Many vision tasks such as scene segmentation, or the recognition of materials within a scene, become considerably easier when it is possible to measure the spectral reflectance of scene surfaces. In this paper, we present an efficient and robust approach for recovering spectral reflectance in a scene that combines the advantages of using multiple spectral sources and a multispectral camera. We have implemented a system based on this approach using a cluster of light sources with different spectra to illuminate the scene and a conventional RGB camera to acquire images. Rather than sequentially activating the sources, we have developed a novel technique to determine the optimal multiplexing sequence of spectral sources so as to minimize the number of acquired images. We use our recovered spectral measurements to recover the continuous spectral reflectance for each scene point by using a linear model for spectral reflectance. Our imaging system can produce multispectral videos of scenes at 30fps. We demonstrate the effectiveness of our system through extensive evaluation. As a demonstration, we present the results of applying data recovered by our system to material segmentation and spectral relighting.

Color differences are determined by illumination, the spectral reflectance of objects, and the spectral sensitivity of the imaging sensor. We explore the optimal illumination conditions that best separate one object from another. Given two objects with distinct spectra, we derive the optimal illumination spectrum to maximize their color distance with a plain RGB camera. In practice, it is crucial to compose the most appropriate illuminations using available lighting sources, since creating an arbitrary illumination spectrum is unrealistic. Therefore, we derive the optimal linear combination of the provided illumination sources. Finally, we verify the effectiveness of the methods through experiments.

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In direct-projected augmented reality, use of projector makes it possible to utilize 3-D real and large objects as displays and frees from discomforts incidental to wearing a device such as HMD. However, the resulting augmentation usually has poor depth resolution due to projectors with low contrast. In direct-projected augmented reality, the radiometric compensation, which is originally employed to recover the color properties of the projector input image in direct-projected augmented reality, seems to be another cause of decreasing the final contrast of the augmentation. In this paper, a contrast enhancement method is proposed that combines a hue-and saturation-preserving histogram equalization with the radiometric compensation in directprojected augmented reality. The method guarantees that the radiometrically compensated color is maintained while the brightness (or intensity) contrast is enhanced. Experimental results demonstrate the validity of our method.

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