Explore MATLAB source code curated for "阴影检测" with clean implementations, documentation, and examples.
This code implements shadow detection and elimination using HSV color space conversion. It extracts both foreground and background images, leveraging HSV's human perception-like color representation for accurate shadow identification while maintaining computational simplicity.
Core functions include precise vehicle positioning and shadow detection, specifically designed for highway image processing with robust performance in shadow-rich environments
Implementation of shadow detection and removal using RGB color model algorithm, featuring display of foreground, background, and shadow-free moving objects with enhanced code-level descriptions
This repository contains the official implementation from Dr. Lalonde's ECCV 2010 paper (Carnegie Mellon University), featuring comprehensive image processing workflows including image segmentation, feature extraction, and AdaBoost classification. The code employs sophisticated features such as color ratios, texture analysis, and skewness measurements, integrating road scene context for robust shadow detection. The implementation demonstrates high detection accuracy while requiring substantial computational time, making it valuable for shadow detection research and development.
Background: This shadow detection method was proposed by Mr. J.W. Hofstee and Mr. E.J. Hanten in their paper "Shadow Segmentation Based on Image Transformation for Illumination Changes" presented at the International Conference on Agricultural Engineering in Zurich (July 6-10). Key Technology: Implements shadow detection through illumination-invariant image transformation. The algorithm successfully detects shadows in certain images but shows varying performance across different image types, indicating potential areas for optimization in practical implementations.