Description

This paper addresses the problem of recognizing and removing shadows from monochromatic natural images from a learning based perspective. Without chromatic information, shadow recognition and removal are extremely challenging in the literature, mainly due to the missing of invariant color cues. Natural scenes make this problem even harder due to the complex illumination condition and ambiguity from many near-black objects. In this paper, a learning based shadow recognition and removal scheme is proposed to tackle the challenges above. Firstly, we propose to use both shadow-variant and invariant cues from illumination, texture and odd order derivative characteristics to recognize shadows. Such features are used to train a classifier via boosting a decision tree and integrated into a Conditional Random Field, which can enforce local consistency over pixel labels. Secondly, a Gaussian model is introduced to remove the recognized shadows from monochromatic natural scenes. The proposed scheme is evaluated using both qualitative and quantitative results based on a novel database of hand-labeled shadows, with comparisons to the existing state-of-the-art schemes. We show that the shadowed areas of a monochromatic image can be accurately identified using the proposed scheme, and highquality shadow-free images can be precisely recovered after shadow removal.