We propose a practical spectral image rendering method for fluorescent objects using a renderer that does not natively support wavelength-shifting transport. For scenes composed of matte fluorescent and non-fluorescent surfaces, the illumination incident on a target fluorescent object is classified into three types: (1) direct illumination from a light source, (2) indirect illumination reflected from other reflective objects, and (3) luminescent illumination emitted from other fluorescent objects. We express the radiance observed on a fluorescent surface as a linear combination of five terms: reflection and fluorescence under direct illumination, reflection and fluorescence under indirect illumination, and reflection induced by luminescent illumination emitted by other fluorescent surfaces. Assuming isotropic emission and single-bounce fluorescence, the fluorescent shading terms reuse the diffuse-reflection shading produced by the renderer, while measured Donaldson matrices provide the wavelength conversion. Mitsuba is used as the underlying conventional rendering system. Experiments conducted with a physically built Cornell Box validate the proposed method through comparisons with direct measurements and an existing fluorescence-capable renderer. Finally, we demonstrate an extension to non-planar fluorescent objects via sparse emitter discretization.

Omni directional spectral imaging technology is crucial in many fields including computer vision and image analysis. It should be noted that the dynamic range of luminance values in natural scenes is quite large, and the scenes are time-varying since objects and light sources change dynamically. This paper proposes a system for acquiring high dynamic range (HDR) spectral images for time-varying Omni directional natural scenes. The system is constructed using two programmable high-speed video cameras with wide angle lenses and a programmable rotating table. Two different types of color filters are mounted on the two color video cameras for six band image acquisition. We present several algorithms for HDR image synthesis, lens distortion correction, image registration, and Omni directional image synthesis. Spectral power distributions of illuminants (color signals) are recovered from the captured six-band images based on the Wiener estimation algorithm. The performances of the proposed system are discussed in detail in terms of the system configurations, acquisition time, artifacts, and spectral estimation accuracy. Experimental results in actual scenes show that the proposed system is feasible and powerful for acquiring HDR spectral images of time-varying Omni directional scenes.

In this paper we propose a novel method for predicting resident's behaviors in a house from one's movement trajectories. The method consists of 1) segmentation of trajectory data into staying or moving and classification of the segments and 2) prediction by time-series association rules from transition events of each segment. The method predicts the start time of target behaviors for daily life support, such as eating, taking a bath etc. The time lag between the prediction and the target behavior can be set up manually, thus the method is adaptable to a variety of supporting systems. The experimental results using real residents' trajectory data of almost two years demonstrate that prediction of behaviors by the proposed method is feasible.

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.

This paper proposes an accurate reversible algorithm to convert full color images to gray images with keeping chroma and spatial resolution. In our previous study, we proposed a reversible color-to-gray algorithm using the two-level Haar wavelet transform with a packet assignment technique. The authors improve the algorithm by devising a color embedding technique. The proposed algorithm distributes color information effectively in the one-level wavelet subbands for preserving chroma and spatial resolution. Experimental results show that the proposed algorithm can improve the recovering accuracy of color images from the textured gray images of the original color images. We also present a practical application to a color-shielded image transmission which can permit a color recovery only to a specific user.

Material classification of object surfaces from captured image data is an essential problem in computer vision. The present paper proposes a method for stably classifying the material of an object surface based on the distribution of the degree of polarization (DOP) around specular highlight on the surface. We show that the characteristic of spatial distribution for DOP is remarkably different in metal and dielectric substance. Based on the characteristic, an algorithm is presented for effectively classifying any object surface into two material categories from images captured by a digital camera with rotating a polarization filter. The feasibility of the proposed method is verified on experiments using a variety of object surfaces made of different materials.

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.

A method is proposed for estimating various reflection parameters from images of an object taken by a multi-band CCD camera. The Torrance-Sparrow model is used for modeling light reflection on an object surface composed of inhomogeneous dielectric materials. First, we examine the validity of the Torrance-Sparrow model based on the spectral reflectance data. Next, we propose algorithms for estimating model parameters from images by a six-color camera. The parameters estimated from a single image are: 1) surface-spectral reflectance, 2) illuminant spectral distribution, 3) illumination direction, 4) surface roughness, and 5) ratio of body to interface intensity. The feasibility of the proposed method is demonstrated in an experiment using a painted object. The estimation accuracy of the whole model is confirmed based on computer graphics images. The method can be applied to objects with general shapes.

We describe a spectral-imaging system with a liquid-crystal tunable filter and algorithms for recovering the spectral functions of the illuminant and surface reflectance from the image data. The filter works as a polarization-interference filter with birefringent elements and linear polarizers. We define the camera outputs by a vector equation and describe the spectral functions by a linear model. Then algorithms are presented for estimating the spectral functions from the image data. An effective image-processing algorithm is proposed for highlight extraction and region segmentation. Experimental estimation of the illuminant and spectral reflectances shows the efficiency in measurement and the accuracy in estimation of the spectral functions.

Knowledge of such spectral functions as the surface-spectral reflectances of objects in a scene and the illuminant spectral-power distribution is crucial for realizing color constancy, accurate color reproduction, and rendering realistic images. The usual camera system with three channels of RGB has difficulty in estimating the spectral functions because illuminants and surface reflectances in natural scenes are spectrally high-dimensional. A multi-channel camera with more spectral channels than three provides an effective imaging device to estimate the complicated surface and illuminant functions. The present paper proposes a camera system and algorithms for solving this estimation problem from the spectral imaging data. First we present a new multichannel camera system with six spectral channels, which is improved in the dynamic range and image resolution. Then we describe practical algorithms for recovering both the surface spectral reflectances and the illuminant spectrum with accuracy.

In the last decade, many sensor-based navigation algorithms have been proposed. In the sensor-based navigation, a robot arrives at its goal globally while avoiding neighbor obstacles locally by sensor information. In every environment, a mobile robot arrives at its goal surely. However if an environment has complicated shape, a mobile robot sometimes joins a loop and consequently runs long until its goal. In general, a loop consists of routes which a robot follows obstacles in the same direction. Nevertheless in most previous algorithms, a mobile robot follows an encountered obstacle in a constant direction. On this observation, a robot is exempted from participation of a loop by reversing a direction to follow an obstacle. The authors discuss algorithms in which the following direction is alternatively changed. This strategy has been adopted by cockroaches living in a natural environment. They then propose algorithms in which the following direction is randomly reversed. Then they compare paths generated by constant, alternative, and random selections in Bug2 and Classl under a graphics simulator for 2D complicated environments.

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.>

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.>

A speech coding algorithm with low complexity and a short processing delay is introduced. The proposed algorithm is ADPCM (adaptive digital pulse code modulation) with a multiquantizer (ADPCM-MQ). The input signal is processed in parallel by multiple ADPCM coders with different characteristics. Then the optimum ADPCM coder with minimum error power is dynamically selected for each frame. A 16-kb/s codec based on this algorithm has been implemented using two general-purpose digital signal processors (MB8764) with 8.3 ms of total processing delay. A segmental SNR of 19-21 dB was achieved at 16 kb/s; with postfiltering the segmental SNR was increased to 23-25 dB. Combined with the time domain compression scheme, the algorithm can be easily applied to 8-kb/s coding. It is also extensible to variable-rate coding.>