Results 261 to 270 of about 33,305 (299)
Detecting shadow lobbying [PDF]
Social Network Analysis and Mining, 2022 Lobbying activity is subject to strict disclosure requirements in the USA. Failure to comply with these requirements can lead to criminal and civil penalties. It is claimed that these tight lobbying disclosure measures resulted in an increase in ‘underground lobbying’.
Ivan Slobozhan +2 more
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Shadow detection: A survey and comparative evaluation of recent methods [PDF]
Pattern Recognition, 2012 This paper presents a survey and a comparative evaluation of recent techniques for moving cast shadow detection. We identify shadow removal as a critical step for improving object detection and tracking.
Andres Sanin +2 more
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New spectrum ratio properties and features for shadow detection [PDF]
Pattern Recognition, 2016 Successfully detecting shadows in still images is challenging yet has wide applications. Shadow properties and features are very important for shadow detection and processing.
Jiandong Tian +2 more
exaly +4 more sources
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Shadow Detection via Predicting the Confidence Maps of Shadow Detection Methods
Proceedings of the 29th ACM International Conference on Multimedia, 2021Today's mainstream shadow detection methods are manually designed via a case-by-case approach. Accordingly, these methods may only be able to detect shadows for specific scenes. Given the complex and diverse shadow scenes in reality, none of the existing methods can provide a one-size-fits-all solution with satisfactory performance.
Jingwei Liao +5 more
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Robust Shadow Detection by Exploring Effective Shadow Contexts
Proceedings of the 29th ACM International Conference on Multimedia, 2021Effective contexts for separating shadows from non-shadow objects can appear in different scales due to different object sizes. This paper introduces a new module, Effective-Context Augmentation (ECA), to utilize these contexts for robust shadow detection with deep structures.
Xianyong Fang +3 more
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Exploiting Residual and Illumination with GANs for Shadow Detection and Shadow Removal
ACM Transactions on Multimedia Computing, Communications, and Applications, 2023Residual image and illumination estimation have been proven to be helpful for image enhancement. In this article, we propose a general framework, called RI-GAN, that exploits residual and illumination using generative adversarial networks (GANs). The proposed framework detects and removes shadows in a coarse-to-fine fashion.
Ling Zhang +3 more
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Kinect Shadow Detection and Classification
2013 IEEE International Conference on Computer Vision Workshops, 2013Kinect depth maps often contain missing data, or "holes", for various reasons. Most existing Kinect-related research treat these holes as artifacts and try to minimize them as much as possible. In this paper, we advocate a totally different idea - turning Kinect holes into useful information. In particular, we are interested in the unique type of holes
Teng Deng +4 more
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Detection of vehicles in shadow areas
2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011This paper presents a new method to automatically detect occluded vehicle in semi or deep shadow areas using combined very high resolution (VHR) 3D LIDAR and hyperspectral data. The proposed shape/spectral integration (SSI) decision fusion algorithm was shown to outperform the spectral based anomaly algorithm mainly in deep shadow areas.
Michal Shimoni +3 more
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Distraction-Aware Shadow Detection
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019Shadow detection is an important and challenging task for scene understanding. Despite promising results from recent deep learning based methods. Existing works still struggle with ambiguous cases where the visual appearances of shadow and non-shadow regions are similar (referred to as distraction in our context).
Quanlong Zheng +3 more
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Detecting shadows from a single image
Optics Letters, 2011We present a novel (to our best knowledge) optical recognition technique for detecting shadows from a single image. Most prior approaches definitely depend on explicit physical computational models, but physics-based approaches have the critical problem that they may fail severely even with slight perturbations.
Jung, C Jung, Chanho +2 more
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