2,549 research outputs found
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
Direction-aware Spatial Context Features for Shadow Detection
Shadow detection is a fundamental and challenging task, since it requires an
understanding of global image semantics and there are various backgrounds
around shadows. This paper presents a novel network for shadow detection by
analyzing image context in a direction-aware manner. To achieve this, we first
formulate the direction-aware attention mechanism in a spatial recurrent neural
network (RNN) by introducing attention weights when aggregating spatial context
features in the RNN. By learning these weights through training, we can recover
direction-aware spatial context (DSC) for detecting shadows. This design is
developed into the DSC module and embedded in a CNN to learn DSC features at
different levels. Moreover, a weighted cross entropy loss is designed to make
the training more effective. We employ two common shadow detection benchmark
datasets and perform various experiments to evaluate our network. Experimental
results show that our network outperforms state-of-the-art methods and achieves
97% accuracy and 38% reduction on balance error rate.Comment: Accepted for oral presentation in CVPR 2018. The journal version of
this paper is arXiv:1805.0463
Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data
This paper presents a new method for shadow removal using unpaired data,
enabling us to avoid tedious annotations and obtain more diverse training
samples. However, directly employing adversarial learning and cycle-consistency
constraints is insufficient to learn the underlying relationship between the
shadow and shadow-free domains, since the mapping between shadow and
shadow-free images is not simply one-to-one. To address the problem, we
formulate Mask-ShadowGAN, a new deep framework that automatically learns to
produce a shadow mask from the input shadow image and then takes the mask to
guide the shadow generation via re-formulated cycle-consistency constraints.
Particularly, the framework simultaneously learns to produce shadow masks and
learns to remove shadows, to maximize the overall performance. Also, we
prepared an unpaired dataset for shadow removal and demonstrated the
effectiveness of Mask-ShadowGAN on various experiments, even it was trained on
unpaired data.Comment: Accepted to ICCV 201
Shape-driven segmentation of the arterial wall in intravascular ultrasound images
Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction,
and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built
shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior,
we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach
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