24,491 research outputs found
Shadow Optimization from Structured Deep Edge Detection
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.Comment: 8 pages. CVPR 201
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
In recent years, various shadow detection methods from a single image have
been proposed and used in vision systems; however, most of them are not
appropriate for the robotic applications due to the expensive time complexity.
This paper introduces a fast shadow detection method using a deep learning
framework, with a time cost that is appropriate for robotic applications. In
our solution, we first obtain a shadow prior map with the help of multi-class
support vector machine using statistical features. Then, we use a semantic-
aware patch-level Convolutional Neural Network that efficiently trains on
shadow examples by combining the original image and the shadow prior map.
Experiments on benchmark datasets demonstrate the proposed method significantly
decreases the time complexity of shadow detection, by one or two orders of
magnitude compared with state-of-the-art methods, without losing accuracy.Comment: 6 pages, 5 figures, Submitted to IROS 201
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