Specularity and shadow detection for the multisource photometric reconstruction of a textured surface

Bringier, Benjamin; Bony, Alexandre; Khoudeir, Majdi

doi:10.1364/josaa.29.000011

Cited by 13 publications

(6 citation statements)

References 22 publications

(40 reference statements)

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“…They assume that light scattering can be approximated by the sum of a Lambertian and a specular component. An extension of this work for n-directions of illumination has been developed by B. Bringier et al [121]. The concept of scattering separation has the advantage to be very fast compared with other methods that use a non-Lambertian BRDF model and a mathematical optimization algorithm.…”

Section: B Shape From Shadingmentioning

confidence: 99%

Modeling, measuring, and using BRDFs: significant French contributions

et al. 2019

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The scattering of light by a surface is described by the Bidirectional Reflectance Distribution Function (BRDF). Unfortunately, this function cannot be straightforwardly acquired nor modeled. French researchers have proposed interesting contributions in the field, with several models and accurate experimental systems. For instance, the National Metrological Institute (LNE-CNAM) has implemented the best angular resolution goniospectrophotometer (0.015 o ). Modeling the BRDF has also been deeply studied in France, especially with the microfacet theory these recent years, a better understanding of the shadowing/masking function, new general distribution functions, visible normals, interfaced Lambertian microfacets and analysis concerning light multiple reflections. This paper proposes a state of the art concerning some significant French contributions in these fields.

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Section: B Shape From Shadingmentioning

confidence: 99%

Modeling, measuring, and using BRDFs: significant French contributions

et al. 2019

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“…As in these state-of-the-art methods, self-shadows will be neglected throughougt this section i.e., we abusively assume {x} + = x. To enforce robustness, we simply follow the approach advocated in [10], which systematically eliminates, in each pixel, the highest gray level, which may come from a specular highlight, as well as the two lowest ones, which may correspond to shadows. More elaborate methods for ensuring robustness will be discussed in Section 4.…”

Section: Modeling Photometric Stereo With Point Light Sourcesmentioning

confidence: 99%

“…The vectors t i (x) defined in (2.23) thus depend on the unknown depth values z(p). Using once again the change of variable z = log(z) 10 , we consider from now on each t i , i ∈ {1, . .…”

Section: Direct Depth Estimation Using Image Ratiosmentioning

confidence: 99%

LED-Based Photometric Stereo: Modeling, Calibration and Numerical Solution

Quéau

Durix

et al. 2017

J Math Imaging Vis

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We conduct a thorough study of photometric stereo under nearby point light source illumination, from modeling to numerical solution, through calibration. In the classical formulation of photometric stereo, the luminous fluxes are assumed to be directional, which is very difficult to achieve in practice. Rather, we use light-emitting diodes (LEDs) to illuminate the scene to be reconstructed. Such point light sources are very convenient to use, yet they yield a more complex photometric stereo model which is arduous to solve. We first derive in a physically sound manner this model, and show how to calibrate its parameters. Then, we discuss two state-of-the-art numerical solutions. The first

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“…It has recently become one of the most popular research issues in the fields of computer vision, face recognition, and image authenticity identification. Besides, illuminant direction estimation can provide effective help in illuminant analysis [1][2][3][4][5][6][7], which is used in many home electronic devices such as smart TV, digital camera, etc. Recent years have witnessed increasing interest in this study.…”

Section: Introductionmentioning

confidence: 99%

Illuminant direction estimation for a single image based on local region complexity analysis and average gray value

Mao

Chen

et al. 2014

Appl. Opt.

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Illuminant direction estimation is an important research issue in the field of image processing. Due to low cost for getting texture information from a single image, it is worthwhile to estimate illuminant direction by employing scenario texture information. This paper proposes a novel computation method to estimate illuminant direction on both color outdoor images and the extended Yale face database B. In our paper, the luminance component is separated from the resized YCbCr image and its edges are detected with the Canny edge detector. Then, we divide the binary edge image into 16 local regions and calculate the edge level percentage in each of them. Afterward, we use the edge level percentage to analyze the complexity of each local region included in the luminance component. Finally, according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model, we calculate the illuminant directions of the luminance component's three local regions, which meet the requirements of lower complexity and larger average gray value, and synthesize them as the final illuminant direction. Unlike previous works, the proposed method requires neither all of the information of the image nor the texture that is included in the training set. Experimental results show that the proposed method works better at the correct rate and execution time than the existing ones.

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Specularity and shadow detection for the multisource photometric reconstruction of a textured surface

Cited by 13 publications

References 22 publications

Modeling, measuring, and using BRDFs: significant French contributions

Modeling, measuring, and using BRDFs: significant French contributions

LED-Based Photometric Stereo: Modeling, Calibration and Numerical Solution

Illuminant direction estimation for a single image based on local region complexity analysis and average gray value

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