► 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.
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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Benton's performance history and her efforts to preserve shadow theatre as a global cultural treasure by drawing on her unpublished writings, the recollections of her colleagues, the testimonies of shadow masters who survived China's Cultural Revolution, as well as young innovators who have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.
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.
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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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The relevance of the problem of measuring shadow activity and its significance for the purposes of macroeconomic analysis and forecasting is emphasized. The importance of forming an adequate ...methodological base for measuring shadow activity and developing an information support system for its research is substantiated. The existing approaches to the definition of shadow activity and methods of its measurement are analyzed. The possibility of building an information base and obtaining appropriate estimates of shadow activity on the basis of resource and use tables developed at the national level is evaluated. The possibility of using the resource approach as the most objective method of reflecting the volume and components of shadow activity at the sectoral and macro levels is studied. The conditions for the use of the resource approach are determined, taking into account the specifics of the industry features of the formation of output and intermediate consumption indicators. The possibility of constructing dynamic series of indicators constructed using the resource approach for analytical purposes is evaluated.
The removal of shadows from images is a classic problem in computer vision, aiming to restore the lighting in shadowed areas, thereby reducing the information interference and loss caused by the ...presence of shadows. In recent years, numerous excellent shadow removal algorithms have emerged, particularly with the rapid development of deep learning technology, which has disrupted traditional physics-based approaches and significantly improved the effectiveness of shadow removal. In this paper, we conduct a comprehensive survey of shadow removal methods published from 2017 to the present. We first introduce background knowledge about image shadow removal, providing detailed explanations of both physics-based and learning-based shadow removal methods. We analyze and compare these algorithms from both quantitative and qualitative perspectives, reassessing all models that provided open-source result sets according to uniform criteria. Additionally, we introduce commonly used datasets and evaluation metrics in the field. Finally, we discuss applications of shadow removal in specific scenarios, along with research challenges and opportunities in this domain.
•Surveys taxonomy of recently image shadow removal techniques, datasets and performance metrics.•Systematic quantitative evaluations are summarized.•The impacts, applications and importance of shadow removal are explored.•Challenges and future direction are explored and analyzed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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 .
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