Shadow detection and removal are important yet challenging computer vision tasks. Existing methods simultaneously contend with the brightness and color information of input image while treating ...different regions of input images equally. We argue that these operations fail to effectively extract the relationship between shadow and non-shadow regions. To relieve these problems, this paper proposes a shadow-aware decomposed transformer network for shadow detection and removal. The network decomposes the input image into brightness and color maps using its bright channel, which are concatenated with the original image as the input data, enabling the network to pay balanced attention to the brightness and color information when calculating the relationship between regions. Additionally, given that the correlation matrix of the transformer measures the relative dependency between regions, the proposed shadow-aware transformer block can extract the relationship between shadow and non-shadow regions more effectively by retaining the specific elements of the correlation matrix. We conduct extensive experiments on three shadow detection benchmark datasets and two shadow removal benchmark datasets. Experimental results show that the proposed method performs favorably against state-of-the-art methods. Codes have been made available at https://github.com/XIAOWANG914/SADT.
•The brightness and color are essential for shadow detection and removal.•Performance of network declines when different regions are treated equally.•The input is decomposed into brightness and color map.•The network focuses on the relationship between shadow and non-shadow regions.
► An up-to-date survey of shadow detection algorithms. ► A novel taxonomy based on object/environment dependency and implementation domain. ► A comparative, metric-based evaluation of representative ...algorithms.
Cast shadows need careful consideration in the development of robust dynamic scene analysis systems. Cast shadow detection is critical for accurate object detection in video streams, and their misclassification can cause errors in segmentation and tracking. Many algorithms for shadow detection have been proposed in the literature; however a complete, comparative evaluation of existing approaches is lacking. This paper presents a comprehensive survey of shadow detection methods, organised in a novel taxonomy based on object/environment dependency and implementation domain. In addition a comparative evaluation of representative algorithms, based on quantitative and qualitative metrics is presented to evaluate the algorithms on a benchmark suite of indoor and outdoor video sequences.
Kansas-born Pauline Benton (1898-1974) was encouraged by her father, one of America's earliest feminist male educators, to reach for the stars. Instead, she reached for shadows. In 1920s Beijing, she ...discovered shadow theatre (piyingxi), a performance art where translucent painted puppets are manipulated by highly trained masters to cast coloured shadows against an illuminated screen. Finding that this thousand-year-old forerunner of motion pictures was declining in China, Benton believed she could save the tradition by taking it to America. Mastering the male-dominated art form in China, Benton enchanted audiences eager for the exotic in Depression-era America. Her touring company, Red Gate Shadow Theatre, was lauded by theatre and art critics and even performed at Franklin Roosevelt's White House. Grant Hayter-Menzies traces Benton's performance history and her efforts to preserve shadow theatre as a global cultural treasure by drawing on her unpublished writings, the recollections of her colleagues, the testimonies of shadow masters who survived China's Cultural Revolution, as well as young innovators who have carried on Benton's pioneering work.
Drawing on medieval Arabic sources and earlier scholarship, this book is a study of the life and work of Ibn Dāniyāl (d. 1310). It also presents the first full English translation of his shadow play ..."The Phantom.".
We focus on addressing the problem of shadow removal for an image, and attempt to make a weakly supervised learning model that does not depend on the pixelwise-paired training samples, but only uses ...the samples with image-level labels that indicate whether an image contains shadow or not. To this end, we propose a deep reciprocal learning model that interactively optimizes the shadow remover and the shadow detector to improve the overall capability of the model. On the one hand, shadow removal is modeled as an optimization problem with a latent variable of the detected shadow mask. On the other hand, a shadow detector can be trained using the prior from the shadow remover. A self-paced learning strategy is employed to avoid fitting to intermediate noisy annotation during the interactive optimization. Furthermore, a color-maintenance loss and a shadow-attention discriminator are both designed to facilitate model optimization. Extensive experiments on the pairwise ISTD dataset, SRD dataset, and unpaired USR dataset demonstrate the superiority of the proposed deep reciprocal model.
Shadow removal is an essential task in computer vision and computer graphics. Recent shadow removal approaches all train convolutional neural networks (CNN) on real paired shadow/shadow-free or ...shadow/shadow-free/mask image datasets. However, obtaining a large-scale, diverse, and accurate dataset has been a big challenge, and it limits the performance of the learned models on shadow images with unseen shapes/intensities. To overcome this challenge, we present SynShadow, a novel large-scale synthetic shadow/shadow-free/matte image triplets dataset and a pipeline to synthesize it. We extend a physically-grounded shadow illumination model and synthesize a shadow image given an arbitrary combination of a shadow-free image, a matte image, and shadow attenuation parameters. Owing to the diversity, quantity, and quality of SynShadow, we demonstrate that shadow removal models trained on SynShadow perform well in removing shadows with diverse shapes and intensities on some challenging benchmarks. Furthermore, we show that merely fine-tuning from a SynShadow-pre-trained model improves existing shadow detection and removal models. Codes are publicly available at https://github.com/naoto0804/SynShadow .
The objective of this work is to detect shadows in images. We pose this as the problem of labeling image regions, where each region corresponds to a group of superpixels. To predict the label of each ...region, we train a kernel Least-Squares Support Vector Machine (LSSVM) for separating shadow and non-shadow regions. The parameters of the kernel and the classifier are jointly learned to minimize the leave-one-out cross validation error. Optimizing the leave-one-out cross validation error is typically difficult, but it can be done efficiently in our framework. Experiments on two challenging shadow datasets, UCF and UIUC, show that our region classifier outperforms more complex methods. We further enhance the performance of the region classifier by embedding it in a Markov Random Field (MRF) framework and adding pairwise contextual cues. This leads to a method that outperforms the state-of-the-art for shadow detection. In addition we propose a new method for shadow removal based on region relighting. For each shadow region we use a trained classifier to identify a neighboring lit region of the same material. Given a pair of lit-shadow regions we perform a region relighting transformation based on histogram matching of luminance values between the shadow region and the lit region. Once a shadow is detected, we demonstrate that our shadow removal approach produces results that outperform the state of the art by evaluating our method using a publicly available benchmark dataset.
In this paper, we present a novel shadow removal system for single natural images as well as color aerial images using an illumination recovering optimization method. We first adaptively decompose ...the input image into overlapped patches according to the shadow distribution. Then, by building the correspondence between the shadow patch and the lit patch based on texture similarity, we construct an optimized illumination recovering operator, which effectively removes the shadows and recovers the texture detail under the shadow patches. Based on coherent optimization processing among the neighboring patches, we finally produce high-quality shadow-free results with consistent illumination. Our shadow removal system is simple and effective, and can process shadow images with rich texture types and nonuniform shadows. The illumination of shadow-free results is consistent with that of surrounding environment. We further present several shadow editing applications to illustrate the versatility of the proposed method.
Automatic Shadow Detection and Removal from a Single Image Khan, Salman H.; Bennamoun, Mohammed; Sohel, Ferdous ...
IEEE transactions on pattern analysis and machine intelligence,
2016-March-1, 2016-Mar, 2016-3-1, 20160301, Volume:
38, Issue:
3
Journal Article
Peer reviewed
Open access
We present a framework to automatically detect and remove shadows in real world scenes from a single image. Previous works on shadow detection put a lot of effort in designing shadow variant and ...invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The features are learned at the super-pixel level and along the dominant boundaries in the image. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow masks. Using the detected shadow masks, we propose a Bayesian formulation to accurately extract shadow matte and subsequently remove shadows. The Bayesian formulation is based on a novel model which accurately models the shadow generation process in the umbra and penumbra regions. The model parameters are efficiently estimated using an iterative optimization procedure. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.
We perform transaction-level analyses of entrusted loans, one of the largest components of shadow banking in China. Entrusted loans involve firms with privileged access to cheap capital channeling ...funds to less privileged firms, and the increase when credit is tight. Nonaffiliated loans have much higher interest rates than both affiliated loans and official bank loans, and they largely flow into real estate. The pricing of entrusted loans, especially of nonaffiliated loans, incorporates fundamental and informational risks. Stock market reactions suggest that both affiliated and nonaffiliated loans are fairly compensated investments.
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