Computer Vision Assignment: Image Stitching with Weighted Smooth Blending
Supporting Lancashire juniors with CV coursework, featuring image stitching techniques and weighted smooth blending algorithms for original implementation
Explore MATLAB source code curated for "图像拼接" with clean implementations, documentation, and examples.
Supporting Lancashire juniors with CV coursework, featuring image stitching techniques and weighted smooth blending algorithms for original implementation
SIFT-based Image Fusion using MATLAB+VC Hybrid Programming - Achieving image stitching functionality through integrated MATLAB feature extraction and VC++ implementation.
Image stitching implementation using SIFT algorithm for feature point extraction and matching, solving homography matrix, performing affine transformation based on computed homography, and finally stitching images. The code includes comprehensive comments for easy understanding, making it suitable for developers interested in computer vision applications.
MATLAB implementation for panoramic image stitching using asymptotic region-based approach
SIFT feature point extraction code with feature matching between two images, suitable for applications like image stitching. Implementation includes keypoint detection, descriptor computation, and matching algorithms.
MATLAB-based image stitching code utilizing the SIFT algorithm, thoroughly tested and operational, effectively merges overlapping images with superior performance
This image stitching approach relies on grayscale information to merge two overlapping images into a single panoramic view with wider coverage, typically implemented through feature detection and transformation algorithms.
Image stitching technique utilizing cross-correlation functions for automated matching point detection and seamless panorama creation.
Source Code - MATLAB Source Code for Image Stitching Implementation
A robust image stitching program that utilizes SIFT algorithm for feature point extraction, implements mismatch filtering algorithms, and visualizes matched points with connecting lines. Key implementation note: The main function is match.m - after running the main function, enter match('image1.jpg', 'image2.jpg') in the command window.