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Modeling the space of camera response functions.
- IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI),
, 2004
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What is the space of camera response functions
- In IEEE Computer Society conference on Computer Vision and Pattern Recognition (CVPR
, 2003
"... Many vision applications require precise measurement of scene radiance. The function relating scene radiance to image brightness is called the camera response. We analyze the properties that all camera responses share. This allows us to find the constraints that any response function must satisfy. T ..."
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Cited by 55 (2 self)
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Many vision applications require precise measurement of scene radiance. The function relating scene radiance to image brightness is called the camera response. We analyze the properties that all camera responses share. This allows us to find the constraints that any response function must satisfy. These constraints determine the theoretical space of all possible camera responses. We have collected a diverse database of real-world camera response functions (DoRF). Using this database we show that real-world responses occupy a small part of the theoretical space of all possible responses. We combine the constraints from our theoretical space with the data from DoRF to create a low-parameter Empirical Model of Response (EMoR). This response model allows us to accurately interpolate the complete response function of a camera from a small number of measurements obtained using a standard chart. We also show that the model can be used to accurately estimate the camera response from images of an arbitrary scene taken using different exposures. The DoRF database and the EMoR model can be downloaded at
Robust radiometric calibration and vignetting correction
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2008
"... In many computer vision systems, it is assumed that the image brightness of a point directly reflects the scene radiance of the point. However, the assumption does not hold in most cases due to nonlinear camera response function, exposure changes, and vignetting. The effects of these factors are mos ..."
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Cited by 46 (7 self)
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In many computer vision systems, it is assumed that the image brightness of a point directly reflects the scene radiance of the point. However, the assumption does not hold in most cases due to nonlinear camera response function, exposure changes, and vignetting. The effects of these factors are most visible in image mosaics and textures of 3D models where colors look inconsistent and notable boundaries exist. In this paper, we propose a full radiometric calibration algorithm that includes robust estimation of the radiometric response function, exposures, and vignetting. By decoupling the effect of vignetting from the response function estimation, we approach each process in a manner that is robust to noise and outliers. We verify our algorithm with both synthetic and real data which shows significant improvement compared to existing methods. We apply our estimation results to radiometrically align images for seamless mosaics and 3D model textures. We also use our method to create high dynamic range (HDR) mosaics which are more representative of the scene than normal mosaics.
Continuous lifelong capture of personal experience with eyetap
- in Proceedings of the First ACM Workshop on Continuous Archival and Retrieval of Personal Experiences
, 2004
"... I begin with the argument that continuous archival of personal experience requires certain criteria to be met. In particular, for continuous usage, it is essential that each ray of light entering the eye be collinear with a corresponding ray of light entering the device, in at least one mode of oper ..."
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Cited by 24 (1 self)
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I begin with the argument that continuous archival of personal experience requires certain criteria to be met. In particular, for continuous usage, it is essential that each ray of light entering the eye be collinear with a corresponding ray of light entering the device, in at least one mode of operation. This is called the EyeTap criterion, and devices meeting this criterion are called EyeTap devices. Secondly, I outline Mediated Reality as a necessary framework for continuous archival and retrieval of personal experience. Thirdly, I show some examples of personalized experience capture (i.e. visual art). Finally, I outline the social issues of such devices, in particular, the accidentally discovered inverse to surveillance that I call “sosuveillance”. It is argued that an equilibrium between surveillance and sousveillance is implicit in the archival of personal experiences.
High dynamic range imaging for digital still camera: an overview
, 2003
"... We present a collection of methods and algorithms able to deal with high dynamic ranges of real pictures acquired by digital engines e.g., charge-coupled device (CCD/CMOS) cameras. An accurate image acquisition can be challenging under difficult light conditions. A few techniques that overcome dynam ..."
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Cited by 21 (9 self)
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We present a collection of methods and algorithms able to deal with high dynamic ranges of real pictures acquired by digital engines e.g., charge-coupled device (CCD/CMOS) cameras. An accurate image acquisition can be challenging under difficult light conditions. A few techniques that overcome dynamic range limitations problems are reported. The presented methods allow the recovery of the original radiance values of the final 8-bit-depth image starting from differently exposed pictures. This allows the capture of both low- and high-light details by merging the various pictures into a single map, thus providing a more faithful description of what the real world scene was. However, in order to be viewed on a common computer monitor, the map needs to be compressed and requantized while preserving the visibility of details. The main problem comes from the fact that the contrast of the radiance values is usually far greater than that of the display device. Various related techniques are reviewed and discussed
Jointly Registering Images in Domain and Range by Piecewise Linear Comparametric Analysis
, 2003
"... This paper describes an approach whereby comparametric analysis is used in jointly registering image pairs in their domain and range, i.e., in their spatial coordinates and pixel values, respectively. This is accomplished by approximating a camera's nonlinear comparametric function with a const ..."
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Cited by 21 (3 self)
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This paper describes an approach whereby comparametric analysis is used in jointly registering image pairs in their domain and range, i.e., in their spatial coordinates and pixel values, respectively. This is accomplished by approximating a camera's nonlinear comparametric function with a constrained piecewise linear one. The optimal fitting of this approximation to comparagram data is then used in a re-parameterized version of the camera's comparametric function to estimate the exposure difference between images. Doing this allows the inherently nonlinear problem of joint domain and range registration to be performed using a computationally attractive least squares formalism. The paper first presents the range registration process and then describes the strategy for performing the joint registration. The models used allow for the pair-wise registration of images taken from a camera that can automatically adjust its exposure as well as tilt, pan, rotate and zoom about its optical center. Results concerning the joint registration as well as range-only registration are provided to demonstrate the method's effectiveness.
Automatic Image Enhancement by Content Dependent Exposure Correction
- EURASIP - Journal on Applied Signal Processing
, 2004
"... We describe an automatic image enhancement technique based on features extraction methods. The approach takes into account images in Bayer data format, captured using a CCD/CMOS sensor and/or 24-bit color images; after identifying the visually significant features, the algorithm adjusts the exposure ..."
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Cited by 21 (9 self)
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We describe an automatic image enhancement technique based on features extraction methods. The approach takes into account images in Bayer data format, captured using a CCD/CMOS sensor and/or 24-bit color images; after identifying the visually significant features, the algorithm adjusts the exposure level using a “camera response”-like function; then a final HUE reconstruction is achieved. This method is suitable for handset devices acquisition systems (e.g., mobile phones, PDA, etc.). The process is also suitable to solve some of the typical drawbacks due to several factors such as poor optics, absence of flashgun, and so forth.
Using Geometry Invariants for Camera Response Function Estimation
"... In this paper, we present a new single-image camera response function (CRF) estimation method using geometry invariants (GI). We derive mathematical properties and geometric interpretation for GI, which lend insight to addressing various algorithm implementation issues in a principled way. In contra ..."
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Cited by 18 (5 self)
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In this paper, we present a new single-image camera response function (CRF) estimation method using geometry invariants (GI). We derive mathematical properties and geometric interpretation for GI, which lend insight to addressing various algorithm implementation issues in a principled way. In contrast to the previous single-image CRF estimation methods, our method provides a constraint equation for selecting the potential target data points. Comparing to the prior work, our experiment is conducted over more extensive data and our method is flexible in that its estimation accuracy and stability can be improved whenever more than one image is available. The geometry invariance theory is novel and may be of wide interest. 1.
Radiometric alignment of image sequences
- Proc. IEEE Conference on Computer Vision and Pattern Recognition
, 2004
"... Color values in an image are related to image irradiance by a nonlinear function called radiometric response function. Since this function depends on the aperture and the shutter speed, image intensity of a same object may vary during the acquisition of an image sequence due to auto exposure feature ..."
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Cited by 15 (3 self)
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Color values in an image are related to image irradiance by a nonlinear function called radiometric response function. Since this function depends on the aperture and the shutter speed, image intensity of a same object may vary during the acquisition of an image sequence due to auto exposure feature of the camera. While this is desirable to make optimal use of the limited dynamic range of most cameras, this causes problems for a number of applications in computer vision. In this paper, we propose a method for estimating the radiometric response function and apply it to radiometrically align images so that the color values are consistent for all images of a sequence. Our approach computes the response function, exposure and white balance changes between images (up to some ambiguity) for a moving camera without any prior knowledge about exposures. We show the performance of our algorithm by estimating the response function from synthetic images and also from real world data, using it to radiometrically align the images. 1
Estimation of the Radiometric Response Functions of a Color Camera from Differently Illuminated Images
- in Proceedings of the IEEE International Conference on Image Processing
, 2004
"... The mapping that relates the image irradiance to the image brightness (intensity) is known as the Radiometric Response Function or Camera Response Function. This usually unknown mapping is nonlinear and varies from one color channel to another. In this paper, we present a method to estimate the radi ..."
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Cited by 13 (0 self)
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The mapping that relates the image irradiance to the image brightness (intensity) is known as the Radiometric Response Function or Camera Response Function. This usually unknown mapping is nonlinear and varies from one color channel to another. In this paper, we present a method to estimate the radiometric response functions (of R, G and B channels) of a color camera directly from the images of an arbitrary scene taken under different illumination conditions (The illumination conditions are not assumed to be known). The response function of a channel is modeled as a gamma curve and is recovered by using a constrained nonlinear minimization approach by exploiting the fact that the material properties of the scene remain constant in all the images. The performance of the proposed method is demonstrated experimentally.
