Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Transferring color to greyscale images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
IEEE Computer Graphics and Applications
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Estimation of Color for Gray-level Image by Probabilistic Relaxation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Segmentation of black and white cartoons
SCCG '03 Proceedings of the 19th spring conference on Computer graphics
Colorization using optimization
ACM SIGGRAPH 2004 Papers
An improved watershed algorithm based on efficient computation of shortest paths
Pattern Recognition
Key frame selection by motion analysis
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
A fast motion estimation algorithm equivalent to exhaustive search
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
Fast image and video colorization using chrominance blending
IEEE Transactions on Image Processing
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Colorization is a computer-aided process of adding color to a grayscale image or video. The task of colorizing a grayscale image involves assigning three dimensional (RGB) pixel values to an image which varies along only one dimension (luminance or intensity). Since different colors may have the same luminance value but vary in hue or saturation, mapping between intensity and color is not unique, and colorization is ambiguous in nature, requiring some amount of human interaction or external information. In this paper we propose a semi-automatic process for colorization where the user indicates how each region should be colored by putting the desired color marker in the interior of the region. The algorithm based on the position and color of the markers, segments the image and colors it. In order to colorize videos, few reference frames are chosen manually from a set of automatically generated key frames and colorized using the above marker approach and their chrominance information is then transferred to the other frames in the video using a color transfer technique making use of motion estimation. The colorization results obtained are visually very good. In addition the amount of manual intervention is reduced since the user only has to apply color markers on few selected reference frames and the proposed algorithm colors the entire video sequence.