A probabilistic framework for specular shape-from-shading

Ragheb, Hossein; Hancock, Edwin R.

doi:10.1109/icpr.2002.1047989

Cited by 24 publications

(35 citation statements)

References 37 publications

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“…In principle, we can overcome both of these problems. In a recent paper, we have described a probabilistic method for specularity removal which uses the Torrance-Sparrow model to perform Lambertian reflectance correction for shiny objects [31]. Local albedo changes can be accommodated using brightness normalization or histogram equalization.…”

Section: B Real World Datamentioning

confidence: 99%

A Graph-Spectral Approach to Shape-From-Shading

Robles-Kelly

Hancock

2004

IEEE Trans. on Image Process.

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Abstract-In this paper, we explore how graph-spectral methods can be used to develop a new shape-from-shading algorithm. We characterize the field of surface normals using a weight matrix whose elements are computed from the sectional curvature between different image locations and penalize large changes in surface normal direction. Modeling the blocks of the weight matrix as distinct surface patches, we use a graph seriation method to find a surface integration path that maximizes the sum of curvature-dependent weights and that can be used for the purposes of height reconstruction. To smooth the reconstructed surface, we fit quadrics to the height data for each patch. The smoothed surface normal directions are updated ensuring compliance with Lambert's law. The processes of height recovery and surface normal adjustment are interleaved and iterated until a stable surface is obtained. We provide results on synthetic and real-world imagery.

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Section: B Real World Datamentioning

confidence: 99%

A Graph-Spectral Approach to Shape-From-Shading

Robles-Kelly

Hancock

2004

IEEE Trans. on Image Process.

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“…There are a variety of angular distribution functions discussed in the computer graphics literature [25,26], which attempt to characterize the distribution of the angle between the direction of the a posteriori mean surface (i.e., O or O ⊥ ) and the predicted direction of the local reflecting surface defined as the bisector of the wave-source direction and the viewing direction (i.e., O R ). The Beckmann distribution for rough surfaces has been discussed in [25,26], which is given by…”

Section: Scatterer Distributionmentioning

confidence: 99%

Geometry-Based Statistical Model for Radio Propagation in Rectangular Office Buildings

Chen¹,

Zhang²,

Hu³

et al. 2009

PIER B

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Abstract-We present a new approach to the modeling of angle and time of arrival statistics for radio propagation in typical office buildings, in which the majority of interior scattering objects are either parallel or perpendicular to the exterior walls. We first describe the reradiating elements in office buildings as randomly distributed arrays of thin strips. The amount of clutter and the amount of transmission/reflection loss are then accounted for through several key parameters of the site-specific features of indoor environment, such as the layout and materials of the building under consideration. Subsequently, the important channel parameters including power azimuthal spectrum (PAS) and power delay spectrum (PDS) are derived. An appealing observation is that when the path angles from multiple channel trials are measured and collectively analyzed, deterministic angle clustering becomes evident. This phenomenon agrees well with the existing ray-tracing (RT) results reported by Jo et al. in buildings of this type and cannot be explained by other geometric channel models (GCMs). Furthermore, the proposed model predicts an asymmetric cluster PAS for a single-channel-trial scenario, which yields an excellent fit to the experimental data presented by Poon and Ho. Finally, we have also investigated the behaviors of the superimposed PAS and PDS under various channel conditions.

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“…For instance Brelstaff and Blake [6] used a simple thresholding strategy to identify specularities on moving curved objects. Other lines of research remove specularities by either using additional hardware [7], imposing constrains on the input images [8], requiring color segmentation [9] as postprocessing steps, or using reflectance models to account for the distribution of image brightness [10]. The main limitation of these methods is that they either rely on pre-determined setups for the image acquisition or the use of the closed form of the BRDF to characterise the specular spike and lobe.…”

Section: Introductionmentioning

confidence: 99%

Specularity Removal from Imaging Spectroscopy Data via Entropy Minimisation

Robles-Kelly

2011

2011 International Conference on Digital Image Computing: Techniques and Applications

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Abstract-In this paper, we present a method to remove specularities from imaging spectroscopy data. We do this by making use of the dichromatic model so as to cast the problem in a linear regression setting. We do this so as to employ the average radiance for each pixel as a means to map the spectra onto a two-dimensional space. This permits the use of an entropy minimisation approach so as to recover the slope of a line described by a linear regressor. We show how this slope can be used to recover the specular coefficient in the dichromatic model and provide experiments on real-world imaging spectroscopy data. We also provide comparison with an alternative and effect a quantitative analysis that shows our method is robust to changes the degree of specularity of the image or the location of the light source in the scene.

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A probabilistic framework for specular shape-from-shading

Cited by 24 publications

References 37 publications

A Graph-Spectral Approach to Shape-From-Shading

A Graph-Spectral Approach to Shape-From-Shading

Geometry-Based Statistical Model for Radio Propagation in Rectangular Office Buildings

Specularity Removal from Imaging Spectroscopy Data via Entropy Minimisation

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