Revisiting Shadow Detection: A New Benchmark Dataset for Complex World

@article{Hu2021RevisitingSD,
  title={Revisiting Shadow Detection: A New Benchmark Dataset for Complex World},
  author={Xiaowei Hu and Tianyu Wang and Chi-Wing Fu and Yitong Jiang and Qiong Wang and Pheng-Ann Heng},
  journal={IEEE Transactions on Image Processing},
  year={2021},
  volume={30},
  pages={1925-1934}
}
Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on various benchmark data, their performance is still limited for general real-world situations. In this work, we collected shadow images for multiple scenarios and compiled a new dataset of 10,500 shadow images, each with labeled ground-truth mask, for supporting shadow detection in the complex world. Our dataset covers… 
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References

SHOWING 1-10 OF 57 REFERENCES
Single-image shadow detection and removal using paired regions
TLDR
This paper addressed the problem of shadow detection and removal from single images of natural scenes by employing a region based approach, and created a new dataset with shadow-free ground truth images, which provides a quantitative basis for evaluating shadow removal.
Shadow Detection and Removal in Real Images: A Survey
Abstract. Shadow detection and removal in real scene images is always a challenging but yet intriguing problem. In contrast with the rapidly expanding and continuous interests on this area, the
Automatic Feature Learning for Robust Shadow Detection
TLDR
This work presents a practical framework to automatically detect shadows in real world scenes from a single photograph using multiple convolutional deep neural networks (ConvNets) and learns features at the super-pixel level and along the object boundaries.
Large-Scale Training of Shadow Detectors with Noisily-Annotated Shadow Examples
TLDR
A semantic-aware patch level Convolutional Neural Network architecture that efficiently trains on patch level shadow examples while incorporating image level semantic information means that the detected shadow patches are refined based on image semantics.
Shadow optimization from structured deep edge detection
  • Li Shen, T. Chua, K. Leman
  • Computer Science
    2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2015
TLDR
This paper presents a novel learning-based framework for shadow region recovery from a single image by using a structured CNN learning framework, and proposes and forms shadow/bright measure to model complex interactions among image regions.
Automatic Shadow Detection and Removal from a Single Image
TLDR
A framework to automatically detect and remove shadows in real world scenes from a single image using multiple convolutional deep neural networks (ConvNets) based on a novel model which accurately models the shadow generation process in the umbra and penumbra regions.
Distraction-Aware Shadow Detection
TLDR
Experimental results demonstrate that the proposed Distraction-aware Shadow Detection Network can boost shadow detection performance, by effectively suppressing the detection of false positives and false negatives, achieving state-of-the-art results.
Learning to recognize shadows in monochromatic natural images
TLDR
Results show shadowed areas of an image can be identified using proposed monochromatic cues, which 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.
Large Scale Shadow Annotation and Detection Using Lazy Annotation and Stacked CNNs
TLDR
This paper proposes a stacked Convolutional Neural Network architecture that efficiently trains on patch level shadow examples while incorporating image level semantic information, which means that the detected shadow patches are refined based on image semantics.
Detecting Ground Shadows in Outdoor Consumer Photographs
TLDR
The key hypothesis is that the types of materials constituting the ground in outdoor scenes is relatively limited, most commonly including asphalt, brick, stone, mud, grass, concrete, etc, so the appearances of shadows on the ground are not as widely varying as general shadows and thus, can be learned from a labelled set of images.
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