Explore MATLAB source code curated for "重采样" with clean implementations, documentation, and examples.
Implementing audio resampling from 22.05 KHz to 8KHz using MATLAB to adapt WAV files for voice communication systems, with code implementation details and signal processing techniques.
This particle filter algorithm implementation program is developed on the MATLAB platform and includes the complete resampling step, serving as an excellent educational resource for beginners learning particle filtering concepts and practical implementation.
I have developed a comprehensive set of 13 resampling methods for particle filtering implemented in separate MATLAB .m files. These implementations include algorithm optimizations and practical considerations for real-time applications. If you have suggestions for improvement, alternative implementations, or technical questions, your feedback would be highly valuable for enhancing these resampling techniques.
An enhanced particle filter program that replaces the traditional resampling process with genetic algorithm optimization for improved performance
Enhanced resampling procedure for particle filters that implements the resampling stage of Sequential Importance Resampling (SIR) with algorithmic improvements
Two MATLAB reference programs demonstrating signal resampling techniques for converting signals between different sampling frequencies, featuring linear interpolation and nearest-neighbor interpolation methods with implementation details.
Implementation of particle filter algorithm featuring residual resampling method for improved state estimation and target tracking applications.
Image mosaic technology spatially aligns and matches a sequence of images with overlapping areas, then performs resampling and synthesis to create a complete, high-resolution panoramic image containing information from all input images. This technology has extensive applications in photogrammetry, computer vision, remote sensing image processing, medical image analysis, and computer graphics. The image mosaic process typically consists of three key steps: image acquisition, image registration (alignment), and image blending/synthesis, where image registration serves as the fundamental component. This paper investigates two distinct image registration algorithms: feature-based registration and transform-domain-based registration, with implementation considerations for feature detection, matching, and transformation estimation.
Learning about classic particle filtering algorithms with code implementation insights
Comparison of Nearest Neighbor Algorithm with Other Resampling Techniques in Image Processing