The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Brian A Wandell - One of the best experts on this subject based on the ideXlab platform.
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designing Illuminant spectral power distributions for surface classification
Computer Vision and Pattern Recognition, 2017Co-Authors: Henryk Blasinski, Joyce E Farrell, Brian A WandellAbstract:There are many scientific, medical and industrial imaging applications where users have full control of the scene illumination and color reproduction is not the primary objective For example, it is possible to co-design sensors and spectral illumination in order to classify and detect changes in biological tissues, organic and inorganic materials, and object surface properties. In this paper, we propose two different approaches to Illuminant spectrum selection for surface classification. In the supervised framework we formulate a biconvex optimization problem where we alternate between optimizing support vector classifier weights and optimal Illuminants. We also describe a sparse Principal Component Analysis (PCA) dimensionality reduction approach that can be used with unlabeled data. We efficiently solve the non-convex PCA problem using a convex relaxation and Alternating Direction Method of Multipliers (ADMM). We compare the classification accuracy of a monochrome imaging sensor with optimized Illuminants to the classification accuracy of conventional RGB cameras with natural broadband illumination.
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simultaneous surface reflectance and fluorescence spectra estimation
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Henryk Blasinski, Joyce E Farrell, Brian A WandellAbstract:There is widespread interest in estimating the fluorescence properties of natural materials in an image. However, the separation between reflected and fluoresced components is difficult, because it is impossible to distinguish reflected and fluoresced photons without controlling the Illuminant spectrum. We show how to jointly estimate the reflectance and fluorescence from a single set of images acquired under multiple Illuminants. We present a framework based on a linear approximation to the physical equations describing image formation in terms of surface spectral reflectance and fluorescence due to multiple fluorophores. We relax the non-convex, inverse estimation problem in order to jointly estimate the reflectance and fluorescence properties in a single optimization step and we use the Alternating Direction Method of Multipliers (ADMM) approach to efficiently find a solution. We provide a software implementation of the solver for our method and prior methods. We evaluate the accuracy and reliability of the method using both simulations and experimental data. To acquire data to test the methods, we built a custom imaging system using a monochrome camera, a filter wheel with bandpass transmissive filters and a small number of light emitting diodes. We compared the system and algorithm performance with the ground truth as well as with prior methods. Our approach produces lower errors compared to earlier algorithms.
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natural scene Illuminant estimation using the sensor correlation
Proceedings of the IEEE, 2002Co-Authors: Shoji Tominaga, Brian A WandellAbstract:This paper describes practical algorithms and experimental results concerning Illuminant classification. Specifically, we review the sensor correlation algorithm for Illuminant classification and we discuss four changes that improve the algorithm's estimation accuracy and broaden its applicability. First, we space the classification Illuminants evenly along the reciprocal scale of color temperature, called "mired," rather than the original color-temperature scale. This improves the perceptual uniformity of the Illuminant classification set. Second, we calculate correlation values between the image color gamut and the reference Illuminant gamut, rather than between the image pixels and the Illuminant gamuts. This change makes the algorithm more reliable. Third, we introduce a new image scaling operation to adjust for overall intensity differences between images. Fourth, we develop the three-dimensional classification algorithms using all three-color channels and compare this with the original two algorithms from the viewpoint of accuracy and computational efficiency. The image processing algorithms incorporating these changes are evaluated using a real image database with calibrated scene Illuminants.
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scene Illuminant classification brighter is better
Journal of The Optical Society of America A-optics Image Science and Vision, 2001Co-Authors: Shoji Tominaga, Satoru Ebisui, Brian A WandellAbstract:Knowledge of the scene Illuminant spectral power distribution is useful for many imaging applications, such as color image reproduction and automatic algorithms for image database applications. In many applications accurate spectral characterization of the Illuminant is impossible because the input device acquires only three spectral samples. In such applications it is sensible to set a more limited objective of classifying the Illuminant as belonging to one of several likely types. We describe a data set of natural images with measured Illuminants for testing Illuminant classification algorithms. One simple type of algorithm is described and evaluated by using the new data set. The empirical measurements show that Illuminant information is more reliable in bright regions than in dark regions. Theoretical predictions of the algorithm’s classification performance with respect to scene Illuminant blackbody color temperature are tested and confirmed by using the natural-image data set.
Shoji Tominaga - One of the best experts on this subject based on the ideXlab platform.
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estimation of bispectral donaldson matrices of fluorescent objects by using two Illuminant projections
Journal of The Optical Society of America A-optics Image Science and Vision, 2015Co-Authors: Shoji Tominaga, Keita Hirai, Takahiko HoriuchiAbstract:This paper proposes a method for estimating the bispectral Donaldson matrices of fluorescent objects by using only two Illuminant projections with continuous spectral power distributions. The Donaldson matrix represents the spectral radiance factor consisting of the sum of two components: a reflected radiance factor and a luminescent radiance factor. First, we describe the spectral characteristics of the observed matrix and model the matrix so that the luminescent radiance factor is separable into the emission and excitation wavelength components. We make no assumption as to the spectral shapes of any components, but derive a physical model that is useful for predicting the excitation spectral component from the reflected radiance component. An algorithm is developed to estimate the entire elements of the Donaldson matrix based on only two sets of spectral sensor outputs under two different Illuminants. We suggest that the difference between the observed reflected radiance factors under the two different Illuminants is not caused by the reflected radiance component, but only the luminescent radiance component. The algorithm is a sequential estimation of three radiance components of luminescent excitation, luminescent emission, and reflection. The feasibility of the proposed method is confirmed in experiments using a variety of fluorescent samples. The estimation accuracy is evaluated numerically in root-mean squared error and the color difference under the assumption of a viewing Illuminant. An optimal selection of the Illuminant pair is shown based on a simulation experiment using blackbody radiators with different color temperatures.
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estimation of multiple Illuminants based on specular highlight detection
Computational Color Imaging Workshop, 2011Co-Authors: Yoshie Imai, Hideki Kadoi, Takahiko Horiuchi, Yu Kato, Shoji TominagaAbstract:This paper proposes a method for estimating the scene Illuminant spectral power distributions of multiple light sources under a complex illumination environment. The spectral power distributions including natural and artificial Illuminants are estimated based on the image data from a high-dimensional spectral imaging system. We note that specular highlights on inhomogeneous dielectric object surfaces includes much information about scene illumination according to the dichromatic reflection model. First, we describe several methods for detecting specular highlight areas. We assume a curved object surface illuminated by multiple light sources from different directions. Then we estimate the Illuminant spectrum of each light source from the image data of that highlight area. Based on this principle, we present an algorithm to estimate multiple Illuminants. The feasibility of the proposed method is shown in experiments.
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natural scene Illuminant estimation using the sensor correlation
Proceedings of the IEEE, 2002Co-Authors: Shoji Tominaga, Brian A WandellAbstract:This paper describes practical algorithms and experimental results concerning Illuminant classification. Specifically, we review the sensor correlation algorithm for Illuminant classification and we discuss four changes that improve the algorithm's estimation accuracy and broaden its applicability. First, we space the classification Illuminants evenly along the reciprocal scale of color temperature, called "mired," rather than the original color-temperature scale. This improves the perceptual uniformity of the Illuminant classification set. Second, we calculate correlation values between the image color gamut and the reference Illuminant gamut, rather than between the image pixels and the Illuminant gamuts. This change makes the algorithm more reliable. Third, we introduce a new image scaling operation to adjust for overall intensity differences between images. Fourth, we develop the three-dimensional classification algorithms using all three-color channels and compare this with the original two algorithms from the viewpoint of accuracy and computational efficiency. The image processing algorithms incorporating these changes are evaluated using a real image database with calibrated scene Illuminants.
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scene Illuminant classification brighter is better
Journal of The Optical Society of America A-optics Image Science and Vision, 2001Co-Authors: Shoji Tominaga, Satoru Ebisui, Brian A WandellAbstract:Knowledge of the scene Illuminant spectral power distribution is useful for many imaging applications, such as color image reproduction and automatic algorithms for image database applications. In many applications accurate spectral characterization of the Illuminant is impossible because the input device acquires only three spectral samples. In such applications it is sensible to set a more limited objective of classifying the Illuminant as belonging to one of several likely types. We describe a data set of natural images with measured Illuminants for testing Illuminant classification algorithms. One simple type of algorithm is described and evaluated by using the new data set. The empirical measurements show that Illuminant information is more reliable in bright regions than in dark regions. Theoretical predictions of the algorithm’s classification performance with respect to scene Illuminant blackbody color temperature are tested and confirmed by using the natural-image data set.
Henryk Blasinski - One of the best experts on this subject based on the ideXlab platform.
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Simultaneous Surface Reflectance and Fluorescence Spectra Estimation
IEEE Transactions on Image Processing, 2020Co-Authors: Henryk Blasinski, Joyce Farrell, Brian WandellAbstract:There is widespread interest in estimating the fluorescence properties of natural materials in an image. However, the separation between reflected and fluoresced components is difficult, because it is impossible to distinguish reflected and fluoresced photons without controlling the Illuminant spectrum. We show how to jointly estimate the reflectance and fluorescence from a single set of images acquired under multiple Illuminants. We present a framework based on a linear approximation to the physical equations describing image formation in terms of surface spectral reflectance and fluorescence due to multiple fluorophores. We relax the non-convex, inverse estimation problem in order to jointly estimate the reflectance and fluorescence properties in a single optimization step. We provide a software implementation of the solver for our method and prior methods. We evaluate the accuracy and reliability of the method using both simulations and experimental data. To evaluate the methods experimentally we built a custom imaging system using a monochrome camera, a filter wheel with bandpass transmissive filters and a small number of light emitting diodes. We compared the methods based upon our framework with the ground truth as well as with prior methods.
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designing Illuminant spectral power distributions for surface classification
Computer Vision and Pattern Recognition, 2017Co-Authors: Henryk Blasinski, Joyce E Farrell, Brian A WandellAbstract:There are many scientific, medical and industrial imaging applications where users have full control of the scene illumination and color reproduction is not the primary objective For example, it is possible to co-design sensors and spectral illumination in order to classify and detect changes in biological tissues, organic and inorganic materials, and object surface properties. In this paper, we propose two different approaches to Illuminant spectrum selection for surface classification. In the supervised framework we formulate a biconvex optimization problem where we alternate between optimizing support vector classifier weights and optimal Illuminants. We also describe a sparse Principal Component Analysis (PCA) dimensionality reduction approach that can be used with unlabeled data. We efficiently solve the non-convex PCA problem using a convex relaxation and Alternating Direction Method of Multipliers (ADMM). We compare the classification accuracy of a monochrome imaging sensor with optimized Illuminants to the classification accuracy of conventional RGB cameras with natural broadband illumination.
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simultaneous surface reflectance and fluorescence spectra estimation
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Henryk Blasinski, Joyce E Farrell, Brian A WandellAbstract:There is widespread interest in estimating the fluorescence properties of natural materials in an image. However, the separation between reflected and fluoresced components is difficult, because it is impossible to distinguish reflected and fluoresced photons without controlling the Illuminant spectrum. We show how to jointly estimate the reflectance and fluorescence from a single set of images acquired under multiple Illuminants. We present a framework based on a linear approximation to the physical equations describing image formation in terms of surface spectral reflectance and fluorescence due to multiple fluorophores. We relax the non-convex, inverse estimation problem in order to jointly estimate the reflectance and fluorescence properties in a single optimization step and we use the Alternating Direction Method of Multipliers (ADMM) approach to efficiently find a solution. We provide a software implementation of the solver for our method and prior methods. We evaluate the accuracy and reliability of the method using both simulations and experimental data. To acquire data to test the methods, we built a custom imaging system using a monochrome camera, a filter wheel with bandpass transmissive filters and a small number of light emitting diodes. We compared the system and algorithm performance with the ground truth as well as with prior methods. Our approach produces lower errors compared to earlier algorithms.
Volodymyr Pyliavskyi - One of the best experts on this subject based on the ideXlab platform.
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development of the algorithm of video image adaptation to spectral power distribution of Illuminants
Eastern-European Journal of Enterprise Technologies, 2019Co-Authors: Volodymyr PyliavskyiAbstract:Proposals for further progress of video technologies, issues that need to be resolved to implement this progress and possible ways to implement them in real devices of special and general application are made. It is proposed to supplement the conventional model of the video path with a color perception model and an adaptive model of the spectral power distribution of the Illuminant. Attention is paid to the end devices of the video path, which may introduce unacceptable changes in the transmitted video information, namely color. The schemes of the algorithm of adaptation to the spectral power distribution of the Illuminant are presented. The possibility of universal use of the proposed algorithm in video transmission systems is considered. The algorithm of video image adaptation to the spectral power distribution of Illuminants based on the selection of reference spectral power distributions with the given color coordinates is proposed. The algorithm of allocation of the spectral power distribution of the Illuminant from the overall image scene is presented. Metrological support to assess the influence of the Illuminant on the quality of color rendering is proposed. It is proposed to use spectral color distributions, the set of which is presented in the paper, as optical test images for testing the color rendering quality. Comparative characteristics with existing sets of spectral power distributions are presented and it is shown that they are not enough to implement the proposed algorithm. The simulation results prove the necessity and advantages of using the proposed algorithm. The image after the application of the algorithm is such if it was observed in sunlight, regardless of what type of lighting was used during shooting or observation. In addition, the presented algorithm allows adaptation to the spectral power distribution of various Illuminants, such as incandescent lamps, fluorescent, LED, signal flares, and the like
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development of the algorithm of video image adaptation to spectral power distribution of Illuminants
Eastern-European Journal of Enterprise Technologies, 2019Co-Authors: Volodymyr PyliavskyiAbstract:Proposals for further progress of video technologies, issues that need to be resolved to implement this progress and possible ways to implement them in real devices of special and general application are made. It is proposed to supplement the conventional model of the video path with a color perception model and an adaptive model of the spectral power distribution of the Illuminant. Attention is paid to the end devices of the video path, which may introduce unacceptable changes in the transmitted video information, namely color. The schemes of the algorithm of adaptation to the spectral power distribution of the Illuminant are presented. The possibility of universal use of the proposed algorithm in video transmission systems is considered. The algorithm of video image adaptation to the spectral power distribution of Illuminants based on the selection of reference spectral power distributions with the given color coordinates is proposed. The algorithm of allocation of the spectral power distribution of the Illuminant from the overall image scene is presented. Metrological support to assess the influence of the Illuminant on the quality of color rendering is proposed. It is proposed to use spectral color distributions, the set of which is presented in the paper, as optical test images for testing the color rendering quality. Comparative characteristics with existing sets of spectral power distributions are presented and it is shown that they are not enough to implement the proposed algorithm. The simulation results prove the necessity and advantages of using the proposed algorithm. The image after the application of the algorithm is such if it was observed in sunlight, regardless of what type of lighting was used during shooting or observation. In addition, the presented algorithm allows adaptation to the spectral power distribution of various Illuminants, such as incandescent lamps, fluorescent, LED, signal flares, and the like
Andreas Bartels - One of the best experts on this subject based on the ideXlab platform.
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Invariance of surface color representations across Illuminant changes in the human cortex.
NeuroImage, 2017Co-Authors: Mm Bannert, Andreas BartelsAbstract:A central problem in color vision is that the light reaching the eye from a given surface can vary dramatically depending on the illumination. Despite this, our color percept, the brain's estimate of surface reflectance, remains remarkably stable. This phenomenon is called color constancy. Here we investigated which human brain regions represent surface color in a way that is invariant with respect to Illuminant changes. We used physically realistic rendering methods to display natural yet abstract 3D scenes that were displayed under three distinct Illuminants. The scenes embedded, in different conditions, surfaces that differed in their surface color (i.e. in their reflectance property). We used multivariate fMRI pattern analysis to probe neural coding of surface reflectance and Illuminant, respectively. While all visual regions encoded surface color when viewed under the same Illuminant, we found that only in V1 and V4α surface color representations were invariant to illumination changes. Along the visual hierarchy there was a gradient from V1 to V4α to increasingly encode surface color rather than illumination. Finally, effects of a stimulus manipulation on individual behavioral color constancy indices correlated with neural encoding of the Illuminant in hV4. This provides neural evidence for the Equivalent Illuminant Model. Our results provide a principled characterization of color constancy mechanisms across the visual hierarchy, and demonstrate complementary contributions in early and late processing stages.
