Explore MATLAB source code curated for "图像处理" with clean implementations, documentation, and examples.
Source code for the InSAR image processing component developed by Delft University of Technology (Netherlands), implemented in MATLAB with comprehensive radar interferometry functionality.
Implementation of eye detection and localization in images with excellent performance using MATLAB, featuring code-based approaches
A collection of MATLAB utilities and practical examples for image processing and computer vision, featuring implementations of RANSAC algorithm, homography matrix computation, and other essential techniques.
A practical MATLAB code example demonstrating Kalman filter application in image processing, specifically for Gaussian noise reduction with detailed algorithm explanations
Complete MATLAB wavelet analysis source code with practical case studies, featuring chapter-by-chapter implementations from the book. Includes: wavelet toolbox usage, signal processing via wavelet transforms, applications in fault diagnosis/biomedical/image processing, wavelet matrix solutions, lifting wavelet transforms with implementations, multi-signal wavelet analysis, and VC environment integration with wavelet toolbox.
This PowerPoint file focuses on image processing concepts and does not require any prior programming language knowledge, making it suitable for all programming enthusiasts. It includes practical code-based implementations covering key algorithms and functions.
Classification of Polar Coordinate, Log-Polar Transformations and Their Inverse Operations: MATLAB and Image Processing Applications
This MATLAB implementation applies fuzzy rough set theory for advanced image processing, featuring noise reduction and feature enhancement capabilities through fuzzification and rough set operations.
Explore MATLAB-based image processing techniques including point detection, line detection, edge detection, edge linking, and thresholding operations with code implementation insights.
Implementation of image processing tasks in MATLAB including edge detection, image segmentation, feature extraction, and digital image recognition. The process involves applying blurring, sharpening, and histogram equalization operations to images with visualization of results. Complete image edge detection is implemented to achieve portrait or sketch effects, with graphical representation of processed outputs including key function explanations and algorithm implementations.