Shadow Optimization from Structured Deep Edge Detection

TL;DR

This paper introduces a novel structured CNN framework for shadow detection that leverages local shadow edge structures and global region interactions, significantly improving shadow region recovery from single images.

Contribution

It presents a new learning-based approach combining structured CNNs and global interaction modeling for more accurate shadow detection and recovery.

Findings

Achieves state-of-the-art results on major shadow benchmarks.

Effectively models complex interactions among image regions.

Improves local consistency and avoids spurious labels.

Abstract

Local structures of shadow boundaries as well as complex interactions of image regions remain largely unexploited by previous shadow detection approaches. In this paper, we present a novel learning-based framework for shadow region recovery from a single image. We exploit the local structures of shadow edges by using a structured CNN learning framework. We show that using the structured label information in the classification can improve the local consistency of the results and avoid spurious labelling. We further propose and formulate a shadow/bright measure to model the complex interactions among image regions. The shadow and bright measures of each patch are computed from the shadow edges detected in the image. Using the global interaction constraints on patches, we formulate a least-square optimization problem for shadow recovery that can be solved efficiently. Our shadow recovery method achieves state-of-the-art results on the major shadow benchmark databases collected under various conditions.