Towards accurate and efficient representation of image irradiance of convex-Lambertian objects under unknown near lighting

Elhabian, Shireen; Rara, Ham; Farag, Aly A.

doi:10.1109/iccv.2011.6126437

Cited by 10 publications

(15 citation statements)

References 14 publications

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“…This basis has been applied in surface modelling under distant illumination [35] and in shape description and reconstruction of surfaces [36]. Recent progress on HSH includes a HSH-based Helmholtz bidirectional reflectance basis [37] and noise-resistant Eigen hemispherical harmonics. In this study, we incorporate the HSH basis as proposed in [34] into the framework of PTM and compare its performance with the polynomial basis.…”

Section: Related Workmentioning

confidence: 99%

Efficient robust image interpolation and surface properties using polynomial texture mapping

Zhang

Drew

2014

J Image Video Proc

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Polynomial texture mapping (PTM) uses simple polynomial regression to interpolate and re-light image sets taken from a fixed camera but under different illumination directions. PTM is an extension of the classical photometric stereo (PST), replacing the simple Lambertian model employed by the latter with a polynomial one. The advantage and hence wide use of PTM is that it provides some effectiveness in interpolating appearance including more complex phenomena such as interreflections, specularities and shadowing. In addition, PTM provides estimates of surface properties, i.e., chromaticity, albedo and surface normals. The most accurate model to date utilizes multivariate Least Median of Squares (LMS) robust regression to generate a basic matte model, followed by radial basis function (RBF) interpolation to give accurate interpolants of appearance. However, robust multivariate modelling is slow. Here we show that the robust regression can find acceptably accurate inlier sets using a much less burdensome 1D LMS robust regression (or 'mode-finder'). We also show that one can produce good quality appearance interpolants, plus accurate surface properties using PTM before the additional RBF stage, provided one increases the dimensionality beyond 6D and still uses robust regression. Moreover, we model luminance and chromaticity separately, with dimensions 16 and 9 respectively. It is this separation of colour channels that allows us to maintain a relatively low dimensionality for the modelling. Another observation we show here is that in contrast to current thinking, using the original idea of polynomial terms in the lighting direction outperforms the use of hemispherical harmonics (HSH) for matte appearance modelling. For the RBF stage, we use Tikhonov regularization, which makes a substantial difference in performance. The radial functions used here are Gaussians; however, to date the Gaussian dispersion width and the value of the Tikhonov parameter have been fixed. Here we show that one can extend a theorem from graphics that generates a very fast error measure for an otherwise difficult leave-one-out error analysis. Using our extension of the theorem, we can optimize on both the Gaussian width and the Tikhonov parameter.

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Section: Related Workmentioning

confidence: 99%

Efficient robust image interpolation and surface properties using polynomial texture mapping

Zhang

Drew

2014

J Image Video Proc

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“…Considering the robotic arm only has 3 degrees of freedom and it cannot keep the sNSL always towards the scene center, so we only conduct ALR with robotic arm (ALR R) for S11-13. We use PTM [4], HSH [5], LDR [25] as the baselines. PTM [4] and HSH [5] are two image-based relighting methods.…”

Section: Quantitative Comparisonmentioning

confidence: 99%

“…We use PTM [4], HSH [5], LDR [25] as the baselines. PTM [4] and HSH [5] are two image-based relighting methods. For PTM and HSH, we use a light probe to calibrate the lighting direction for each captured image.…”

Section: Quantitative Comparisonmentioning

confidence: 99%

“…L IGHTING recurrence (LR) plays an important role in many computer vision applications, such as accurate surface and material acquisition [1][2][3], cultural heritage imaging [4,5] and scene or image change surveillance [6,7]. Despite the variances and diversity of previous successful methods on this topic, including relighting [8,9], reflectance transformation imaging (RTI) [5,10] and photometric stereo (PS) [11,12], most of them focus on synthetic (or virtual) lighting recurrence (SLR), which passively take multi-illumination images as input to virtually synthesize the target lighting condition.…”

Section: Introductionmentioning

confidence: 99%

“…1 Both of them significantly harm the FGCD accuracy. Besides, the relighting method [5] needs at least 30 minutes (including the time of images capturing, lighting calibration, lighting calculation and rerendering) for once lighting recurrence, which clearly cannot satisfy the massive-volume and multi-observations monitoring requirements.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Active Recurrence of Lighting Condition for Fine-Grained Change Detection

Zhang

Feng

Wan

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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This paper studies a new problem, namely active lighting recurrence (ALR) that physically relocalizes a light source to reproduce the lighting condition from single reference image for a same scene, which may suffer from fine-grained changes during twice observations. ALR is of great importance for fine-grained visual inspection and change detection, because some phenomena or minute changes can only be clearly observed under particular lighting conditions. Therefore, effective ALR should be able to online navigate a light source toward the target pose, which is challenging due to the complexity and diversity of real-world lighting and imaging processes. To this end, we propose to use the simple parallel lighting as an analogy model and based on Lambertian law to compose an instant navigation ball for this purpose. We theoretically prove the feasibility, i.e., equivalence and convergence, of this ALR approach for realistic near point light source and small near surface light source. Besides, we also theoretically prove the invariance of our ALR approach to the ambiguity of normal and lighting decomposition. The effectiveness and superiority of the proposed approach have been verified by both extensive quantitative experiments and challenging real-world tasks on fine-grained change detection of cultural heritages. We also validate the generality of our approach to non-Lambertian scenes.

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