explore MATLAB source code curated for "工具包" with clean implementations, documentation, and examples.
MATLAB implementation for image segmentation using Bandelet transform, including required toolboxes and sample images for input/output processing
The K-SVD algorithm toolkit can be installed in MATLAB's relevant paths and directly invoked for sparse representation tasks.
The latest Curvelet Transform toolkit includes all forms of Fast Discrete Curvelet Transforms (FDCT) - featuring USFFT-based, wrapping-based, and 3D FDCT implementations. This powerful toolkit serves as an excellent resource for learning curvelet transformations through practical code examples.
A multi-agent toolkit designed for direct implementation and simulation of multi-agent reinforcement learning algorithms, featuring ready-to-use APIs and modular components
A comprehensive toolkit for signal overcomplete dictionary learning algorithms, implementing all methods featured in the seminal paper "Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms." The package includes optimized implementations for simultaneous sparse approximation and group-lasso optimization techniques.
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Support Vector Machine toolkit compatible with MATLAB R2021a (formerly MATLAB 6.5), featuring comprehensive classification and regression capabilities with kernel function implementations and parameter optimization features.
A comprehensive MATLAB processing toolkit leveraging quaternion mathematics to implement various image processing functionalities, complete with a robust API for user accessibility.
A comprehensive Contourlet transform toolkit for image feature extraction supporting customizable decomposition levels - for instance, three-level decomposition extracts 17-dimensional feature vectors suitable for texture analysis and SAR image segmentation applications.
This toolkit primarily calculates musical beats while also providing additional functionality for computing music signal energy and fundamental frequency analysis, implementing digital signal processing algorithms for audio feature extraction.