Graphical Models and Image Processing
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Selection of the optimal prototype subset for 1-NN classification
Pattern Recognition Letters
Transferring color to greyscale images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
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
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Colorization using optimization
ACM SIGGRAPH 2004 Papers
Groves decipherment from space photos using prototype matching
Pattern Recognition Letters - Special issue: Pattern recognition for remote sensing (PRRS 2002)
Optimization based grayscale image colorization
Pattern Recognition Letters
Selective color transfer with multi-source images
Pattern Recognition Letters
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This paper introduces a new approach to colorization of grayscale aerial or space imagery. Proposed method is based on a simple premise: similar textures should have similar color distributions. This premise is formalized by using Bayesian texture classification of grayscale destination imagery with a set of textural prototypes or swatches from source color imagery. Only 2D distributions of chrominance channels are transferred and original luminance value is retained. A special segmentation technique is proposed in order to increase the performance of the algorithm. Examples on real images show high quality of colorization neither without using low-resolution color imagery nor without any user intervention. Appropriate prototype selection enables modeling of season changes of vegetation. It is shown that the proposed approach can be successfully applied to color infrared (CIR) imagery transform into visible RGB colors and for color image remapping.