Results 11 to 20 of about 49,732,844 (278)
Cloth Representation by Shape from Shading with Shading Primitives [PDF]
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005 Cloth is a complex visual pattern with flexible 3D shape and illumination variations. Computing the 3D shape of cloth from a single image is of great interest to both computer graphics and vision researches. However, the acquisition of 3D cloth shape by shape from shading (SFS) is still a challenge.
Han, Feng, Zhu, Song-Chun
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Shape from Gradients. A psychophysical and computational study of the role complex illumination gradients, such as shading and mutual illumination, play in three-dimensional shape perception. [PDF]
, 2013 The human visual system gathers information about three-dimensional object shape from a wide range of sources. How effectively we can use these sources, and how they are combined to form a consistent and accurate percept of the 3D world is the focus of ...
Harding, Glen
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Robust Shape from Shading [PDF]
Procedings of the British Machine Vision Conference 1993, 1993 Existing shape from shading algorithms are not robust enough to work reliably with real images. We present a new paradigm for shape from shading based upon a scale space surface representation. Using this representation, the shape from shading problem becomes one of forming a surface from Gaussian basis functions in such a way that the shading in the ...
Jones, A. G. +1 more
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Luminance contrast provides metric depth information
Royal Society Open Science, 2023 The perception of depth from retinal images depends on information from multiple visual cues. One potential depth cue is the statistical relationship between luminance and distance; darker points in a local region of an image tend to be farther away than
Paul B. Hibbard +4 more
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A Linearization–Based Hybrid Approach for 3D Reconstruction of Objects in a Single Image
International Journal of Applied Mathematics and Computer Science, 2021 The shape-from-shading (SFS) technique uses the pattern of shading in images in order to obtain 3D view information. By virtue of their ease of implementation, linearization-based SFS algorithms are frequently used in the literature.
Kotan Muhammed +2 more
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Shape from Shading Through Shape Evolution [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 In this paper, we address the shape-from-shading problem by training deep networks with synthetic images. Unlike conventional approaches that combine deep learning and synthetic imagery, we propose an approach that does not need any external shape dataset to render synthetic images.
Dawei Yang, Jia Deng 0001
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Reconstruction of High-Frequency Lunar Digital Elevation Model using Shape from Shading [PDF]
INCAS Bulletin, 2018 This paper deals with a procedure to reconstruct a lunar surface based on fusion of Shape from Shading with absolute depth information exploited from Lunar Orbiter Laser Altimeter data.
Min-Hyun CHO, Min-Jea TAHK
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IEEE Access, 2022 Computer vision and deep learning approaches have an important role in industrial inspection systems. Computer vision technology is essential for fast, defect-free control of products in the production line.
Feyza Selamet +2 more
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STOCKPILE MONITORING USING LINEAR SHAPE-FROM-SHADING ON PLANETSCOPE IMAGERY [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 The storage and management of stockpiles of materials is a fundamental process in large scale activities such as mining, civil engineering, and in the management of waste landfill sites.
M. d’Autume +4 more
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On the perception of shape from shading [PDF]
Perception & Psychophysics, 1992 The extraction of three-dimensional shape from shading is one of the most perceptually compelling, yet poorly understood, aspects of visual perception. In this paper, we report several new experiments on the manner in which the perception of shape from shading interacts with other visual processes such as perceptual grouping, preattentive search ("pop ...
D A, Kleffner, V S, Ramachandran
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