SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
IEEE Computer Graphics and Applications
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Time-Of-Flight Depth Sensor - System Description, Issues and Solutions
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 3 - Volume 03
Ensuring Color Consistency across Multiple Cameras
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Generation of high-quality depth maps using hybrid camera system for 3-D video
Journal of Visual Communication and Image Representation
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
New color correction approach to multi-view images with region correspondence
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Multiview Video Coding Using View Interpolation and Color Correction
IEEE Transactions on Circuits and Systems for Video Technology
Histogram-Based Prefiltering for Luminance and Chrominance Compensation of Multiview Video
IEEE Transactions on Circuits and Systems for Video Technology
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In order to capture 3D scenes, a multi-view camera consisting of two or more cameras is widely used; however, color consistency among views is not guaranteed in many situations. In this paper, we design relative mapping curves with consideration of the properties of luminance and chrominance components to improve the consistency. The input images are categorized into source and reference views. We convert their color domain to the YUV color space, and estimate coefficients in the mapping curves by analyzing correspondences between the two views. After that, we generate lookup tables and convert the color distributions of the source views. From the experimental results, we confirm that our proposed method improves the visual quality of multi-view images and reduces Euclidean distances in the CIELab color space among views.