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

Bringier, Benjamin; Helbert, David; Khoudeir, Majdi

doi:10.1364/josaa.25.000566

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…In color photometric stereo, [8][9][10][11][12] various color lights are simultaneously emitted from different directions. The surface reflects those lights and a color camera can capture images with a different channel.…”

Section: Color Photometric Stereomentioning

confidence: 99%

Development of an inspection system for planar steel surface using multispectral photometric stereo

2013

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We address the methodology and hardware design for detecting defects on a steel surface. The goal of this work is to develop a surface inspection system for defects with three-dimensional (3-D) characteristics on planar steel surface using multispectral photometric stereo technique. First, the reflections of multispectral illuminations from a steel surface are captured by a multi-channel camera system. Then, a 3-D reconstructed surface profile is extracted from estimated gradient fields. The developed system is applied to real steel specimens and the experimental results show that it can reliably detect tiny defects on the surface. © 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) Fig. 3 Side view of the reconstructed surfaces using various algorithms: (a) top view of the surface defect, (b) the presented method, (c) Frankot-Chellappa, 14 (d) shapelet, 15 (e) affine transformation, 16 and (f) least-squares. 17 Optical Engineering 039701-3 March 2013/Vol. 52(3) Kang, Jang, and Won: Development of an inspection system for planar steel surface. . . Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/16/2015 Terms of Use: http://spiedl.org/terms Fig. 7 Reconstruction result of the steel surface. Optical Engineering 039701-6 March 2013/Vol. 52(3) Kang, Jang, and Won: Development of an inspection system for planar steel surface. . . Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/16/2015 Terms of Use: http://spiedl.org/terms

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Section: Color Photometric Stereomentioning

confidence: 99%

Development of an inspection system for planar steel surface using multispectral photometric stereo

2013

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“…As such, the relative movement between the imaging system and the object needs to be precisely controlled. There are other applications of DPS where single coloured images are utilised instead of multiple graylevel images [10].…”

Section: Introductionmentioning

confidence: 99%

In-process surface profile assessment of rotary machined timber using a dynamic photometric stereo technique

Ogun

Jackson

Parkin

2012

Proceedings of the Institution of Mechanical Engineers, Part I:

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Machining operations have advanced in speed and there is an increasing demand for higher quality surface finish. It is therefore necessary to develop real-time surface inspection techniques which will provide sensory information for controlling the machining processes. This paper describes a practical method for real-time analysis of planed wood using the photometric stereo technique. Earlier research has shown that the technique is very effective in assessing surface waviness on static wood samples. In this paper, the photometric stereo method is extended to real industrial applications where samples are subjected to rapid movements. Surface profiles extracted from the dynamic photometric stereo method are compared with those from the static measurements and the results show that there is a high correlation between the two methods.

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“…In recent years different photometric stereo techniques have been proposed to recover the normal vectors to the surface and albedo from digital images. [3][4][5][6][7][8][9][10][11][12][13][14] A common strategy of all these techniques is to estimate surface information either by measuring distances directly or by measuring parameters calculated from images of the illuminated objects. 7,15 Both approaches usually assume that objects exhibit Lambertian behavior in the underlying reflectance phenomenon, i.e., just before the acquisition process, all surfaces show the same radiance regardless of the illumination geometry.…”

Section: Introductionmentioning

confidence: 99%

“…An efficient way of exploiting this irrelevance is to use a conventional photometric stereo method relying on a single color image of a Lambertian surface under complex lighting conditions rather than three gray-scale images. 5 One interesting approach to avoid the use of multiple images is the one recently proposed by Bringier et al 13 They obtained a surface-height map from an image using just one red-green-blue (RGB) color-image acquisition. Each colorimage component (red, green, and blue) replaces gray images in the photometric approach.…”

Section: Introductionmentioning

confidence: 99%

Photometric-based recovery of illuminant-free color images using a red-green-blue digital camera

et al. 2012

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Albedo estimation has traditionally been used to make computational simulations of real objects under different conditions, but as yet no device is capable of measuring albedo directly. The aim of this work is to introduce a photometric-based color imaging framework that can estimate albedo and can reproduce the appearance both indoors and outdoors of images under different lights and illumination geometry. Using a calibration sample set composed of chips made of the same material but different colors and textures, we compare two photometric-stereo techniques, one of them avoiding the effect of shadows and highlights in the image and the other ignoring this constraint. We combined a photometric-stereo technique and a color-estimation algorithm that directly relates the camera sensor outputs with the albedo values. The proposed method can produce illuminant-free images with good color accuracy when a three-channel red-green-blue (RGB) digital camera is used, even outdoors under solar illumination.1 Introduction Our visual system allows us to distinguish elements such as color, texture, and surface shape, and our brains use this information to recognize objects under different lighting and observation conditions. Computational vision uses imaging devices to simulate the way the human visual system analyzes color images. Reproducing color appearance in color-based image displays depends on several factors such as the imaging device itself, the geometry of the lighting conditions, and the surface properties of the objects in question. When dealing with textured objects these factors become more evident; for example, the position of the camera and the object must be kept fixed because the color appearance of the object can change with the direction of illumination, which can cause a problem for object characterization. Thus, extracting information from textured images independently of lighting and imaging geometries poses a challenge for many computational vision systems. The use of the albedo rather than raw sensor response data can help to solve this issue. Albedo is usually defined as the ratio of scattered flux to that scattered and absorbed by a monodisperse or relatively uniform suspension, i.e., the ratio of flux scattered to the quantity of incident flux. 1 As opposed to intensity, albedo does not depend on the illumination conditions, and has been used in computer graphics to derive illuminantinvariant properties. 2 But the light reflected from a surface depends on the surface's spectral characteristics, the illumination geometry, the spectral content of the incident light, the viewing direction, and the roughness of the surface. This means that albedo recovery should not only be insensitive to the intensity of illumination but also to spectral changes in the reflected light.

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

Cited by 13 publications

References 25 publications

Development of an inspection system for planar steel surface using multispectral photometric stereo

Development of an inspection system for planar steel surface using multispectral photometric stereo

In-process surface profile assessment of rotary machined timber using a dynamic photometric stereo technique

Photometric-based recovery of illuminant-free color images using a red-green-blue digital camera

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