Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relaxation Methods for Supervised Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
IEEE Computer Graphics and Applications
Adjustment Learning and Relevant Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Unsupervised colorization of black-and-white cartoons
Proceedings of the 3rd international symposium on Non-photorealistic animation and rendering
Colorization using optimization
ACM SIGGRAPH 2004 Papers
Color transfer in correlated color space
Proceedings of the 2006 ACM international conference on Virtual reality continuum and its applications
ACM SIGGRAPH 2006 Papers
ACM SIGGRAPH 2006 Papers
A Spectral Color Analysis and Colorization Technique
IEEE Computer Graphics and Applications
AppWand: editing measured materials using appearance-driven optimization
ACM SIGGRAPH 2007 papers
Proceedings of the 15th international conference on Multimedia
AppProp: all-pairs appearance-space edit propagation
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH Asia 2008 papers
Structure-Preserving Colorization Based on Quaternionic Phase Reconstruction
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Image and Video Colorization Using Vector-Valued Reproducing Kernel Hilbert Spaces
Journal of Mathematical Imaging and Vision
Semantic colorization with internet images
Proceedings of the 2011 SIGGRAPH Asia Conference
Object-Based image recoloring using alpha matte and color histogram specification
VSMM'06 Proceedings of the 12th international conference on Interactive Technologies and Sociotechnical Systems
Colorization using quaternion algebra with automatic scribble generation
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Computer Graphics Forum
Automatic grayscale image colorization using histogram regression
Pattern Recognition Letters
Spectralization: reconstructing spectra from sparse data
EGSR'10 Proceedings of the 21st Eurographics conference on Rendering
EGSR'07 Proceedings of the 18th Eurographics conference on Rendering Techniques
Image colorization using similar images
Proceedings of the 20th ACM international conference on Multimedia
Image colorization with an affective word
CVM'12 Proceedings of the First international conference on Computational Visual Media
Image recoloring using linear template mapping
Multimedia Tools and Applications
Non-photorealistic rendering with spot colour
Proceedings of the Symposium on Computational Aesthetics
Data-driven hallucination of different times of day from a single outdoor photo
ACM Transactions on Graphics (TOG)
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We present a new method for colorizing grayscale images by transferring color from a segmented example image. Rather than relying on a series of independent pixel-level decisions, we develop a new strategy that attempts to account for the higher-level context of each pixel. The colorizations generated by our approach exhibit a much higher degree of spatial consistency, compared to previous automatic color transfer methods [WAM02]. We also demonstrate that our method requires considerably less manual effort than previous user-assisted colorization methods [LLW04]. Given a grayscale image to colorize, we first determine for each pixel which example segment it should learn its color from. This is done automatically using a robust supervised classification scheme that analyzes the low-level feature space defined by small neighborhoods of pixels in the example image. Next, each pixel is assigned a color from the appropriate region using a neighborhood matching metric, combined with spatial filtering for improved spatial coherence. Each color assignment is associated with a confidence value, and pixels with a sufficiently high confidence level are provided as "micro-scribbles" to the optimization-based colorization algorithm of Levin et al. [LLW04], which produces the final complete colorization of the image.