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

HEIR

书目名称Computational Color Imaging影响因子(影响力)




书目名称Computational Color Imaging影响因子(影响力)学科排名




书目名称Computational Color Imaging网络公开度




书目名称Computational Color Imaging网络公开度学科排名




书目名称Computational Color Imaging被引频次




书目名称Computational Color Imaging被引频次学科排名




书目名称Computational Color Imaging年度引用




书目名称Computational Color Imaging年度引用学科排名




书目名称Computational Color Imaging读者反馈




书目名称Computational Color Imaging读者反馈学科排名

epicardium

Chipmunk

莎草

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

Heretical

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.

腼腆

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

腼腆

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

Ibd810

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

Stricture

phytochemicals

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