Object surface recovery using a multi-light photometric stereo technique for non-Lambertian surfaces subject to shadows and specularities

Sun, Jiuai; Smith, Melvyn L.; Smith, Lyndon; Midha, Sagar; Bamber, Jeffrey C.

doi:10.1016/j.imavis.2006.04.025

Cited by 74 publications

(58 citation statements)

References 12 publications

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“…In [14] it was shown that the minimum number of lights for PST for the complete visible surface of any convex object is six. Unlike [10], they argued that simply discarding highest or lowest intensity pixels may lead to information loss so they discard only pixels with doubtful intensities.…”

Section: Related Workmentioning

confidence: 99%

Robust estimation of surface properties and interpolation of shadow/specularity components

Drew

Hel-Or

Malzbender

et al. 2012

Image and Vision Computing

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The Polynomial Texture Map framework (PTM) extends the simple model of image formation from the Lambertian variant of Photometric Stereo (PST) to more general reflectances and to more complex-shaped surfaces. It forms an alternative method for apprehending object colour, albedo, and surface normals. Here we consider solving such a model in a robust version, not to date attempted for PTM, with the upshot that both shadows and specularities are identified automatically without the need for any thresholds. Instead of the linear model used in few-source PST for Lambertian surfaces, PTM adopts a higher degree polynomial model. PTM has two aims: interpolation of images for new lighting directions, and recovery of surface properties. Here we show that a robust approach is a good deal more accurate in recovering surface properties. For new-lighting interpolation, we demonstrate that a simple radial basis function interpolation can accurately interpolate specularities as well as attached and cast shadows even with a medium-sized image set, with no need for reflectance sharing across pixels or extremely large numbers of interpolation coefficients.

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Section: Related Workmentioning

confidence: 99%

Robust estimation of surface properties and interpolation of shadow/specularity components

Drew

Hel-Or

Malzbender

et al. 2012

Image and Vision Computing

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“…This information allows the procedure to ignore unreliable data and to avoid integrating across cliffs. The weight map can be obtained in many ways, either from external information or by error detection algorithms applied to the slope data [2,3,4,5,16,19,23]. Most of these weight acquisition techniques can be used with our integrator as well.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Depth from Slope with Weights

Saracchini

Stolfi

Leitão³

et al. 2010

Procedings of the British Machine Vision Conference 2010

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We describe a robust method to recover the depth coordinate from a normal or slope map of a scene, obtained e.g. through photometric stereo or interferometry. The key feature of our method is the fast solution of the Poisson-like integration equations by a multi-scale iterative technique. The method accepts a weight map that can be used to exclude regions where the slope information is missing or untrusted, and to allow the integration of height maps with linear discontinuities (such as along object silhouettes) which are not recorded in the slope maps. Except for pathological cases, the memory and time costs of our method are typically proportional to the number of pixels N. Tests show that our method is as accurate as the best weighted slope integrators, but substantially more efficient in time and space.

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“…The pressure on the foot skin is fundamental for tactile imaging, but may cause the transmission of pathological organisms in our application. PSI, on the other hand, is a modest and noncontact technology, which achieves the 3D surface reconstruction based on a set of images taken from one fixed viewpoint under three or more different illumination conditions [78][79][80][81][82]. The schematic of a PSI system is illustrated in Fig.…”

Section: D Surface Reconstructionmentioning

confidence: 99%

“…Our method for PSI reconstruction combined the one-specularity-twoshadow assumption [81] and the simple decision criterion proposed by Reshmeier et al [121]. In the pixel-wise PSI reconstruction, the K (K=8) grey-level image intensities were ranked in descending order.…”

Section: Surface Reconstructionmentioning

confidence: 99%

“…In the pixel-wise PSI reconstruction, the K (K=8) grey-level image intensities were ranked in descending order. The assumption that at most one specular reflection and two shadows are present among the multiple images has been proven to work well with most objects [81]. In view of this, the highest and the last two lowest image intensities, together with their corresponding illumination sources, were always discarded.…”

Section: Surface Reconstructionmentioning

confidence: 99%

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An intelligent telemedicine system for detection of diabetic foot complications

Liu¹

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Object surface recovery using a multi-light photometric stereo technique for non-Lambertian surfaces subject to shadows and specularities

Cited by 74 publications

References 12 publications

Robust estimation of surface properties and interpolation of shadow/specularity components

Robust estimation of surface properties and interpolation of shadow/specularity components

Multi-Scale Depth from Slope with Weights

An intelligent telemedicine system for detection of diabetic foot complications

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