Benjamin Bringier

Photometric reconstruction of a dynamic textured surface from just one color image acquisition

Textured surface analysis is essential for many applications. We present a three-dimensional recovery approach for real textured surfaces based on photometric stereo. The aim is to be able to measure the textured surfaces with a high degree of accuracy. For this, we use a color digital sensor and principles of color photometric stereo. This method uses a single color image, instead of a sequence of gray-scale images, to recover the surface of the three dimensions. It can thus be integrated into dynamic systems where there is significant relative motion between the object and the camera. To evaluate the performance of our method, we compare it on real textured surfaces to traditional photometric stereo using three images. We thus show that it is possible to have similar results with just one color image.

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

Textured surface analysis is essential for many applications. In this paper, we present a three-dimensional (3D) recovery approach for real textured surfaces based on photometric stereo. The aim is to be able to reconstruct the textured surfaces in 3D with a high degree of accuracy. For this, the proposed method uses a sequence of six images and a Lambertian bidirectional reflectance distribution function (BRDF) to recover the surface height map. A hierarchical selection of these images is employed to eliminate the effects of shadows and highlights for all surface facets. To evaluate the performances of our method, we compare it to other traditional photometric stereo methods on real textured surfaces using six or more images.

Spatio temporal characteristics of the human color perception for digital quality assessment

Understanding the contrast sensitivity function (CSF) of the Human Visual System (HVS) has been in the focus of human psychophysics for more than 3 decades. This effort, despite certain successes, is far from closure. In this paper we present a study on the spatio-temporal characteristics of the HVS. We propose a new method of integration of both spatial and temporal effects in the CSF curve that is to be used in color image quality evaluation. The obtained results are very encouraging and show that the new CSF allows some improvements of the classical quality evaluation scheme.

Accurate image quantization adapted to multisource photometric reconstruction for rough textured surface analysis

In classical photometric stereo (PS), a Lambertian surface is illuminated from three distant light sources to recover one normal direction per pixel of the input image. In continuous noiseless cases, PS allows us to reconstruct the textured surfaces in three-dimensions with a high degree of accuracy and a high resolution. In the real world, an image is a digital quantization, a limited and noisy representation of a surface. In this paper, we present an accurate 3D recovery approach for real textured surfaces based on an acquisition PS method. The proposed method uses a sequence of images for each light source to recover an accurate and unlimited representation of a surface. To evaluate the performances of the proposed method, we compare it to other traditional PS methods on real textured surfaces.

Integration of human perception for color texture management

For more than thirty years image processing has used the numerical approach for image filtering and segmentation, and for extracting information by using attributes or features. Recent developments have proposed psycho-visual tests to measure the pattern sensitivity of the human visual system in terms of contrast and spatial frequencies. These contrast sensitivity functions make possible assessing the quality of the perceived image for a high resolution image. In this paper, we propose a novel approach to image processing, namely the use of human perception along common image processing tools towards image interpretation.

STD: Student's t-Distribution of Slopes for Microfacet Based BSDFs

This paper focuses on microfacet reflectance models, and more precisely on the definition of a new and more general distribution function, which includes both Beckmanns and GGX distributions widely used in the computer graphics community. Therefore, our model makes use of an additional parameter γ, which controls the distribution function slope and tail height. It actually corresponds to a bivariate Students t‐distribution in slopes space and it is presented with the associated analytical formulation of the geometric attenuation factor derived from Smith representation. We also provide the analytical derivations for importance sampling isotropic and anisotropic materials. As shown in the results, this new representation offers a finer control of a wide range of materials, while extending the capabilities of fitting parameters with captured data.

Rendering Rough Opaque Materials with Interfaced Lambertian Microfacets

Specular microfacet distributions have been successfully employed by many authors for representing glossiness of materials. They are generally combined with a Lambertian term to account for the colored aspect. These representations make use of the Fresnel reflectance factor at the interface, but the transmission factor at the interface should also be managed. One solution is to employ a multi-layered model with a single layer for the rough interface, which requires a numerical simulation for handling the multiple reflections of light between the substrate and the interface. In this paper, we propose rather to use a representation corresponding to a Fresnel interface lying on a Lambertian substrate, for which the multiple reflections of light between the interface and the substrate can be expressed analytically. With this interfaced Lambertian model, we show how Fresnel transmission affects the material appearance for flat and rough surfaces with isotropic and anisotropic distributions, that produce light backscattering effects. We also propose a methodology for using such materials in any physically based Monte Carlo rendering system, as well as an approximate representation, suitable for GPU applications or measured data fitting. Our approach generalizes several previous models, including flat Lambertian materials as well as specular and Lambertian microfacets. Our results illustrate the wide range of materials that can be rendered with this representation.

Appearance of Interfaced Lambertian Microfacets, using STD Distribution

This paper presents the use of Students T-Distribution (STD) with interfaced Lambertian (IL) microfacets. The resulting model increases the range of materials while providing a very accurate adjustment of appearance. STD has been recently proposed as a generalized distribution of microfacets which includes Beckmann and GGX widely used in computer graphics; IL corresponds to a physical representation of a Lambertian substrate covered with a flat Fresnel interface. We illustrate the appearance variations that can be observed, and discuss the advantages of using such a combination.

Print spectral reflectance estimation using trichromatic camera

This paper deals with print quality control through a spectral color measurement. The aim is to estimate the spectral reflectance curve of each pixel of a printed sheet for a spectral matching with the reference image. The proposed method consists to perform a spectral characterization of the complete chain which includes the printing system and a digital trichromatic camera. First, the spectral printer model is presented and verified by experiments. Then, the camera spectral sensitivity curves are estimated through the capture of a color chart whose spectral reflectance curves have been previously measured. Finally, the spectral printer model is used to estimate the print spectral reflectance curves from camera responses.

Evidence theory for high dynamic range reconstruction with linear digital cameras

With the advent of the digital camera, a common popular imaging processing is high dynamic range (HDR) that aims to overcome the technological limitations of the irradiance sensor dynamic range. In this paper, we will present a new method to combine low dynamic range images (LDR) for HDR processing. This method is based on the theory of evidence. Without a prior knowledge of the sensor intrinsic parameters and no extra data, it allows to locally maximizing the signal to noise ratio over the entire acquisition dynamic. In addition, our method is less sensitive to object or people in motion into the scene that are causing ghost-like artifacts with the conventional methods. This technique require that the camera be absolutely still between exposures or need a translational alignment. Simulation and experimental results are presented to demonstrate both the accuracy and efficiency of our algorithm.