Addressing Radiometric Nonidealities: A Unified Framework

Литвинов, А. А.; Schechner, Yoav Y.

doi:10.1109/cvpr.2005.64

Cited by 48 publications

(26 citation statements)

References 30 publications

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“…One can address this precisely if the cause(s) of the deviation of the input image from the main assumptions typically put forth in the SFS algorithms is(are) known a priori. For example, if this deviation is attributed to an intensity non-linearity, such as γ-correction, the transformation can be explicitly defined as [7] …”

Section: Shading Correction For Better Sfsmentioning

confidence: 99%

“…The constant is a normalization factor which is determined by the value of γ. Interestingly, it was shown [7] that the same simple parametric form of γ-correction in (1) can also account for a rather complicated camera response function disturbing the common SFS assumptions, regardless of the camera's temporal gain and spatial non-uniformities. Similarly, an explicit form of F can be devised if this deviation is due to vignetting [7]. However it is more often that the cause of this deviation is unknown, or no explicit form can be envisioned for it (e.g., non-Lambertian surfaces).…”

Section: Shading Correction For Better Sfsmentioning

confidence: 99%

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Better Shading for Better Shape Recovery

El-Melegy

Abdelrahim

Farag

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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The basic idea of shape from shading is to infer the shape of a surface from its shading information in a single image. Since this problem is ill-posed, a number of simplifying assumptions have been often used. However they rarely hold in practice. This paper presents a simple shading-correction algorithm that transforms the image to a new image that better satisfies the assumptions typically needed by existing algorithms, thus improving the accuracy of shape recovery. The algorithm takes advantage of some local shading measures that have been driven under these assumptions. The method is successfully evaluated on real data of human teeth with ground-truth 3D shapes.

show abstract

Section: Shading Correction For Better Sfsmentioning

confidence: 99%

Section: Shading Correction For Better Sfsmentioning

confidence: 99%

Better Shading for Better Shape Recovery

El-Melegy

Abdelrahim

Farag

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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show abstract

“…Stumpfel et al [27] capture many images of a known illuminant at different locations in the image and fit a polynomial to the measured irradiances. The same principle has been used to estimate vignetting from overlapping images of an arbitrary scene [28][29][30] using measured irradiances of the same scene point at different image locations. All these methods require the user to acquire new images under controlled conditions.…”

Section: Related Workmentioning

confidence: 99%

“…All these methods require the user to acquire new images under controlled conditions. Some of the above approaches [28,29] can be used to simultaneously estimate the vignetting and the response function of a camera, but there are typically ambiguities in recovering this information. Since we recover both properties independently, we do not have any ambiguities.…”

Section: Related Workmentioning

confidence: 99%

Priors for Large Photo Collections and What They Reveal about Cameras

Kuthirummal

Agarwala

Goldman

et al. 2008

Lecture Notes in Computer Science

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Abstract.A large photo collection downloaded from the internet spans a wide range of scenes, cameras, and photographers. In this paper we introduce several novel priors for statistics of such large photo collections that are independent of these factors. We then propose that properties of these factors can be recovered by examining the deviation between these statistical priors and the statistics of a slice of the overall photo collection that holds one factor constant. Specifically, we recover the radiometric properties of a particular camera model by collecting numerous images captured by it, and examining the deviation of this collection's statistics from that of a broader photo collection whose camera-specific effects have been removed. We show that using this approach we can recover both a camera model's non-linear response function and the spatially-varying vignetting of the camera's different lens settings. All this is achieved using publicly available photographs, without requiring images captured under controlled conditions or physical access to the cameras. We also apply this concept to identify bad pixels on the detectors of specific camera instances. We conclude with a discussion of future applications of this general approach to other common computer vision problems.

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“…Usually, the LUT method is more commonly used than the function model. A. Litvinov et al [5] correct vignetting and nonlinear response function problems at the same time. They derive a combination model of vignetting and nonlinear response functions.…”

Section: Introductionmentioning

confidence: 99%

Correction of Photometric Distortion of a Micro Camera-Projector System for Structured Light 3D Scanning

Park¹,

Park²

2012

Journal of Sensor Science and Technology

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This paper addresses photometric distortion problems of a compact 3D scanning sensor which is composed of a micro-size and inexpensive camera-projector system. Recently, many micro-size cameras and projectors are available. However, erroneous 3D scanning results may arise due to the poor and nonlinear photometric properties of the sensors. This paper solves two inherent photometric distortions of the sensors. First, the response functions of both the camera and projector are derived from the least squares solutions of passive and active calibration, respectively. Second, vignetting correction of the vision camera is done by using a conventional method, however the projector vignetting is corrected by using the planar homography between the image planes of the projector and camera, respectively. Experimental results show that the proposed technique enhances the linear properties of the phase patterns that are generated by the sensor.

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Addressing Radiometric Nonidealities: A Unified Framework

Abstract: Cameras may have non-ideal radiometric aspects, including spatial non-uniformity, e.g.,

Cited by 48 publications

References 30 publications

Better Shading for Better Shape Recovery

Better Shading for Better Shape Recovery

Priors for Large Photo Collections and What They Reveal about Cameras

Correction of Photometric Distortion of a Micro Camera-Projector System for Structured Light 3D Scanning

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