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Raoult, Didier, and Jean-Marc Rolain. "The Paradigm of the Shadoks and Antibiotic Resistance." Clinical Infectious Diseases 69, no. 9 (2019): 1641. http://dx.doi.org/10.1093/cid/ciz183.
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George, Sam. "Vampires, Demons and the Disappearing Shadow in Folkloric Fictions of the Long Nineteenth Century." Gothic Studies 22, no. 1 (2020): 31–48. http://dx.doi.org/10.3366/gothic.2020.0036.
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This article interrogates manifestations of the shadow soul, combining anthropological, magical and religious approaches to the shadow in an analysis of a small group of interrelated Gothic texts. It enters the shadowy worlds of Peter Schlemihl, protagonist of Hans Andersen's ‘The Shadow’, Dracula, and Peter Pan, who, on being detached from their shadows, have themselves become shades. This journey into the kingdom of shadows will uncover the folkloric demon, the vampire, the physiognomist, the satyr, the pagan Pan, and the Devil himself. To read these narratives through Frazer's The Golden Bough challenges conventional psychoanalytic readings and offers an alternative, folkloric perspective, one which lays bare the ritualistic and magical beliefs that inform late nineteenth- and early twentieth-century Gothic.
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Denis, Sébastien. "(Dé)synchro-Shadok. Animation, musique concrète et ingénierie à l’ORTF." Intermédialités, no. 19 (October 9, 2012): 103–20. http://dx.doi.org/10.7202/1012658ar.
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La série Les Shadoks de Jacques Rouxel, produite en France entre 1968 et 1974, est emblématique de l’esprit expérimental de Pierre Schaeffer, à la tête à l’époque du Service de la Recherche de l’ORTF. Si la série est largement axée sur la désynchronisation entre image et son pour mettre en valeur la musique concrète, Schaeffer a pour sa part tenté de synchroniser ses équipes avec des découvertes technologiques (notamment l’animographe) tout en désynchronisant l’organisation de son service et en resynchronisant le public avec un esprit moderne, en rupture avec le conformisme audiovisuel des années Pompidou.
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Sharma, Nazuk. "Luxury implications of showcasing a product with its “cast” shadow." Journal of Consumer Marketing 33, no. 7 (2016): 507–16. http://dx.doi.org/10.1108/jcm-06-2016-1847.
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Purpose This paper aims to investigate the role of showcasing a product with its cast shadow (formed in the ad’s background by the advertised product) on consumer product perceptions. Design/methodology/approach Three experimentally designed studies, incorporating two product categories, demonstrate the impact of visual presentation of a product with its shadow on consumer evaluations. A total of 203 participants (MTurkers, and student respondents at a southern university) provided data for these studies through questionnaires (online as well as paper-pencil formats). Findings Findings reveal that the presence of a product’s cast shadow in the ad frame increases its visual acuity, which in turn enhances its luxury perceptions. Downstream, a product shadow’s presence positively impacts its overall evaluations, through enhanced product luxury perceptions. Also, consumers with high Centrality of Visual Product Aesthetics (CVPA) demonstrate a stronger liking for such product presentations. Research limitations/implications The current findings not only demonstrate the positive impact of product shadows on consumer perceptions, but also enrich the luxury and aesthetics literature streams. Practical implications Advertisers often subjectively use product shadows as stylistic tools in marketing communications. This research offers some practical guidelines to use shadows in fostering product luxury perceptions and better target aesthetically-sensitive consumers. Originality/value Advertising research suggests that visual styling and presentation of products significantly impacts consumer perceptions. However, the role of product shadows has not yet been empirically examined. This paper makes an attempt to test whether and how product shadows impact consumer perceptions.
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Fushio, Yuki, and Mitsunori Matsushita. "Dynamic Shadow Generation System Based on Shape Recognition." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 2 (2012): 212–18. http://dx.doi.org/10.20965/jaciii.2012.p0212.
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This paper proposes a dynamic shadow generation system that combines the shadow of an object with the artificial shadows of virtual characters. In our proposed system, infrared light is used to capture an object’s shadow in order to discriminate between a shadow generated by an object and a virtual shadow generated by the system. Our proposed system determines areas in which shadows do not appear and then the shadows of virtual characters are added to these areas. This paper presents the basic idea of the system, the shadow generation algorithm that it uses, and an empirical study on the sharpness of a generated shadow, along with the distance between an object and a screen used in this system.
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Lekshmi, Ajayan, and C. Christopher Seldev. "Shadow Removal Using Inpainting and Interpolation Technique." Applied Mechanics and Materials 626 (August 2014): 32–37. http://dx.doi.org/10.4028/www.scientific.net/amm.626.32.
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Shadows are viewed as undesired information that strongly affects images. Shadows may cause a high risk to present false color tones, to distort the shape of objects, to merge, or to lose objects. This paper proposes a novel approach for the detection and removal of shadows in an image. Firstly the shadow and non shadow region of the original image is identified by HSV color model. The shadow removal is based on exemplar based image inpainting. Finally, the border between the reconstructed shadow and the non shadow areas undergoes bilinear interpolation to yield a smooth transition between them. They would lead to a better fitting of the shadow and non shadow classes, thus resulting in a potentially better reconstruction quality.
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Edwards, B. H. "The Shadow of Shadows." positions: east asia cultures critique 11, no. 1 (2003): 11–49. http://dx.doi.org/10.1215/10679847-11-1-11.
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Zhou, Kaixuan, Roderik Lindenbergh, and Ben Gorte. "Automatic Shadow Detection in Urban Very-High-Resolution Images Using Existing 3D Models for Free Training." Remote Sensing 11, no. 1 (2019): 72. http://dx.doi.org/10.3390/rs11010072.
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Up-to-date 3D city models are needed for many applications. Very-high-resolution (VHR) images with rich geometric and spectral information and a high update rate are increasingly applied for the purpose of updating 3D models. Shadow detection is the primary step for image interpretation, as shadow causes radiometric distortions. In addition, shadow itself is valuable geometric information. However, shadows are often complicated and environment-dependent. Supervised learning is considered to perform well in detecting shadows when training samples selected from these images are available. Unfortunately, manual labeling of images is expensive. Existing 3D models have been used to reconstruct shadows to provide free, computer-generated training samples, i.e., samples free from intensive manual labeling. However, accurate shadow reconstruction for large 3D models consisting of millions of triangles is either difficult or time-consuming. In addition, due to inaccuracy and incompleteness of the model, and different acquisition time between 3D models and images, mislabeling refers to training samples that are shadows but labeled as non-shadows and vice versa. We propose a ray-tracing approach with an effective KD tree construction to feasibly reconstruct accurate shadows for a large 3D model. An adaptive erosion approach is first provided to remove mislabeling effects near shadow boundaries. Next, a comparative study considering four classification methods, quadratic discriminant analysis (QDA) fusion, support vector machine (SVM), K nearest neighbors (KNN) and Random forest (RF), is performed to select the best classification method with respect to capturing the complicated properties of shadows and robustness to mislabeling. The experiments are performed on Dutch Amersfoort data with around 20% mislabels and the Toronto benchmark by simulating mislabels from inverting shadows to non-shadows. RF is tested to give robust and best results with 95.38% overall accuracy (OA) and a value of 0.9 for kappa coefficient (KC) for Amersfoort and around 96% OA and 0.92 KC for Toronto benchmarks when no more than 50% of shadows are inverted. QDA fusion and KNN are tested to be robust to mislabels but their capability to capture complicated properties of shadows is worse than RF. SVM is tested to have a good capability to separate shadow and non-shadows but is largely affected by mislabeled samples. It is shown that RF with free-training samples from existing 3D models is an automatic, effective, and robust approach for shadow detection from VHR images.
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Yamashita, Yukihiro, Fumihiko Sakaue, and Jun Sato. "Fast 3D Shape Recovery from Shadows Projected on Arbitrary Curved Surfaces." International Journal of Virtual Reality 9, no. 1 (2010): 1–6. http://dx.doi.org/10.20870/ijvr.2010.9.1.2754.
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The shadow based 3D surface reconstruction methods usually assume that shadows are projected on planar surfaces. However, shadows are often projected on curved surfaces in the real scene. Recently, the shadow graph has been proposed for representing shadow information efficiently, and for recovering 3D shapes from shadows projected on curved surfaces. Unfortunately, the method requires a large computational cost and is weak to the image intensity noises. In this paper, we introduce 1D shadow graphs which can represent shadow information quite efficiently, and can be used for recovering 3D shapes with much smaller computational costs than before. We also extend our method, so that we can recover 3D shape quite accurately by using shading information as well as shadow information. The proposed method is tested by using the real and synthetic images.
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Han, Hongyin, Chengshan Han, Xucheng Xue, et al. "A Mixed Property-Based Automatic Shadow Detection Approach for VHR Multispectral Remote Sensing Images." Applied Sciences 8, no. 10 (2018): 1883. http://dx.doi.org/10.3390/app8101883.
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Shadows in very high-resolution multispectral remote sensing images hinder many applications, such as change detection, target recognition, and image classification. Though a wide variety of significant research has explored shadow detection, shadow pixels are still more or less omitted and are wrongly confused with vegetation pixels in some cases. In this study, to further manage the problems of shadow omission and vegetation misclassification, a mixed property-based shadow index is developed for detecting shadows in very high-resolution multispectral remote sensing images based on the difference of the hue component and the intensity component between shadows and nonshadows, and the difference of the reflectivity of the red band and the near infrared band between shadows and vegetation cover in nonshadows. Then, the final shadow mask is achieved, with an optimal threshold automatically obtained from the index image histogram. To validate the effectiveness of our approach for shadow detection, three test images are selected from the multispectral WorldView-3 images of Rio de Janeiro, Brazil, and are tested with our method. When compared with other investigated standard shadow detection methods, the resulting images produced by our method deliver a higher average overall accuracy (95.02%) and a better visual sense. The highly accurate data show the efficacy and stability of the proposed approach in appropriately detecting shadows and correctly classifying shadow pixels against the vegetation pixels for very high-resolution multispectral remote sensing images.
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Cha, Do Wan, Sung Nam Oh, Keun Ha Choi, Kab Il Kim, Kyung Soo Kim, and Soo Hyun Kim. "Design and Evaluation of the Unmanned Technology Research Centre Exoskeleton (UTRCEXO)." Applied Mechanics and Materials 436 (October 2013): 345–50. http://dx.doi.org/10.4028/www.scientific.net/amm.436.345.
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In this paper, we present an exoskeleton called the Unmanned Technology Research Centre Exoskeleton (UTRCEXO), which utilizes a shadow walking assistance mechanism. The shadow walking assistance mechanism shadows the operators walking after visible movements by the operator, such as a heel-off event or a toe-off event. The UTRCEXO determines the operators walking intention with the toe-off event, when the toe leaves the ground, and then shadows the operators walking. The UTRCEXO implementing the shadow walking assistance mechanism shadows the first step of the operator immediately.
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Casati, Roberto. "Some varities of shadow illusions: Split shadows, occluded shadows, stolen shadows, and shadows of shadows." Perception 41, no. 3 (2012): 357–60. http://dx.doi.org/10.1068/p7156.
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Casati, Roberto. "Some Varieties of Shadow Illusions: Split Shadows, Occluded Shadows, Stolen Shadows, and Shadows of Shadows." Perception 41, no. 3 (2012): 357–60. http://dx.doi.org/10.1068/p7156web.
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Norman, J. Farley, Thomas E. Dawson, and Shane R. Raines. "The Perception and Recognition of Natural Object Shape from Deforming and Static Shadows." Perception 29, no. 2 (2000): 135–48. http://dx.doi.org/10.1068/p2994.
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In this study of the informativeness of shadows for the perception of object shape, observers viewed shadows cast by a set of natural solid objects and were required to discriminate between them. In some conditions the objects underwent rotation in depth while in other conditions they remained stationary, thus producing both deforming and static shadows. The orientation of the light source casting the shadows was also varied, leading to further alterations in the shape of the shadows. When deformations in the shadow boundary were present, the observers were able to reliably recognize and discriminate between the objects, invariant over the shadow distortions produced by movements of the light source. The recognition performance for the static shadows depended critically upon the content of the specific views that were shown. These results support the idea that there are invariant features of shadow boundaries that permit the recognition of shape (cf Koenderink, 1984 Perception13 321–330).
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Bankhele, Akanksha. "Shadow Detection and Removal Technique using CNN." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1748–55. http://dx.doi.org/10.22214/ijraset.2021.37667.
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Abstract: The Shadow detection and removal Technique is used in many real-world applications, such as surveillance systems, computer vision applications and indoor outdoor system. The shape and orientation of an object, as well as the light source, can be revealed by shadows in an image. In a traffic surveillance system, the shadow can misclassify the actual target, lowering the system’s accuracy. Numerous algorithms and techniques have been developed by researchers to aid in the detection and removal of shadows in images. This paper aims to provide an overview of different shadow detection and removal techniques, their advantages and drawbacks. Also implementation of Convolutional Neural Network for shadow detection and OpenCV features to remove shadows by re-designing the output and analysing different loss functions to train the network. Keywords: Shadow Detection and Removal Techniques, Shadow Image Processing.
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Plummer, Matthew, Douglas Stow, Emanuel Storey, Lloyd Coulter, Nicholas Zamora, and Andrew Loerch. "Reducing Shadow Effects on the Co-Registration of Aerial Image Pairs." Photogrammetric Engineering & Remote Sensing 86, no. 3 (2020): 177–86. http://dx.doi.org/10.14358/pers.86.4.177.
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Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (<small>SIFT</small>) and Random Sample Consensus (<small>RANSAC</small>) Alignment (<small>SARA</small>). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows.
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Hsieh, Yi-Ta, Kun-Yong Yu, Chaur-Tzuhn Chen, and Jan-Chang Chen. "Estimating stand attributes of complex forest types in subtropical mountain environments by combining the shadow fraction method with analyses of high-dynamic-range photographs." Canadian Journal of Forest Research 50, no. 10 (2020): 1093–99. http://dx.doi.org/10.1139/cjfr-2018-0502.
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Shadow fractions can be overestimated because of topographic shadows, which can occupy a significant area on aerial photographs of mountainous terrain. In this study, we first used high-dynamic-range (HDR) image analysis techniques to extract the original canopy shadow from the topographic shadows on aerial photographs. Subsequently, we applied the shadow fraction method to estimate selected forest attributes (stand height, basal area, and stem volume). In this paper, we discuss the effects of tree shadow fraction normalization, auxiliary spectral information, and forest type on forest attribute estimation. HDR image analysis successfully extracted canopy shadow information from topographic shadows. The tree shadow fraction normalization method had no obvious effect. The shadow fraction enhanced spectral information to estimate stand attributes. Using shadow fractions resulted in better estimates of stand height for mixed-hardwood forest ([Formula: see text] = 0.45), basal area for mixed-hardwood forest ([Formula: see text] = 0.50), and stem volume for conifer–hardwood forest ([Formula: see text] = 0.43). This difference in estimated results is related to the shade patterns produced by stand structures in the different forest types.
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Zhou, Guoqing, and Hongjun Sha. "Building Shadow Detection on Ghost Images." Remote Sensing 12, no. 4 (2020): 679. http://dx.doi.org/10.3390/rs12040679.
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Although many efforts have been made on building shadow detection from aerial images, little research on simultaneous shadows detection on both building roofs and grounds has been presented. Hence, this paper proposes a new method for simultaneous shadow detection on ghost image. In the proposed method, a corner point on shadow boundary is selected and its 3D approximate coordinate is calculated through photogrammetric collinear equation on the basis of assumption of average elevation within the aerial image. The 3D coordinates of the shadow corner point on shadow boundary is used to calculate the solar zenith angle and the solar altitude angle. The shadow areas on the ground, at the moment of aerial photograph shooting are determined by the solar zenith angle and the solar altitude angle with the prior information of the digital building model (DBM). Using the relationship between the shadows of each building and the height difference of buildings, whether there exists a shadow on the building roof is determined, and the shadow area on the building roof on the ghost image is detected on the basis of the DBM. High-resolution aerial images located in the City of Denver, Colorado, USA are used to verify the proposed method. The experimental results demonstrate that the shadows of the 120 buildings in the study area are completely detected, and the success rate is 15% higher than the traditional shadow detection method based on shadow features. Especially, when the shadows occur on the ground and on the buildings roofs, the successful rate of shadow detection can be improved by 9.42% and 33.33% respectively.
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Zhang, W. X., G. Q. Zhou, T. Yue, et al. "ORTHOPHOTO SHADOW DETECTION METHOD UNDER ARTIFICIAL SHADOW." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 199–204. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-199-2020.
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Abstract. Shadows are ubiquitous in high-resolution images, especially in urban regions where there are more serious shadow occlusions. In order to improve the detection effect of shadows, this paper analyzes the characteristics and properties of shadows in orthophotos, and proposes an orthophoto shadow detection method under artificial shadow. Firstly, the shadow modeling tool is used to calculate the shadow regions (i.e. artificial shadow) caused by the building obstructing the sun's rays. Secondly, the relaxation matching algorithm is extended by the position and the shape of the shadow polygon as characteristic constraints. The relaxation matching algorithm is extended by the position and shape as shadow polygon’s characteristic constraints. Thirdly, the feature constraint value is calculated which between the shadow polygons of the two images. The correlation coefficient is used to obtain the initial probability value of each shadow polygon in the orthophoto. Finally, the optimal solution is obtained by continuous correction and iteration of the initial probability value. The method performs an overall matching of the two images and obtains the position of the shadow regions of the buildings in the orthophoto image. Experiment shows that this method reduces the mismatch rate and improves the matching accuracy. This method can detect shadow regions of buildings in orthophoto quickly and efficiently.
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Yi, Yugen, Jiangyan Dai, Chengduan Wang, et al. "An Effective Framework Using Spatial Correlation and Extreme Learning Machine for Moving Cast Shadow Detection." Applied Sciences 9, no. 23 (2019): 5042. http://dx.doi.org/10.3390/app9235042.
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Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, we present a novel moving cast-shadow detection framework based on the extreme learning machine (ELM) to efficiently distinguish shadow points from the foreground object. First, according to the physical model of shadows, pixel-level features of different channels in different color spaces and region-level features derived from the spatial correlation of neighboring pixels are extracted from the foreground. Second, an ELM-based classification model is developed by labelled shadow and un-shadow points, which is able to rapidly distinguish the points in the new input whether they belong to shadows or not. Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on two publicly common datasets including 13 different scenes demonstrate that the performance of the proposed framework is superior to representative state-of-the-art methods.
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Setiyono, Budi, Dwi Ratna Sulistyaningrum, Soetrisno ., and Hasanuddin Al-Habib. "Improvement of sub region matching illumination transfer in hybrid shadow removal method for moving vehicle video." International Journal of Engineering & Technology 7, no. 4 (2018): 2678. http://dx.doi.org/10.14419/ijet.v7i4.13725.
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Intelligent Transportation System (ITS) is a concept to manage transportation based on technology development. Video from surveillance cameras can be used for monitoring the number of vehicles and speed using digital image processing. Shadows on the vehicle is one of the noise that must be removed in order to obtain better accuracy. Shadow is caused by the reflection of objects exposed to the light. In this study, we combined two methods to eliminate shadows on moving vehicle, the subregion illumination transfer method and the background-based Gaussian mixture model. Foreground image is used for sub-Region Illumination Transfer and gamma decoding processes is used to detect the presence of shadows The detected shadow is removed by replacing it with the background in that position. Experiments are done by making simulated video of moving objects without shadows and objects that have a shadow. By using the proposed method, the shadow will be omitted, and the results are compared with the object without the shadow. The experimental results are: mean value of PSNR for objects moving closer to the camera with a light intensity of 0.8 is 53.47. While on the moving object with a small shadow area, we obtained an average PSNR of 51.87927dB.
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Cunha, Pedro V. P., Carlos A. R. Herdeiro, Eugen Radu, and Helgi F. Rúnarsson. "Shadows of Kerr black holes with and without scalar hair." International Journal of Modern Physics D 25, no. 09 (2016): 1641021. http://dx.doi.org/10.1142/s0218271816410212.
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For an observer, the Black Hole (BH) shadow is the BHs apparent image in the sky due to the gravitational lensing of nearby radiation, emitted by an external source. A recent class of solutions dubbed Kerr BHs with scalar hair possess smaller shadows than the corresponding Kerr BHs and, under some conditions, novel exotic shadow shapes can arise. Thus, these hairy BHs could potentially provide new shadow templates for future experiments such as the Event Horizon Telescope. In order to obtain the shadows, the backward ray-tracing algorithm is briefly introduced, followed by numerical examples of shadows of Kerr BHs with scalar hair contrasting with the Kerr analogues. Additionally, an analytical solution for the Kerr shadow is derived in closed form for a ZAMO observer at an arbitrary position.
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Chen, Qi, Xing Ben Yang, and Shao Wen Yang. "Fast Shadow Detection and Removal Based on Saliency." Applied Mechanics and Materials 703 (December 2014): 286–89. http://dx.doi.org/10.4028/www.scientific.net/amm.703.286.
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In this paper, we present a new method for removing shadows from images. Different from traditional methods that explore pixel or edge information, we employ a shadow saliency region based approach. We combine the region saliency with the clustering algorithm to get shadow region with the entropy rate in an unsupervised manner. The shadow free image is recovered by relighting each pixel based on our lighting model, and any visible shadow boundaries in the image can be eliminated by the Bayesian framework. We show that our method is fast and can generate satisfactory results for images with shadows.
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Ke, Yongzhen, Fan Qin, Weidong Min, and Guiling Zhang. "Exposing Image Forgery by Detecting Consistency of Shadow." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/364501.
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We propose two tampered image detection methods based on consistency of shadow. The first method is based on texture consistency of shadow for the first kind of splicing image, in which the shadow as well as main body is copied and pasted from another image. The suspicious region including shadow and nonshadow is first selected. Then texture features of the shadow region and the nonshadow region are extracted. Last, correlation function is used to measure the similarity of the two texture features. By comparing the similarity, we can judge whether the image is tampered. Due to the failure in detecting the second kind of splicing image, in which main body, its shadow, and surrounding regions are copied and pasted from another image, another method based on strength of light source of shadows is proposed. The two suspicious shadow regions are first selected. Then an efficient method is used to estimate the strength of light source of shadow. Last, the similarity of strength of light source of two shadows is measured by correlation function. By combining the two methods, we can detect forged image with shadows. Experimental results demonstrate that the proposed methods are effective despite using simplified model compared with the existing methods.
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Yasutomi, Suguru, Tatsuya Arakaki, Ryu Matsuoka, et al. "Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows." Applied Sciences 11, no. 3 (2021): 1127. http://dx.doi.org/10.3390/app11031127.
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Acoustic shadows are common artifacts in medical ultrasound imaging. The shadows are caused by objects that reflect ultrasound such as bones, and they are shown as dark areas in ultrasound images. Detecting such shadows is crucial for assessing the quality of images. This will be a pre-processing for further image processing or recognition aiming computer-aided diagnosis. In this paper, we propose an auto-encoding structure that estimates the shadowed areas and their intensities. The model once splits an input image into an estimated shadow image and an estimated shadow-free image through its encoder and decoder. Then, it combines them to reconstruct the input. By generating plausible synthetic shadows based on relatively coarse domain-specific knowledge on ultrasound images, we can train the model using unlabeled data. If pixel-level labels of the shadows are available, we also utilize them in a semi-supervised fashion. By experiments on ultrasound images for fetal heart diagnosis, we show that our method achieved 0.720 in the DICE score and outperformed conventional image processing methods and a segmentation method based on deep neural networks. The capability of the proposed method on estimating the intensities of shadows and the shadow-free images is also indicated through the experiments.
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Li, Pengwei, and Wenying Ge. "A Soft Shadow Detection Method Based on MRF for Remote Sensing Images." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/404095.
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Shadows limit many remote sensing applications such as classification, target detection, and change detection. Most current shadow detection methods utilize the histogram threshold of spectral characteristics to distinguish the shadows and nonshadows directly, called “hard binary shadow.” Obviously, the performance of threshold-based methods heavily rely on the selected threshold. Simultaneously, these threshold-based methods do not take any spatial information into account. To overcome these shortcomings, a soft shadow description method is developed by introducing the concept of opacity into shadow detection, and MRF-based shadow detection method is proposed in order to make use of neighborhood information. Experiments on remote sensing images have shown that the proposed method can obtain more accurate detection results.
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Leigh, Sang-won, Asta Roseway, and Ann Paradiso. "Remnance of Form: Interactive Shadows as Altered Views of Objects." Leonardo 52, no. 3 (2019): 286–87. http://dx.doi.org/10.1162/leon_a_01717.
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The projective nature of light results in distorted shadows, the remnants of physical forms, no different from any human perception that is governed by subjectivity. This work seeks to widen the gap between the objective world and the perception of it by programmatically altering the association between an object and its shadow. The shadow can transform or be animated, demonstrating a range of personality and emotion. On the other hand, the visual and interaction gestalts of our installation are carefully designed to emulate physical shadows. By juxtaposing the poetic interpretation of the shadow and realistic visuals, we explore the evocation of strong personal connections with an object and its shadow.
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Murali, Saritha, and V. K. Govindan. "Shadow Detection and Removal from a Single Image Using LAB Color Space." Cybernetics and Information Technologies 13, no. 1 (2013): 95–103. http://dx.doi.org/10.2478/cait-2013-0009.
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Abstract A shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. The shadows are sometimes helpful for providing useful information about objects. However, they cause problems in computer vision applications, such as segmentation, object detection and object counting. Thus shadow detection and removal is a pre-processing task in many computer vision applications. This paper proposes a simple method to detect and remove shadows from a single RGB image. A shadow detection method is selected on the basis of the mean value of RGB image in A and B planes of LAB equivalent of the image. The shadow removal is done by multiplying the shadow region by a constant. Shadow edge correction is done to reduce the errors due to diffusion in the shadow boundary.
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ZHANG, FAN, HANQIU SUN, LEILEI XU, and KITLUN LEE. "HARDWARE-ACCELERATED PARALLEL-SPLIT SHADOW MAPS." International Journal of Image and Graphics 08, no. 02 (2008): 223–41. http://dx.doi.org/10.1142/s0219467808003064.
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Shadow mapping is well known for its generality and efficiency, thus it has been extensively employed for real-time shadow rendering in diverse applications. However, it suffers from inherent aliasing problem due to its image-based nature. In this paper, we present the parallel-split shadow maps scheme which produces high-quality shadows especially in large-scale and complex scenes. Our scheme splits the view frustum into parts using planes parallel to the view plane, and then generates a shadow map for each part. A fast and robust splitting strategy based on the analysis of shadow-map aliasing is proposed, which results in a moderate aliasing distribution over the depth range. Hardware-accelerated processing is developed to eliminate extra rendering passes which surpass that of standard shadow mapping when synthesizing scene-shadows.
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Lee, Mi Yeon, and Terri L. Kurz. "Lights, shadow, action!" Teaching Children Mathematics 24, no. 2 (2017): 136–38. http://dx.doi.org/10.5951/teacchilmath.24.2.0136.
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“More weight, longer shadow,” a student predicts before we begin measuring the lengths of our objects' shadows. Another third-grade student conjectures, “Standing-up objects make the shadow longer, and lying-down objects make the shadow shorter.”
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Ishida, Shuya, Shinji Fukui, Yuji Iwahori, M. K. Bhuyan, and Robert J. Woodham. "Construction of Shadow Model by Robust Features to Illumination Changes." International Journal of Software Innovation 1, no. 4 (2013): 45–55. http://dx.doi.org/10.4018/ijsi.2013100104.
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Methods in the field of computer vision need a shadow detection because shadows often have a harmful effect on a result. A new shadow detection method is proposed in this paper. The proposed method is based on the shadow model. The model is constructed by robust features to illumination changes. The proposed method uses the difference of chrominance (UV) components of luma chrominance (YUV) color space between the background image and the observed image, Normalized Vector Distance, Peripheral Increment Sign Correlation image and edge information. These features remove shadow effects in part. The proposed method can construct the effective shadow model by using the features. In addition, the result is improved by the region based method and the shadow model is updated. The proposed method can extract shadows accurately. Results are demonstrated by the experiments using the real videos.
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Castiello, Umberto, Dean Lusher, Carol Burton, and Peter Disler. "Shadows in the Brain." Journal of Cognitive Neuroscience 15, no. 6 (2003): 862–72. http://dx.doi.org/10.1162/089892903322370780.
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The aims of the present study were to investigate whether the processing of an object shadow occurs implicitly, that is without conscious awareness, and where physically within the human brain shadows are processed. Here we present neurological evidence, obtained from studies of brain-injured patients with visual neglect, that shadows are implicitly processed and that this processing may take place within the temporal lobe. Neglect patients with lesions that do not involve the right temporal lobe were still able to process shadows to optimize object shape perception. In contrast, shadow processing was not found to be as efficient in neglect patients with lesions that involve the right temporal lobe.
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Ibarra-Arenado, Manuel José, Tardi Tjahjadi, and Juan Pérez-Oria. "Shadow Detection in Still Road Images Using Chrominance Properties of Shadows and Spectral Power Distribution of the Illumination." Sensors 20, no. 4 (2020): 1012. http://dx.doi.org/10.3390/s20041012.
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A well-known challenge in vision-based driver assistance systems is cast shadows on the road, which makes fundamental tasks such as road and lane detections difficult. In as much as shadow detection relies on shadow features, in this paper, we propose a set of new chrominance properties of shadows based on the skylight and sunlight contributions to the road surface chromaticity. Six constraints on shadow and non-shadowed regions are derived from these properties. The chrominance properties and the associated constraints are used as shadow features in an effective shadow detection method intended to be integrated on an onboard road detection system where the identification of cast shadows on the road is a determinant stage. Onboard systems deal with still outdoor images; thus, the approach focuses on distinguishing shadow boundaries from material changes by considering two illumination sources: sky and sun. A non-shadowed road region is illuminated by both skylight and sunlight, whereas a shadowed one is illuminated by skylight only; thus, their chromaticity varies. The shadow edge detection strategy consists of the identification of image edges separating shadowed and non-shadowed road regions. The classification is achieved by verifying whether the pixel chrominance values of regions on both sides of the image edges satisfy the six constraints. Experiments on real traffic scenes demonstrated the effectiveness of our shadow detection system in detecting shadow edges on the road and material-change edges, outperforming previous shadow detection methods based on physical features, and showing the high potential of the new chrominance properties.
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Jiang, Yue, Xuehu Yan, Jianqing Qi, Yuliang Lu, and Xuan Zhou. "Secret Image Sharing with Dealer-Participatory and Non-Dealer-Participatory Mutual Shadow Authentication Capabilities." Mathematics 8, no. 2 (2020): 234. http://dx.doi.org/10.3390/math8020234.
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A ( k , n ) threshold secret image sharing (SIS) method is proposed to divide a secret image into n shadows. The beauty of this scheme is that one can only reconstruct a secret image with k or more than k shadows, but one cannot obtain any information about the secret from fewer than k shadows. In the ( k , n ) threshold SIS, shadow authentication means the detection and location of manipulated shadows. Traditional shadow authentication schemes require additional bits for authentication; need much information to be public; or need to put each shadow into a host image, utilizing the information hiding technique, which makes the generation, recovery and authentication complexity higher. Besides, most existing schemes work when a dealer participates in recovery. Our contribution is that we propose a SIS method for a ( k , n ) threshold with dealer-participatory and non-dealer-participatory mutual shadow authentication capabilities which integrates polynomial-based SIS and visual secret sharing (VSS) through using the result of VSS to “guide” the polynomial-based SIS by a screening operation. In our scheme, when an authentication image is public, all involved actors (participants and dealer) can mutually authenticate each other by exchange the lowest level plane instead of the whole shadow. Our scheme is suitable for the case with and without a dealer participate recovery. In addition, the proposed scheme has characteristics of low generation and authentication complexity, no pixel expansion, 100% detection rate and lossless recovery.
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Koizumi, Tomomi, Hiroyuki Ito, Shoji Sunaga, and Masaki Ogawa. "Directional Bias in the Perception of Cast Shadows." i-Perception 8, no. 1 (2017): 204166951668226. http://dx.doi.org/10.1177/2041669516682267.
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Previous studies have demonstrated that the perception of shading is based upon assumptions about lighting direction, for example, light from above. However, it is not clear whether these assumptions are used in the perception of cast shadows. Moreover, it is unclear whether a perceptual interaction exists between shading and cast shadows because until now they have been studied separately. In this study, we investigated through three experiments whether the light-from-above (or another direction) assumption is used in interpreting ambiguous cast shadows, and whether shading information influences the interpretation of cast shadows. Our results indicate the existence of the light-from-above assumption in interpreting cast shadows. Consistent shading information enhanced the interpretation, and judgments of lighting direction were also based on both cast shadow and shading information. However, the perceptual determination of shape from shading was relatively independent of the cast shadow interpretation or the lighting direction judgments of the scene.
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Kubec, Agata. "Uświadomiona i nieuświadomiona postać cienia organizacyjnego w komercyjnej organizacji medialnej." Zarządzanie w Kulturze 21, no. 4 (2020): 375–98. http://dx.doi.org/10.4467/20843976zk.20.029.13047.
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A Conscious and Unconscious Form of Organizational Shadow in a Commercial Media Organization The aim of the article is to explore and describe the organizational shadow in relation to the commercial media institution. The concept of shadow was introduced by Jung, who described it as the negative side of the human soul. As Bowles noted in 1991, this phenomenon can be applied to management sciences. The organizational shadow is created as a result of rejecting difficult, defective and uncomfortable elements of the institution’s identity, which can lead to the development of management pathologies. Interviews, which are the domain of qualitative methodology, allowed to distinguish organizational shadows in the commercial radio station, taking into account the causes and effects of their development as well as awareness of their existence among employees. The shadows include: unequal treatment of volunteer workers, giving up the development of young radio broadcasters, as well as a unjust remuneration policy. The conclusion of the article is indication the brand myth as the main reason for allowing the development of shadows.
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Kunzendorf, Robert G., William Butler, and Brian Pearson. "Are Monocular Arrays Discriminable from Binocular Arrays?" Perceptual and Motor Skills 66, no. 2 (1988): 531–34. http://dx.doi.org/10.2466/pms.1988.66.2.531.
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On each trial of this experiment, a subject was visually stimulated by one or two shadows on a translucent background in a Telebinocular. For each subject, there were 40 trials of monocular stimulation by one shadow, 40 trials of monocular stimulation by two shadows (one in each hemifield), and 40 trials of binocular stimulation by two shadows (one in the left hemifield of one eye and another in the right hemifield of the other eye). Across these randomly ordered trials, 52 subjects were unable to discriminate two right-eye shadows from two left-eye shadows and were unable to discriminate two monocularly perceived shadows from two binocularly perceived shadows. Generally, subjects tended to misidentify right-hemifield shadows as right-eye shadows and tended to misidentify two-hemifield shadows as two-eye shadows.
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Yagishita, D., and H. Chikatsu. "Generation of effective orthophotos for road surfaces using MMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5 (June 6, 2014): 621–28. http://dx.doi.org/10.5194/isprsarchives-xl-5-621-2014.
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In recent years, high precision and high resolution road surface orthophotos have been generated using video cameras mounted on surveying vehicles. However, there is a serious issue in generating an orthophoto from this image. The shadows of the surrounding structures and vehicles on the road surface cause a lack of information and decrease in visibility. Therefore, the shadows should be removed from the images for exact road management. On the other hand, the Mobile Mapping System with a laser scanner mounted on vehicles has been receiving more attention because the laser scanner intensity is almost unaffected by shadows. This paper presents shadow extraction and shadow correction for generating road surface orthophotos using the laser scanner intensity.
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Puerta, Antonio Medina. "The power of shadows: shadow stereopsis." Journal of the Optical Society of America A 6, no. 2 (1989): 309. http://dx.doi.org/10.1364/josaa.6.000309.
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Et. al., Mohan kumar Shilpa ,. "An Effective Framework Using Region Merging and Learning Machine for Shadow Detection and Removal." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2506–14. http://dx.doi.org/10.17762/turcomat.v12i2.2098.
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Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, the foreground is detected by background subtraction and the shadow is detected by combination of Mean-Shift and Region Merging Segmentation. Using Gabor method, we obtain the moving targets with texture features. According to the characteristics of shadow in HSV space and texture feature, the shadow is detected and removed to eliminate the shadow interference for the subsequent processing of moving targets.
 Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on publicly common datasets that the performance of the proposed framework is superior to representative state-of-the-art methods.
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Azevedo, S. C., E. A. Silva, and M. M. Pedrosa. "Shadow detection improvement using spectral indices and morphological operators in high resolution images from urban areas." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 29, 2015): 587–92. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-587-2015.
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While high-resolution remote sensing images have increased application possibilities for urban studies, the large number of shadow areas has created challenges to processing and extracting information from these images. Furthermore, shadows can reduce or omit information from the surface as well as degrading the visual quality of images. The pixels of shadows tend to have lower radiance response within the spectrum and are often confused with low reflectance targets. In this work, a shadow detection method was proposed using a morphological operator for dark pattern identification combined with spectral indices. The aims are to avoid misclassification in shadow identification through properties provided by them on color models and, therefore, to improve shadow detection accuracy. Experimental results were tested applying the panchromatic and multispectral band of WorldView-2 image from São Paulo city in Brazil, which is a complex urban environment composed by high objects like tall buildings causing large shadow areas. Black top-hat with area injunction was applied in PAN image and shadow identification performance has improved with index as Normalized Difference Vegetation Index (NDVI) and Normalized Saturation-Value Difference Index (NSDVI) ratio from HSV color space obtained from pansharpened multispectral WV-2 image. An increase in distinction between shadows and others objects was observed, which was tested for the completeness, correctness and quality measures computed, using a created manual shadow mask as reference. Therefore, this method can contribute to overcoming difficulties faced by other techniques that need shadow detection as a first necessary preprocessing step, like object recognition, image matching, 3D reconstruction, etc.
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Petikam, Lohit, Ken Anjyo, and Taehyun Rhee. "Shading Rig: Dynamic Art-directable Stylised Shading for 3D Characters." ACM Transactions on Graphics 40, no. 5 (2021): 1–14. http://dx.doi.org/10.1145/3461696.
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Despite the popularity of three-dimensional (3D) animation techniques, the style of 2D cel animation is seeing increased use in games and interactive applications. However, conventional 3D toon shading frequently requires manual editing to clean up undesired shadows or add stylistic details based on art direction. This editing is impractical for the frame-by-frame editing in cartoon feature film post-production. For interactive stylised media and games, post-production is unavailable due to real-time constraints, so art-direction must be preserved automatically. For these reasons, artists often resort to mesh and texture edits to mitigate undesired shadows typical of toon shaders. Such edits allow real-time rendering but are limited in resolution, animation quality and lack detail control for stylised shadow design. In our framework, artists build a “shading rig,” a collection of these edits, that allows artists to animate toon shading. Artists pre-animate the shading rig under changing lighting, to dynamically preserve artistic intent in a live application, without manual intervention. We show our method preserves continuous motion and shape interpolation, with fewer keyframes than previous work. Our shading shape interpolation is computationally cheaper than state-of-the-art image interpolation techniques. We achieve these improvements while preserving vector quality rendering, without resorting either to high texture resolution or mesh density.
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Kadhim, N. M. S. M., M. Mourshed, and M. T. Bray. "SHADOW DETECTION FROM VERY HIGH RESOLUTON SATELLITE IMAGE USING GRABCUT SEGMENTATION AND RATIO-BAND ALGORITHMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 95–101. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-95-2015.
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Very-High-Resolution (VHR) satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour), the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates significant performance of the ratio algorithm. The differences in the characteristics of the two satellite imageries in terms of spatial and spectral resolution can play an important role in the estimation and detection of the shadow of urban objects.
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Yang, Xiaqing, Jun Shi, Yuanyuan Zhou, et al. "Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR." Remote Sensing 12, no. 18 (2020): 3083. http://dx.doi.org/10.3390/rs12183083.
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Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.
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Li, Z., G. Cai, and H. Ren. "FULLY CONVOLUTIONAL NETWORK BASED SHADOW EXTRACTION FROM GF-2 IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 985–91. http://dx.doi.org/10.5194/isprs-archives-xlii-3-985-2018.
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There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building extraction which is hard to achieve a satisfactory accuracy through image classification itself. This paper focused on the method of building shadow extraction by designing a fully convolutional network and training samples collected from GF-2 satellite imagery in the urban region of Changchun city. By means of spatial filtering and calculation of adjacent relationship along the sunlight direction, the small patches from vegetation or bridges have been eliminated from the preliminary extracted shadows. Finally, the building shadows were separated. The extracted building shadow information from the proposed method in this paper was compared with the results from the traditional object-oriented supervised classification algorihtms. It showed that the deep learning network approach can improve the accuracy to a large extent.
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Rahman, Mir Mustafizur, Gregory J. McDermid, Taylor Mckeeman, and Julie Lovitt. "A workflow to minimize shadows in UAV-based orthomosaics." Journal of Unmanned Vehicle Systems 7, no. 2 (2019): 107–17. http://dx.doi.org/10.1139/juvs-2018-0012.
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Shadows from buildings, terrain, and other elevated features represent lost and (or) impaired data values that hinder the quality of optical images acquired under all but the most diffuse illumination conditions. This is particularly problematic in high-spatial-resolution imagery acquired from unmanned aerial vehicles (UAVs), which generally operate very close to the ground. However, the flexibility and low cost of re-deployment of the platform also presents opportunities, which we capitalize on in a new workflow designed to eliminate shadows from UAV-based orthomosaics. Our straightforward, three-step procedure relies on images acquired from two different UAV flights, where illumination conditions produce diverging shadow orientations: one before solar noon and another after. From this multi-temporal image stack, we first identify and then eliminate shadows from individual orthophoto components, then construct the final orthomosaic using a feature-matching strategy with the commercial software package Photoscan. The utility of our strategy is demonstrated over a treed-wetland study site in northwestern Alberta, Canada; a complex scene containing a wide variety of shadows, which our workflow effectively eliminated. While shadow-reduced orthomosaics are generally less useful for feature-identification tasks that rely on the shadow element of image interpretation, they create a superior foundation for most other image-processing routines, including classification and change-detection.
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Ye, S. P., C. X. Chen, A. Nedzved, and J. Jiang. "Building detection by local region features in SAR images." Computer Optics 44, no. 6 (2020): 944–50. http://dx.doi.org/10.18287/2412-6179-co-703.
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The buildings are very complex for detection on SAR images, where the basic features of those are shadows. There are many different representations for SAR shadow. As result it is no possible to use convolutional neural network for building detection directly. In this article we give property analysis of SAR shadows of different type buildings. After that, each region (ROI) prepared for training of building detection is corrected with its own SAR shadow properties. Reconstructions of ROI will be put in a modified YOLO network for building detection with better quality result.
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Casas Cambra, Llogari, Matthias Fauconneau, Maggie Kosek, Kieran Mclister, and Kenny Mitchell. "Enhanced Shadow Retargeting with Light-Source Estimation Using Flat Fresnel Lenses." Computers 8, no. 2 (2019): 29. http://dx.doi.org/10.3390/computers8020029.
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Shadow-retargeting maps depict the appearance of real shadows to virtual shadows given corresponding deformation of scene geometry, such that appearance is seamlessly maintained. By performing virtual shadow reconstruction from unoccluded real-shadow samples observed in the camera frame, this method efficiently recovers deformed shadow appearance. In this manuscript, we introduce a light-estimation approach that enables light-source detection using flat Fresnel lenses that allow this method to work without a set of pre-established conditions. We extend the adeptness of this approach by handling scenarios with multiple receiver surfaces and a non-grounded occluder with high accuracy. Results are presented on a range of objects, deformations, and illumination conditions in real-time Augmented Reality (AR) on a mobile device. We demonstrate the practical application of the method in generating otherwise laborious in-betweening frames for 3D printed stop-motion animation.
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Zhou, G., J. Sha, T. Yue, et al. "BUILDING SHADOW DETECTION FROM GHOST IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (May 2, 2018): 2527–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2527-2018.
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Shadow is one of the basic features of remote sensing image, it expresses a lot of information of the object which is loss or interference, and the removal of shadow is always a difficult problem to remote sensing image processing. In this paper, it is mainly analyzes the characteristics and properties of shadows from the ghost image (traditional orthorectification). The DBM and the interior and exterior orientation elements of the image are used to calculate the zenith angle of sun. Then this paper combines the scope of the architectural shadows which has be determined by the zenith angle of sun with the region growing method to make the detection of architectural shadow areas. This method lays a solid foundation for the shadow of the repair from the ghost image later. It will greatly improve the accuracy of shadow detection from buildings and make it more conducive to solve the problem of urban large-scale aerial imagines.
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Jiao, Libin, Lianzhi Huo, Changmiao Hu, and Ping Tang. "Refined UNet: UNet-Based Refinement Network for Cloud and Shadow Precise Segmentation." Remote Sensing 12, no. 12 (2020): 2001. http://dx.doi.org/10.3390/rs12122001.
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Formulated as a pixel-level labeling task, data-driven neural segmentation models for cloud and corresponding shadow detection have achieved a promising accomplishment in remote sensing imagery processing. The limited capability of these methods to delineate the boundaries of clouds and shadows, however, is still referred to as a central issue of precise cloud and shadow detection. In this paper, we focus on the issue of rough cloud and shadow location and fine-grained boundary refinement of clouds on the dataset of Landsat8 OLI and therefore propose the Refined UNet to achieve this goal. To this end, a data-driven UNet-based coarse prediction and a fully-connected conditional random field (Dense CRF) are concatenated to achieve precise detection. Specifically, the UNet network with adaptive weights of balancing categories is trained from scratch, which can locate the clouds and cloud shadows roughly, while correspondingly the Dense CRF is employed to refine the cloud boundaries. Eventually, Refined UNet can give cloud and shadow proposals sharper and more precisely. The experiments and results illustrate that our model can propose sharper and more precise cloud and shadow segmentation proposals than the ground truths do. Additionally, evaluations on the Landsat 8 OLI imagery dataset of Blue, Green, Red, and NIR bands illustrate that our model can be applied to feasibly segment clouds and shadows on the four-band imagery data.