Low complexity unsupervised multi-camera color calibration with application to panoramic video capturing

Helwani, Karim; Kondrad, Lukasz; Piotto, Nicola

doi:10.1109/icip.2015.7351022

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…In this case, we have more input data compared to the previous one. Indeed, if we consider the 24 patches of the Macbeth color checker, i goes from 1 to 24 in equation (10) while it goes from 1 to 24 × n f in equation (11), where n f is the number of facets used to sample each patch. Thus, for each color patch, we consider n f different RGB triplets in the case of inter-reflections (folded patches) but only 1 triplet per patch without inter-reflection (flat patches).…”

Section: Synthetic Datamentioning

confidence: 99%

“…However, these RGB coordinates do not only characterize intrinsic properties of the observed surfaces since they depend on the light illuminating the scene as well as on the camera characteristics such as the sensor responses. Indeed, the same objects under two different lights or acquired by two different cameras can be characterized by significantly different RGB coordinates [10,11,13]. Thus, the light spectral power distribution (SPD) and the camera sensor sensitivities are relevant information that can be added as input to computer vision algorithms along with the RGB data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3D color charts for camera spectral sensitivity estimation

Deeb¹,

Muselet²,

Hébert³

et al. 2017

Procedings of the British Machine Vision Conference 2017

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Estimating spectral data such as camera sensor responses or illuminant spectral power distribution from raw RGB camera outputs is crucial in many computer vision applications. Usually, 2D color charts with various patches of known spectral reflectance are used as reference for such purpose. Deducing n-D spectral data (n»3) from 3D RGB inputs is an ill-posed problem that requires a high number of inputs. Unfortunately, most of the natural color surfaces have spectral reflectances that are well described by low-dimensional linear models, i.e. each spectral reflectance can be approximated by a weighted sum of the others. It has been shown that adding patches to color charts does not help in practice, because the information they add is redundant with the information provided by the first set of patches. In this paper, we propose to use spectral data of higher dimensionality by using 3D color charts that create inter-reflections between the surfaces. These inter-reflections produce multiplications between natural spectral curves and so provide non-linear spectral curves. We show that such data provide enough information for accurate spectral data estimation.

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Section: Synthetic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%