Shadow Detection Source Code
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Resource Overview
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.
Detailed Documentation
This document describes the source code from Dr. Lalonde's ECCV 2010 paper publication at Carnegie Mellon University. The implementation encompasses multiple computer vision stages: image segmentation algorithms partition input images into coherent regions, followed by feature extraction processes that compute color ratio distributions, texture descriptors using filter banks, and statistical skewness measurements. The AdaBoost classifier then integrates these multi-modal features with road scene contextual information to perform shadow detection. The code achieves high-precision detection results through its meticulous feature engineering, though the comprehensive processing pipeline results in longer execution times. This implementation serves as an important reference for shadow detection methodologies, particularly demonstrating effective feature combination strategies and machine learning integration in computer vision systems.
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