Abstract
This paper addresses the problem of recognizing shadows from monochromatic natural images. Without chromatic information, shadow classification is very challenging because the invariant color cues are unavailable. Natural scenes make this problem even harder because of ambiguity from many near black objects. We propose to use both shadow-variant and shadow-invariant cues from illumination, textural and odd order derivative characteristics. Such features are used to train a classifier from boosting a decision tree and integrated into a Conditional random Field, which can enforce local consistency over pixel labels. The proposed approach is evaluated using both qualitative and quantitative results based on a novel database of hand-labeled shadows. Our results show shadowed areas of an image can be identified using proposed monochromatic cues.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.