Explore MATLAB source code curated for "计算机视觉" with clean implementations, documentation, and examples.
3D reconstruction technology represents a crucial field in computer vision research, with stereo vision serving as a fundamental technique that utilizes stereo image pairs captured by left and right cameras to reconstruct 3D scene information based on geometric relationships, often implemented through disparity mapping and triangulation algorithms.
Computer Vision: Practical CCD camera calibration tool with robust implementation
Orthogonal correlation object detection employs orthogonal correlation methods to identify target positions in images. Suitable for various computer vision applications including video object detection, visual object detection, object localization, visual object tracking, video object tracking, image matching, and image registration. Implementation typically involves calculating correlation matrices between target templates and image regions using orthogonal basis functions.
MATLAB source code for computing homography matrices, suitable for computer vision applications like image registration. The implementation includes point correspondence matching and robust estimation techniques.
Detailed open-source computer vision object recognition code with comprehensive implementation examples and clear code annotations
Vehicle License Plate Recognition (VLPR) is a technology that detects vehicles on monitored roads and automatically extracts license plate information, including Chinese characters, English letters, Arabic numerals, and plate colors. As a vital component of modern intelligent transportation systems, VLPR employs digital image processing, pattern recognition, and computer vision techniques to analyze vehicle images or video sequences captured by cameras, extracting unique license plate numbers for each vehicle through algorithmic processing.
Monte Carlo methods and their practical applications in image processing and computer vision with algorithmic implementations
Pattern Recognition and Image Normalization: Computer Vision Preprocessing Techniques with Implementation Insights
Applied in the field of computer vision, this technique utilizes homography matrix data to merge two images into a single composite image, with implementation involving feature detection, matching, and perspective transformation algorithms.
This MATLAB code implements standard Hough Transform for detecting lines in binary images, demonstrating the working mechanism of this fundamental computer vision technique. The implementation uses polar coordinate representation and accumulator arrays to identify linear patterns.