Mobile phone cameras are often significantly more useful than professional digital single-lens reflex (DSLR) cameras. Knowledge of the camera spectral sensitivity function is important in many fields that make use of images. In this study, methods for measuring and estimating spectral sensitivity functions for mobile phone cameras are developed. In the direct measurement method, the spectral sensitivity at each wavelength is measured using monochromatic light. Although accurate, this method is time-consuming and expensive. The indirect estimation method is based on color samples, in which the spectral sensitivities are estimated from the input data of color samples and the corresponding output RGB values from the camera. We first present an imaging system for direct measurements. A variety of mobile phone cameras are measured using the system to create a database of spectral sensitivity functions. The features of the measured spectral sensitivity functions are then studied using principal component analysis (PCA) and the statistical features of the spectral functions extracted. We next describe a normal method to estimate the spectral sensitivity functions using color samples and point out some drawbacks of the method. A method to solve the estimation problem using the spectral features of the sensitivity functions in addition to the color samples is then proposed. The estimation is stable even when only a small number of spectral features are selected. Finally, the results of the experiments to confirm the feasibility of the proposed method are presented. We establish that our method is excellent in terms of both the data volume of color samples required and the estimation accuracy of the spectral sensitivity functions.

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.

A set of multichannel 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 describe a camera system with six spectral channels of fixed wavelength bands. This system is composed of a monochrome charge-coupled device camera, six different color filters, and a personal computer. The dynamic range of the camera is extended for sensing the high-intensity levels of highlights. We assume that the object surfaces in a scene are made of an inhomogeneous dielectric material whose reflection properties are described by the dichromatic reflection model. The process for estimating the spectral data comprises several steps: (1) finitedimensional linear model representation of wavelength functions, (2) illuminant estimation, (3) data normalization and image segmentation, and (4) reflectance estimation. An algorithm is proposed for detecting highlight areas in the image. The reliability of the camera system and the algorithms is demonstrated in an experiment. Finally, a new type of camera system using a liquid-crystal filter is proposed.

We propose a spectral imaging method for material classification and inspection of raw printed circuit boards (PCBs). The method is composed of two steps (1) estimation the PCB surface-spectral reflectances and (2) unsupervised classification of the reflectance data to make the inspection of PCB easy and efficient. First, we develop a spectral imaging system that captures high dynamic range images of a raw PCB with spatially and spectrally high resolutions in the region of visible wavelength. The surface-spectral reflectance is then estimated at every pixel point from multiple spectral images, based on the reflection characteristics of different materials. Second, the surface-spectral reflectance data are classified into several groups, according to the number of PCB materials. We develop an unsupervised classification algorithm incorporating both spectral information and spatial information, based on the Nyström approximation of the normalized cut method. The initial seeds for the Nyström procedure are effectively chosen using a guidance module based on the K-means algorithm. Low-dimensional spectral features are efficiently extracted from the original high-dimensional spectral reflectance data. The feasibility of the proposed method is examined in experiments using real PCBs in detail.

We propose a technique for viewpoint and illumination-independent digital archiving of art paintings in which the painting surface is regarded as a 2-D rough surface with gloss and shading. Surface materials like oil paints are inhomogeneously dielectric with the dichromatic reflection property. The procedure for total digital archiving is divided into three main steps: acquisition, analysis, and rendering. In the first stage, we acquire images of a painting using a multiband imaging system with six spectral channels at different illumination directions. In the second stage, we estimate the surface properties of surface-spectral reflectance functions, surface normal vectors, and 3-D reflection model parameters. The principal component analysis suggests that the estimated spectral reflectances have the potential for high data compression. In the third stage, we combine all the estimates for rendering the painting under arbitrary illumination and viewing conditions. We confirm the feasibility of the proposed technique in experiments using oil paintings.

We propose an improved method for estimating surface-spectral reflectance from the image data acquired by an RGB digital camera. We suppose a multispectral image acquisition system in the visible range, where a camera captures multiple images for the scene of an object under multiple light sources. First, the observed image data are described using the camera spectral sensitivities, the surface-spectral reflectance, the illuminant spectral power distributions, an additive noise term, and a gain parameter. Then, the optimal reflectance estimate is determined to minimize the mean-square error between the estimate and the original surface-spectral reflectance. We attempt to further improve the estimation accuracy and develop a novel linear estimator in a more general form than the Wiener estimator. Furthermore, we calibrate the imaging system using a reference standard sample. Finally, experiments are performed to validate the proposed method for estimating the surface-spectral reflectance using different mobile phone cameras.