Computational Color Imaging: 4th International Workshop, by Hidehiko Komatsu, Akiko Nishio, Gouki Okazawa, Naokazu Goda

By Hidehiko Komatsu, Akiko Nishio, Gouki Okazawa, Naokazu Goda (auth.), Shoji Tominaga, Raimondo Schettini, Alain Trémeau (eds.)

This ebook constitutes the refereed lawsuits of the 4th Computational colour Imaging Workshop, CCIW 2013, held in Chiba, Japan, in March 2013. The 21 revised complete papers, provided including four invited papers, have been conscientiously reviewed and chosen from a variety of submissions. The papers are equipped in topical sections on colour picture notion; colour blend; multi-spectral photograph research and rendering; colour photograph detection and type; colour picture gains; and colour snapshot filtering and enhancement.

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Such implementations show the possibilities of computational spectral imaging. The majority of the systems that use adaptive spectral imaging by either modulating the optical proprieties of the filters or by using active illumination [7] are bulky and not portable. One way to miniaturize a reconfigurable spectral imaging device is by exploring imaging sensors with tunable spectral sensitivities. 4 Efficient Adaptive Spectral Imaging By using a tunable spectral imaging sensor, it is possible to build a reconfigurable spectral imaging system that adapts to the content of the scene, increasing capture efficiency.

Left) Graphic representation of which areas of the image corresponds to which filter mode for a fruit scene (Right) A graphic representation of the spatial distribution of filter mode selection is exemplified in Figure 11. It is possible to inspect visually that the selected filter modes correlate well with the color of the objects in the scene. 4 47 Discussions on Tunable Imaging Sensors Computational spectral imaging can mitigate spectral imaging issues such as robustness of spectral estimation transform especially the dependency on training sample set since the imaging system itself is able to reconfigure to the spectral characteristics of the capturing scene.

The reflectance prroperties of the surfaces were defined using the Ward liight reflection model that was then t used in conjunction with the spectrally and spatiaally characterized model of thee booth luminaires to produce the renderings shown. N Note that the hues of the neutral surfaces change appropriately for the different illuminaants, and that the reflection patteerns in the surfaces are correct for the patterns of lights in the different luminaires. Allthough it cannot be shown in still images, because of the system’s IR tracker, the loccations of the surface highlights also change appropriattely with observer movement.

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