The Experts below are selected from a list of 1143 Experts worldwide ranked by ideXlab platform
Takahiko Horiuchi - One of the best experts on this subject based on the ideXlab platform.
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SITIS - Estimation of Two Illuminant Spectral Power Distributions from Highlights of Overlapping Illuminants
2013 International Conference on Signal-Image Technology & Internet-Based Systems, 2013Co-Authors: Nguyen Thuc Dieu Hang, Takahiko Horiuchi, Keita Hirai, Shoji TominagaAbstract:A method is proposed for estimating two illuminant spectral power distributions from the Highlights of overlapping illuminants on an object. It was assumed in most previous studies that Specular Highlights on object surfaces are caused by a single light source, or by separate multiple light sources. In this paper, we assume that two Highlights from different light sources are overlapped on object surfaces, and estimate the spectral power distributions of both. A multiband camera system is used for capturing spectral images of dielectric objects in a scene. First, we detect Specular Highlight areas from the spectral image. Then, the illuminant spectra of two light sources are simultaneously estimated based on cluster classification of particular pixel distribution in the Highlight area. The feasibility of the proposed method is examined in experiments on real-world scenes.
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ECCV Workshops (2) - An effective method for illumination-invariant representation of color images
Computer Vision – ECCV 2012. Workshops and Demonstrations, 2012Co-Authors: Takahiko Horiuchi, Abdelhameed Ibrahim, Hideki Kadoi, Shoji TominagaAbstract:This paper proposes a method for illumination-invariant representation of natural color images. The invariant representation is derived, not using spectral reflectance, but using only RGB camera outputs. We suppose that the materials of target objects are composed of dielectric or metal, and the surfaces include illumination effects such as Highlight, gloss, or Specularity. We preset the procedure for realizing the invariant representation in three steps: (1) detection of Specular Highlight, (2) illumination color estimation, and (3) invariant representation for reflectance color. The performance of the proposed method is examined in experiments using real-world objects including metals and dielectrics in detail. The limitation of the method is also discussed. Finally, the proposed representation is applied to the edge detection problem of natural color images.
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SSIAI - Illumination-invariant representation for natural color images and its application
2012 IEEE Southwest Symposium on Image Analysis and Interpretation, 2012Co-Authors: Abdelhameed Ibrahim, Takahiko Horiuchi, Shoji TominagaAbstract:Illumination factors such as shading, shadow, and Specular Highlight, observed from object surfaces in a natural scene, affect seriously the appearance and analysis of the color images. This paper proposes an illumination-invariant representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. Illumination color is estimated from Specular reflection component on inhomogeneous surfaces without using a reference white standard. The overall performance of the proposed representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of effective edge detection is introduced and compared with the state-of-the-art illumination-invariant methods.
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Invariant representation for spectral reflectance images and its application
EURASIP Journal on Image and Video Processing, 2011Co-Authors: Abdelhameed Ibrahim, Shoji Tominaga, Takahiko HoriuchiAbstract:Spectral images as well as color images observed from object surfaces are much influenced by various illumination conditions such as shading and Specular Highlight. Several invariant representations were proposed for these conditions using the standard dichromatic reflection model of dielectric materials. However, these representations are inadequate for other materials like metal. This article proposes an invariant representation that is derived from the standard dichromatic reflection model for dielectric and the extended dichromatic reflection model for metal. We show that a normalized surface-spectral reflectance by the minimum reflectance is invariant to Highlights, shading, surface geometry, and illumination intensity. Here the illumination spectrum and the spectral sensitivity functions of the imaging system are measured in a separate way. As an application of the proposed invariant representation, a segmentation algorithm based on the proposed representation is presented for effectively segmenting spectral images of natural scenes and bare circuit boards.
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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.
Shoji Tominaga - One of the best experts on this subject based on the ideXlab platform.
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SITIS - Estimation of Two Illuminant Spectral Power Distributions from Highlights of Overlapping Illuminants
2013 International Conference on Signal-Image Technology & Internet-Based Systems, 2013Co-Authors: Nguyen Thuc Dieu Hang, Takahiko Horiuchi, Keita Hirai, Shoji TominagaAbstract:A method is proposed for estimating two illuminant spectral power distributions from the Highlights of overlapping illuminants on an object. It was assumed in most previous studies that Specular Highlights on object surfaces are caused by a single light source, or by separate multiple light sources. In this paper, we assume that two Highlights from different light sources are overlapped on object surfaces, and estimate the spectral power distributions of both. A multiband camera system is used for capturing spectral images of dielectric objects in a scene. First, we detect Specular Highlight areas from the spectral image. Then, the illuminant spectra of two light sources are simultaneously estimated based on cluster classification of particular pixel distribution in the Highlight area. The feasibility of the proposed method is examined in experiments on real-world scenes.
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ECCV Workshops (2) - An effective method for illumination-invariant representation of color images
Computer Vision – ECCV 2012. Workshops and Demonstrations, 2012Co-Authors: Takahiko Horiuchi, Abdelhameed Ibrahim, Hideki Kadoi, Shoji TominagaAbstract:This paper proposes a method for illumination-invariant representation of natural color images. The invariant representation is derived, not using spectral reflectance, but using only RGB camera outputs. We suppose that the materials of target objects are composed of dielectric or metal, and the surfaces include illumination effects such as Highlight, gloss, or Specularity. We preset the procedure for realizing the invariant representation in three steps: (1) detection of Specular Highlight, (2) illumination color estimation, and (3) invariant representation for reflectance color. The performance of the proposed method is examined in experiments using real-world objects including metals and dielectrics in detail. The limitation of the method is also discussed. Finally, the proposed representation is applied to the edge detection problem of natural color images.
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SSIAI - Illumination-invariant representation for natural color images and its application
2012 IEEE Southwest Symposium on Image Analysis and Interpretation, 2012Co-Authors: Abdelhameed Ibrahim, Takahiko Horiuchi, Shoji TominagaAbstract:Illumination factors such as shading, shadow, and Specular Highlight, observed from object surfaces in a natural scene, affect seriously the appearance and analysis of the color images. This paper proposes an illumination-invariant representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. Illumination color is estimated from Specular reflection component on inhomogeneous surfaces without using a reference white standard. The overall performance of the proposed representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of effective edge detection is introduced and compared with the state-of-the-art illumination-invariant methods.
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Invariant representation for spectral reflectance images and its application
EURASIP Journal on Image and Video Processing, 2011Co-Authors: Abdelhameed Ibrahim, Shoji Tominaga, Takahiko HoriuchiAbstract:Spectral images as well as color images observed from object surfaces are much influenced by various illumination conditions such as shading and Specular Highlight. Several invariant representations were proposed for these conditions using the standard dichromatic reflection model of dielectric materials. However, these representations are inadequate for other materials like metal. This article proposes an invariant representation that is derived from the standard dichromatic reflection model for dielectric and the extended dichromatic reflection model for metal. We show that a normalized surface-spectral reflectance by the minimum reflectance is invariant to Highlights, shading, surface geometry, and illumination intensity. Here the illumination spectrum and the spectral sensitivity functions of the imaging system are measured in a separate way. As an application of the proposed invariant representation, a segmentation algorithm based on the proposed representation is presented for effectively segmenting spectral images of natural scenes and bare circuit boards.
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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.
Narendra Ahuja - One of the best experts on this subject based on the ideXlab platform.
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Efficient and Robust Specular Highlight Removal
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015Co-Authors: Qingxiong Yang, Jinhui Tang, Narendra AhujaAbstract:A robust and effective Specular Highlight removal method is proposed in this paper. It is based on a key observation—the maximum fraction of the diffuse colour component in diffuse local patches in colour images changes smoothly. The Specular pixels can thus be treated as noise in this case. This property allows the Specular Highlights to be removed in an image denoising fashion: an edge-preserving low-pass filter (e.g., the bilateral filter) can be used to smooth the maximum fraction of the colour components of the original image to remove the noise contributed by the Specular pixels. Recent developments in fast bilateral filtering techniques enable the proposed method to run over $200\times$ faster than state-of-the-art techniques on a standard CPU and differentiates it from previous work.
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real time Specular Highlight removal using bilateral filtering
European Conference on Computer Vision, 2010Co-Authors: Qingxiong Yang, Shengnan Wang, Narendra AhujaAbstract:In this paper, we propose a simple but effective Specular Highlight removal method using a single input image. Our method is based on a key observation - the maximum fraction of the diffuse color component (so called maximum diffuse chromaticity in the literature) in local patches in color images changes smoothly. Using this property, we can estimate the maximum diffuse chromaticity values of the Specular pixels by directly applying low-pass filter to the maximum fraction of the color components of the original image, such that the maximum diffuse chromaticity values can be propagated from the diffuse pixels to the Specular pixels. The diffuse color at each pixel can then be computed as a nonlinear function of the estimated maximum diffuse chromaticity. Our method can be directly extended for multi-color surfaces if edge-preserving filters (e.g., bilateral filter) are used such that the smoothing can be guided by the maximum diffuse chromaticity. But maximum diffuse chromaticity is to be estimated. We thus present an approximation and demonstrate its effectiveness. Recent development in fast bilateral filtering techniques enables our method to run over 200× faster than the state-of-the-art on a standard CPU and differentiates our method from previous work.
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ECCV (4) - Real-time Specular Highlight removal using bilateral filtering
Computer Vision – ECCV 2010, 2010Co-Authors: Qingxiong Yang, Shengnan Wang, Narendra AhujaAbstract:In this paper, we propose a simple but effective Specular Highlight removal method using a single input image. Our method is based on a key observation - the maximum fraction of the diffuse color component (so called maximum diffuse chromaticity in the literature) in local patches in color images changes smoothly. Using this property, we can estimate the maximum diffuse chromaticity values of the Specular pixels by directly applying low-pass filter to the maximum fraction of the color components of the original image, such that the maximum diffuse chromaticity values can be propagated from the diffuse pixels to the Specular pixels. The diffuse color at each pixel can then be computed as a nonlinear function of the estimated maximum diffuse chromaticity. Our method can be directly extended for multi-color surfaces if edge-preserving filters (e.g., bilateral filter) are used such that the smoothing can be guided by the maximum diffuse chromaticity. But maximum diffuse chromaticity is to be estimated. We thus present an approximation and demonstrate its effectiveness. Recent development in fast bilateral filtering techniques enables our method to run over 200× faster than the state-of-the-art on a standard CPU and differentiates our method from previous work.
Abdelhameed Ibrahim - One of the best experts on this subject based on the ideXlab platform.
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ECCV Workshops (2) - An effective method for illumination-invariant representation of color images
Computer Vision – ECCV 2012. Workshops and Demonstrations, 2012Co-Authors: Takahiko Horiuchi, Abdelhameed Ibrahim, Hideki Kadoi, Shoji TominagaAbstract:This paper proposes a method for illumination-invariant representation of natural color images. The invariant representation is derived, not using spectral reflectance, but using only RGB camera outputs. We suppose that the materials of target objects are composed of dielectric or metal, and the surfaces include illumination effects such as Highlight, gloss, or Specularity. We preset the procedure for realizing the invariant representation in three steps: (1) detection of Specular Highlight, (2) illumination color estimation, and (3) invariant representation for reflectance color. The performance of the proposed method is examined in experiments using real-world objects including metals and dielectrics in detail. The limitation of the method is also discussed. Finally, the proposed representation is applied to the edge detection problem of natural color images.
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SSIAI - Illumination-invariant representation for natural color images and its application
2012 IEEE Southwest Symposium on Image Analysis and Interpretation, 2012Co-Authors: Abdelhameed Ibrahim, Takahiko Horiuchi, Shoji TominagaAbstract:Illumination factors such as shading, shadow, and Specular Highlight, observed from object surfaces in a natural scene, affect seriously the appearance and analysis of the color images. This paper proposes an illumination-invariant representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. Illumination color is estimated from Specular reflection component on inhomogeneous surfaces without using a reference white standard. The overall performance of the proposed representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of effective edge detection is introduced and compared with the state-of-the-art illumination-invariant methods.
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Invariant representation for spectral reflectance images and its application
EURASIP Journal on Image and Video Processing, 2011Co-Authors: Abdelhameed Ibrahim, Shoji Tominaga, Takahiko HoriuchiAbstract:Spectral images as well as color images observed from object surfaces are much influenced by various illumination conditions such as shading and Specular Highlight. Several invariant representations were proposed for these conditions using the standard dichromatic reflection model of dielectric materials. However, these representations are inadequate for other materials like metal. This article proposes an invariant representation that is derived from the standard dichromatic reflection model for dielectric and the extended dichromatic reflection model for metal. We show that a normalized surface-spectral reflectance by the minimum reflectance is invariant to Highlights, shading, surface geometry, and illumination intensity. Here the illumination spectrum and the spectral sensitivity functions of the imaging system are measured in a separate way. As an application of the proposed invariant representation, a segmentation algorithm based on the proposed representation is presented for effectively segmenting spectral images of natural scenes and bare circuit boards.
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A spectral invariant representation of spectral reflectance
Optical Review, 2011Co-Authors: Abdelhameed Ibrahim, Shoji Tominaga, Takahiko HoriuchiAbstract:Spectral image acquisition as well as color image is affected by several illumination factors such as shading, gloss, and Specular Highlight. Spectral invariant representations for these factors were proposed for the standard dichromatic reflection model of inhomogeneous dielectric materials. However, these representations are inadequate for other characteristic materials like metal. This paper proposes a more general spectral invariant representation for obtaining reliable spectral reflectance images. Our invariant representation is derived from the standard dichromatic reflection model for dielectric materials and the extended dichromatic reflection model for metals. We proof that the invariant formulas for spectral images of natural objects preserve spectral information and are invariant to Highlights, shading, surface geometry, and illumination intensity. It is proved that the conventional spectral invariant technique can be applied to metals in addition to dielectric objects. Experimental results show that the proposed spectral invariant representation is effective for image segmentation.
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ICPR - Spectral Invariant Representation for Spectral Reflectance Image
2010 20th International Conference on Pattern Recognition, 2010Co-Authors: Abdelhameed Ibrahim, Shoji Tominaga, Takahiko HoriuchiAbstract:Although spectral images contain large amount of information, compared with color images, the image acquisition is affected by several factors such as shading and Specular Highlight. Many researchers have introduced color invariant and spectral invariant representations for these factors using the standard dichromatic reflection model of inhomogeneous dielectric materials. However, these representations are inadequate for other materials like metal. This paper proposes a more general spectral invariant representation for obtaining reliable spectral reflectance images. Our invariant representation is derived from the standard dichromatic reflection model for dielectric materials and the extended dichromatic reflection model for metals. We proof the invariant formulas for spectral images of most natural objects preserve spectral information and are invariant to Highlights, shading, surface geometry, and illumination intensity. The method is applied to the problem of material classification and image segmentation of a raw circuit board. Experiments are done with real spectral images to examine the performance of the proposed method.
Elli Angelopoulou - One of the best experts on this subject based on the ideXlab platform.
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Specular Highlight detection based on the fresnel reflection coefficient
International Conference on Computer Vision, 2007Co-Authors: Elli AngelopoulouAbstract:Reliable Specularity detection can affect the accuracy of further image analysis. The majority of Specularity detection algorithms are based on the chromaticity of the regions of Specular Highlights. They assume that the color of Specular Highlights of dielectrics is approximately the color of the incident light. We will show how this assumption is inaccurate especially in multispectral images. We propose a new, physics-based Specularity detection method, which depends on the Fresnel term of the Specular Highlight, instead of assumptions on chromaticity space. We compute at each pixel an approximation to the Fresnel term at various wavelengths. We then use mean-shift analysis to segment the image based on the Fresnel information. Our experiments with multispectral, as well as traditional RGB images, show improved Specularity detection and higher robustness in chromaticity space noise.
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ICCV - Specular Highlight Detection Based on the Fresnel Reflection Coefficient
2007 IEEE 11th International Conference on Computer Vision, 2007Co-Authors: Elli AngelopoulouAbstract:Reliable Specularity detection can affect the accuracy of further image analysis. The majority of Specularity detection algorithms are based on the chromaticity of the regions of Specular Highlights. They assume that the color of Specular Highlights of dielectrics is approximately the color of the incident light. We will show how this assumption is inaccurate especially in multispectral images. We propose a new, physics-based Specularity detection method, which depends on the Fresnel term of the Specular Highlight, instead of assumptions on chromaticity space. We compute at each pixel an approximation to the Fresnel term at various wavelengths. We then use mean-shift analysis to segment the image based on the Fresnel information. Our experiments with multispectral, as well as traditional RGB images, show improved Specularity detection and higher robustness in chromaticity space noise.
