Title

Learning-Based Shadow Recognition And Removal From Monochromatic Natural Images

Keywords

conditional random field; decision tree; Gaussian model; mono-chromatic image; nature scene; Shadow recognition; shadow removal

Abstract

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 this paper, 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-mentioned. First, 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. Second, 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 high-quality shadow-free images can be precisely recovered after shadow removal.

Publication Date

12-1-2017

Publication Title

IEEE Transactions on Image Processing

Volume

26

Issue

12

Number of Pages

5811-5824

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TIP.2017.2737321

Socpus ID

85028963646 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85028963646

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