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Titlebook: Computational Color Imaging; 7th International Wo Shoji Tominaga,Raimondo Schettini,Takahiko Horiuch Conference proceedings 2019 Springer N

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發(fā)表于 2025-3-21 17:05:39 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computational Color Imaging
副標(biāo)題7th International Wo
編輯Shoji Tominaga,Raimondo Schettini,Takahiko Horiuch
視頻videohttp://file.papertrans.cn/233/232200/232200.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Color Imaging; 7th International Wo Shoji Tominaga,Raimondo Schettini,Takahiko Horiuch Conference proceedings 2019 Springer N
描述.This book constitutes the refereed proceedings of the 7th Computational Color Imaging Workshop, CCIW 2019, held in Chiba, Japan, in March 2019..The 22 full papers presented in this volume were carefully reviewed and selected from 34 submissions. The papers are organized in topical sections named: computational color imaging; multispectral imaging; perceptual model and application; color image evaluation; colot image filtering; color image applications; and color imaging for material appearance. In addition, the book contains 3 invited talks in full paper length.?.
出版日期Conference proceedings 2019
關(guān)鍵詞artificial intelligence; color image processing; computer networks; image coding; image processing; image
版次1
doihttps://doi.org/10.1007/978-3-030-13940-7
isbn_softcover978-3-030-13939-1
isbn_ebook978-3-030-13940-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

書目名稱Computational Color Imaging影響因子(影響力)




書目名稱Computational Color Imaging影響因子(影響力)學(xué)科排名




書目名稱Computational Color Imaging網(wǎng)絡(luò)公開度




書目名稱Computational Color Imaging網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computational Color Imaging被引頻次




書目名稱Computational Color Imaging被引頻次學(xué)科排名




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書目名稱Computational Color Imaging年度引用學(xué)科排名




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書目名稱Computational Color Imaging讀者反饋學(xué)科排名




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https://doi.org/10.1057/978-1-137-49994-3rmed the estimation accuracy by using achromatic color patches. The results showed that average root mean square error (RMSE) for the spectral color estimation of six color patches was 0.035 in the range in which perfect reflection is defined as 1. We also generated spectral images from 12 band imag
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Contemporary Performance InterActionsbest color theme is extracted by using a supervised method based on a regression model trained on user-defined color themes and that the computational metric adopted is comparable to a subjective one.
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Louise Ladouceur,Nicole Noletteears, and at the same time obtained psychological features of appearance. As physical features, melanin and hemoglobin pigmentation distributions, shading and the frequency of UV care for 12 years were obtained. Subjective evaluation values were acquired as psychological features. As a result of CCA
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Improving Generalization Ability of Deep Neural Networks for Visual Recognition Tasksmultiple different types of image distortion, such as noise, defocus/motion blur, rain-streaks, raindrops, haze etc. We first introduce our recent study of architectural design of CNNs for image restoration targeting at a single, identified type of distortion. We then introduce another study, which
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Computational Imaging in Projection Mappingesh rate, and depth-of-field. I also covers an emerging issue in the projection mapping research, which is dynamic projection mapping. This article is written by reorganizing a previously published state-of-the-art report paper by the same author?[.] for an invited talk at the IAPR Computational Col
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Acquisition of 3D Data and Spectral Color by Using RGBD Camera and Programmable Light Sourcermed the estimation accuracy by using achromatic color patches. The results showed that average root mean square error (RMSE) for the spectral color estimation of six color patches was 0.035 in the range in which perfect reflection is defined as 1. We also generated spectral images from 12 band imag
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