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

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

This e-book constitutes the refereed complaints of the 4th Computational colour Imaging Workshop, CCIW 2013, held in Chiba, Japan, in March 2013. The 21 revised complete papers, offered including four invited papers, have been conscientiously reviewed and chosen from quite a few submissions. The papers are prepared in topical sections on colour photograph conception; colour mix; multi-spectral snapshot research and rendering; colour photo detection and type; colour picture positive factors; and colour photograph filtering and enhancement.

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Often a parameter balancing the relative weight 28 J. van de Weijer and F. Shahbaz Khan Fig. 1. Early and late fusion schemes to combine color and shape information. The α and β parameters determine the relative weight of the two cues. of color and shape is introduced when concatenating the two histograms. This parameter is typically optimized with cross-validation. In the case of early fusion, the features of color and shape are concatenated before assignment to the vocabulary, after which the image representation is built with: Mi n wnsc |I i ∝ δ wijsc , wnsc (3) j=1 The final representation of the image is a single multi-cue histogram n wnsc |I i .

While the results are promising, there are additional experiments to be performed to further show the benefit of tunable sensors over traditional sensors for multispectral imaging. For example, the current set of data is still limited, and it is necessary to run more simulations with more datasets in the future. Moreover, while the proposed filter mode selection mechanism works reasonably well, other approaches for filter mode selection may be superior or more efficient. Currently, the filter modes are picked manually and do not consider width or height modulation of the curves.

In Fig. 4 examples of local regions attributed to the same Portmanteau word are shown. Fig. 4. Example of Portmanteau vocabulary. Each of the large boxes contains 100 image patches sampled from one Portmanteau word on the Oxford Flower-102 dataset. 32 J. van de Weijer and F. Shahbaz Khan As said, image representation constructed from Portmanteau vocabularies, possess cue compactness and cue binding. They also allow for cue weighting (see [13] for details). They scale relatively well with the number of categories: the number of words used in the final representation is a user input and compact image representations have been used for problems with up to two hundred classes [13].

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