Explore MATLAB source code curated for "图像序列" with clean implementations, documentation, and examples.
Performing Gaussian background analysis on image sequences to remove background and noise, with reference to the paper "Automatic Temporal Segmentation for Content-Based Video Coding." Includes implementation details for background modeling and foreground extraction.
Compress a raw video image sequence to generate a compressed file; decompress this compressed file to produce a display image; compare the display image with the original image in terms of quality, compressed file size, and other relevant metrics.
A MATLAB simulation program for cylindrical panoramic stitching of image sequences, implementing feature detection, transformation, and blending for seamless panoramic image generation.
Image stitching is a technique that synthesizes a wide-angle scene image from a sequence of discretized images captured from the real world. When two images with overlapping correlations are available, image stitching aims to merge them into a single composite image. The panoramic stitching process typically involves spatial projection, matching and alignment, and overlay fusion procedures, which can be implemented using feature detection algorithms like SIFT or ORB for accurate registration.
The core concept of motion estimation involves partitioning each frame of an image sequence into non-overlapping macroblocks, assuming uniform pixel displacement within each macroblock. For every macroblock, the algorithm searches within a specified range in the reference frame to identify the most similar block (matching block) based on predefined matching criteria. The relative displacement between the matching block and current block defines the motion vector. During video compression, storing only motion vectors and residual data enables complete reconstruction of the current block.
This MATLAB simulation program implements cylindrical panoramic stitching for image sequences, featuring a cylindrical projection module that provides insights for implementing alternative projection formulas.
Implement background modeling on image sequences using Gaussian Mixture Model and save the results (with accompanying images). The implementation involves probability distribution fitting and foreground-background separation through adaptive parameter estimation.
Cylindrical Panorama Stitching Algorithm for Image Sequences - Includes MATLAB simulation program demonstrating feature extraction, image registration, and blending techniques.
MATLAB implementation of Struck tracking algorithm with included image sequences, ready-to-run code optimized for newer MATLAB versions (R2018b+ recommended)
Implementation of Unscented Kalman Filter (UKF) for human body tracking in image sequences, developed following classical UKF algorithms. This program serves as an introductory learning resource for UKF implementation, featuring enhanced processing modules and noise handling capabilities.