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.
Very recently, @cite_25 detected shadows using a patch-level CNN and a shadow prior map generated from hand-crafted features, while @cite_5 developed scGAN with a sensitivity parameter to adjust weights in the loss functions. Although the shadow detection accuracy keeps improving on the benchmark datasets @cite_17 @cite_24 , existing methods may still misrecognize black objects as shadows and miss unobvious shadows in the testing images. The most recent work by @cite_5 emphasized the importance of reasoning global semantics for shadow detection. Compared to this work, we suggest to consider the directional variance when analyzing the spatial context. Results show that our method can further outperform @cite_5 in terms of both the accuracy and the BER value.
@cite_24 @cite_5 @cite_25 @cite_17
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We introduce scGAN, a novel extension of conditional Generative Adversarial Networks (GAN) tailored for the challenging problem of shadow detection in images. Previous methods for shadow detection focus on learning the local appearance of shadow regions, while using limited local context reasoning in the form of pairwise potentials in a Conditional Random Field. In contrast, the proposed adversarial approach is able to model higher level relationships and global scene characteristics. We train a shadow detector that corresponds to the generator of a conditional GAN, and augment its shadow accuracy by combining the typical GAN loss with a data loss term. Due to the unbalanced distribution of the shadow labels, we use weighted cross entropy. With the standard GAN architecture, properly setting the weight for the cross entropy would require training multiple GANs, a computationally expensive grid procedure. In scGAN, we introduce an additional sensitivity parameter w to the generator. The proposed approach effectively parameterizes the loss of the trained detector. The resulting shadow detector is a single network that can generate shadow maps corresponding to different sensitivity levels, obviating the need for multiple models and a costly training procedure. We evaluate our method on the large-scale SBU and UCF shadow datasets, and observe up to 17 error reduction with respect to the previous state-of-the-art method. 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. 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 authors are unaware of any comprehensive surveys on this topic. This paper aimed to give a comprehensive and critical survey of current shadow detection and removal methods. Algorithms are categorized into there sets by their different functions and assumptions about the scenes. A discussion of reasonable evaluation is given at the end of this survey. 1 Introduction Shadows and shadings in images have long been disruptive to computer vision algorithms. They appear as surface features, when in fact they are caused by the interaction between light and objects. This may lead to problems in scene understanding, object segmentation, tracking, recognition, etc. Because of the undesirable effects of shadows on image analysis, much attention was paid to the area of shadow detection and removal over the past decades and covered many specific applications such as traffic surveillance [1, 2], face recognition [3, 4, 5] and image segmentation [6]. In spite of these extensive studies, more researches focus on providing a general method for arbitrary scene images and thereby obtaining visually pleasing shadow free images. In contrast with the rapidly expanding interests on shadow removal, no comprehensive survey is reported on this particular topic. A list of recent work on this area is reported in [7] but algorithm details are missed. A survey was conducted by A. Prati [8, 9] on the moving cast shadow detection, which is the part of current interests. This paper aims to give a relatively comprehensive stud Successfully detecting shadows in still images is challenging yet has wide applications. Shadow properties and features are very important for shadow detection and processing. The aim of this work is to find some new physical properties of shadows and use them as shadow features to design an effective shadow detection method for outdoor color images. We observe that although the spectral power distribution (SPD) of daylight and that of skylight are quite different, in each channel, the spectrum ratio of the point-wise product of daylight SPD with sRGB color matching functions (CMFs) to the point-wise product of skylight SPD with sRGB CMFs roughly approximates a constant. This further leads to that the ratios of linear sRGB pixel values of surfaces illuminated by daylight (in non-shadow regions) to those illuminated by skylight (in shadow regions) equal to a constant in each channel. Following this observation, we calculated the spectrum ratios under various Sun angles and further found out four new shadow properties. With these properties as shadow features, we developed a simple shadow detection method to quickly locate shadows in single still images. In our method, we classify an edge as a shadow or non-shadow edge by verifying whether the pixel values on both sides of the Canny edges satisfy the three shadow verification criteria derived from the shadow properties. Extensive experiments and comparison show that our method outperforms state-of-the-art shadow detection methods. HighlightsWe found the ratios of regions lit by daylight vs. by skylight equal to a constant.We calculated spectrum ratios and found out four new shadow properties.Following the new shadow properties, we propos