Shoji Tominaga

Multichannel vision system for estimating surface and illumination functions

This paper describes a set of experimental measurements and theoretical calculations designed to recover both the surface-spectral reflectance function and the illuminant spectral-power distribution from the image data. A multichannel vision system comprising six color channels was created with the use of a monochrome CCD camera and color filters. The spectral sensitivity of each color channel is calibrated, and the dynamic range of the camera is extended for sensing a wide range of intensity levels. Three algorithms and the corresponding results are introduced. First, a method of choosing the appropriate dimension of the linear model dimensions is introduced. Second, the illuminant parameters are estimated from the sensor measurements made at multiple points within separate objects. Third, the sensor responses are corrected for highlight and shading variations. The body reflectance parameters, unique to each surface, are recovered from these corrected values. Experimental results with a small number of test surfaces and a simple illumination geometry demonstrate that the illuminant spectrum and the surface-spectral reflectance functions can be recovered to within typical deviations of 1% and 4%, respectively.

Surface identification using the dichromatic reflection model

The author describes a method based on the dichromatic reflection model for identifying object surfaces. The surface spectral reflectance function of an inhomogeneous object is described as the sum of a constant interface (specular) reflectance and a body (diffuse) reflectance under all illumination geometries. The interface component is used to estimate the spectral power distribution of the illuminant without using a reference white standard, whereas the body component is used as the principal indication of the surface identity. The body reflectance function of each surface is recovered. A method to classify the observed reflectances is developed, and an algorithm to estimate a body reflectance function, unique to each surface, from the classified reflectances is proposed. The author shows the reliability of the surface classification method and the accuracy of estimated body reflectance function. >

Spectral imaging by a multichannel camera

A set of multi-channel camera systems and algorithms is described for recovering both the surface spectral- reflectance function and the illuminant spectral-power distribution from the data of spectral imaging. We show the camera system with six spectral channels of fixed wavelength bands. This system is created by using a monochrome CCD camera, six different color filters, and a personal computer. The dynamic range of the camera is extended for sensing the high intensity level of highlights. We suppose in a scene that the object surface of an inhomogeneous dielectric material is described by the dichromatic reflection model. The process for estimating the spectral information is composed of several steps of (1) the finite- dimensional linear model representation of wavelength functions, (2) illuminant estimation, (3) data normalization and image segmentation, (4) reflectance estimation. The reliability of the camera system and the algorithms is demonstrated in an experiment. Finally a new type of system using liquid crystal filters is briefly introduced.

Estimating reflection parameters from a single color image

This article proposes a method for estimating various parameters of a reflection model from a single color image of an object. We used an RGB charge-coupled device (CCD) camera for imaging and parameter estimation. We assumed the object surface is composed of an inhomogeneous dielectric material and used the Phong model to describe the surfaces dichromatic reflection. This approach also generalizes to objects with smooth convex surfaces.

Scene illuminant classification: brighter is better

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 algorithms classification performance with respect to scene illuminant blackbody color temperature are tested and confirmed by using the natural-image data set.

Component estimation of surface spectral reflectance

For inhomogeneous materials, the standard reflectance model suggests that under all viewing geometries surface reflectance functions can be described as the sum of a constant function of wavelength (specular) and a diffuse function that is characteristic of the material. As the viewing geometry varies, the relative contribution of these two terms varies. In a previous study [ J. Opt. Soc. Am. A6, 576 ( 1989)] we described how to use light reflected from inhomogeneous materials, measured in different viewing geometries, to estimate the relative spectral power distribution of the ambient light. Here we show that two restrictions, that (a) surface reflectance functions are all nonnegative and (b) surface reflectance functions are the positive weighted sum of subsurface (diffuse) and interface (specular) components, may be used to estimate the subsurface component of the surface reflectance function. A band of surface spectral reflectances is recovered, as possible solutions for the subsurface estimates.

A color classification method for color images using a uniform color space

A color classification method that partitions color image data into a set of uniform color regions is described. The ability to classify spatial regions of the measured image into a small number of uniform regions can be useful for several problems, including image segmentation and image representation. The input image data are first mapped from device coordinates into all approximately uniform perceptual color space. Colors are classified by means of cluster detection in the uniform color space. The classification process is composed of two stages of basic classification and reclassification. The basic classification is based on histogram analysis to detect color clusters sequentially. The principal components of the color data are extracted for effective discrimination of clusters. At the reclassification stage, the extracted representative colors are reclassified on a color distance. Experimental results show that a fundamental set of colors composing an image with shades and shadows is extracted at the basic classification stage and that the objects in the original image are extracted at the reclassification stage.<<ETX>>

Color in image and video processing: most recent trends and future research directions

The motivation of this paper is to provide an overview of the most recent trends and of the future research directions in color image and video processing. Rather than covering all aspects of the domain this survey covers issues related to the most active research areas in the last two years. It presents the most recent trends as well as the state-of-the-art, with a broad survey of the relevant literature, in the main active research areas in color imaging. It also focuses on the most promising research areas in color imaging science. This survey gives an overview about the issues, controversies, and problems of color image science. It focuses on human color vision, perception, and interpretation. It focuses also on acquisition systems, consumer imaging applications, and medical imaging applications. Next it gives a brief overview about the solutions, recommendations, most recent trends, and future trends of color image science. It focuses on color space, appearance models, color difference metrics, and color saliency. It focuses also on color features, color-based object tracking, scene illuminant estimation and color constancy, quality assessment and fidelity assessment, color characterization and calibration of a display device. It focuses on quantization, filtering and enhancement, segmentation, coding and compression, watermarking, and lastly on multispectral color image processing. Lastly, it addresses the research areas which still need addressing and which are the next and future perspectives of color in image and video processing.

Computational Color Imaging

The present paper discusses the concept of subtractive color mixing widely used in color hardcopy applications and shows that a more realistic concept would be “spectral mixing”: the physical description of the coloration of light by printed surfaces comes from the mixing of light components selectively absorbed by inks or dyes during their patch within the printing materials. Some classical reflectance equations for continuous tone and halftone prints are reviewed and considered as spectral mixing laws. The challenge of extending these models to new inkless printing processes based on laser radiation is also addressed.

Polarization imaging for material classification

A simple and stable method is proposed for distinguishing dielectric and metal material surfaces from the polarization images captured by a vision system consisting of a linear polarizer and a digital camera. The polarization state is determined by the transmitted light intensity through the polarizer as a function of polarizing orientation. The degree of polarization (DOP) is estimated from the image intensities through the polarizer. The DOP map is quite effective for material classification around specular highlight on an object surface. We prove that the DOP map is convex for a dielectric surface and concave for a metal surface. The problem of material classification is then reduced to a simple judgment of the convexity of the DOP map obtained around the highlight peak. The proposed method is not a pixelwise local method based on thresholding the Fresnel ratio computed at each pixel but an area-based method based on the DOP map in a highlight area. The feasibility of the method is confirmed in experiments under a variety of conditions.