Get Computational Color Imaging: 4th International Workshop, PDF

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

ISBN-10: 3642366996

ISBN-13: 9783642366994

ISBN-10: 3642367003

ISBN-13: 9783642367007

This e-book 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, awarded including four invited papers, have been conscientiously reviewed and chosen from a number of submissions. The papers are geared up in topical sections on colour photo belief; colour mix; multi-spectral snapshot research and rendering; colour photograph detection and type; colour photo good points; and colour photo filtering and enhancement.

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In: Pashler, H. ) Attention, Psychology Press Ltd. (1998) 23. : What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience 5, 1–7 (2004) 24. : A divisive information-theoretic feature clustering algorithm for text classification. Journal of Machine Learning Research (JMLR) 3, 1265–1287 (2003) 25. : Top-Down Cues for Event Recognition. , Sugimoto, A. ) ACCV 2010, Part III. LNCS, vol. 6494, pp. 691–702. Springer, Heidelberg (2011) 26. : Discriminative compact pyramids for object and scene recognition.

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. Specific spectral bands are more appropriate for certain reflectances of the scene. For example, if a region of the scene is predominantly red, then it is more appropriate to have pixel sensitivities absorb more red light.

For simplicity, the entire training set was used to produce a calibration matrix via the pseudo-inverse method. See Figure 4 for a surface plot of an example calibration matrix. This calibration matrix shows diagonal correlation, exhibiting expected characteristics. H. Imai Fig. 4. 2 Filter Mode Selection As expected, the filter modes successfully divide the scene into different regions, depending on dominant transition location. The number of filter modes used is very important. As shown in Figure 5, using as little as 2 filter modes will most likely be sufficient for the tunable sensor spectral imaging system.

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Computational Color Imaging: 4th International Workshop, CCIW 2013, Chiba, Japan, March 3-5, 2013. Proceedings by Hidehiko Komatsu, Akiko Nishio, Gouki Okazawa, Naokazu Goda (auth.), Shoji Tominaga, Raimondo Schettini, Alain Trémeau (eds.)


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