Image Registration Using Mutual Information with MATLAB Implementation
MATLAB-based image registration utilizing mutual information for accurate alignment of medical, remote sensing, or computer vision images
Explore MATLAB source code curated for "图像" with clean implementations, documentation, and examples.
MATLAB-based image registration utilizing mutual information for accurate alignment of medical, remote sensing, or computer vision images
Pseudo-Zernike Moment Transform - Computes pseudo-Zernike moments of images as features, with implementation involving orthogonal polynomial calculations and complex radial basis functions.
Cubic spline interpolation MATLAB programming that directly generates visual plots and outputs spline functions with detailed implementation code
Fingerprint database collected using AES2410 sensor with approximately 500 raw images, plus about 5,000 images each with no background and optical background variants. Essential for developing fingerprint recognition algorithms with MATLAB image processing and pattern matching functions.
Gaussian Pyramid: Laplacian Pyramid Decomposition MATLAB Implementation; This MATLAB source code performs pyramid decomposition on input images using a multi-level approach where the "level" parameter specifies the decomposition depth. The algorithm works by recursively applying Gaussian smoothing and downsampling operations.
Mean Shift algorithm implementation for image segmentation and smoothing, demonstrated using example images of horses with code-level insights.
Implementation of image color clustering using the K-means algorithm with flexible test image replacement capability
DCT watermarking algorithm with practical implementation using 8x8 block-based DCT domain approach, designed for embedding 64x64 watermark images
Implementation of 2D stationary wavelet transform for images, including decomposition of source images and reconstruction with signal processing algorithms
Using Radon transform to extract rotation-invariant image features, restoring images to their original orientation through inverse transformation for subsequent recognition tasks