Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
A multiresolution flow-based multiphase image segmentation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Image and Video Colorization Using Vector-Valued Reproducing Kernel Hilbert Spaces
Journal of Mathematical Imaging and Vision
Unsupervised colour image segmentation using dual-tree complex wavelet transform
Computer Vision and Image Understanding
Multi-view video based multiple objects segmentation using graph cut and spatiotemporal projections
Journal of Visual Communication and Image Representation
Original paper: Real time feature extraction and Standard Cutting Models fitting in grape leaves
Computers and Electronics in Agriculture
Image-to-MIDI mapping based on dynamic fuzzy color segmentation for visually impaired people
Pattern Recognition Letters
Color transfer using scattered point interpolation
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
Color image segmentation using tensor voting based color clustering
Pattern Recognition Letters
Color transfer based on multiscale gradient-aware decomposition and color distribution mapping
Proceedings of the 20th ACM international conference on Multimedia
Shape priors extraction and application for geodesic distance transforms in images and videos
Pattern Recognition Letters
A framework for interactive image color editing
The Visual Computer: International Journal of Computer Graphics
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We propose an automatic approach to soft color segmentation, which produces soft color segments with an appropriate amount of overlapping and transparency essential to synthesizing natural images for a wide range of image-based applications. Although many state-of-the-art and complex techniques are excellent at partitioning an input image to facilitate deriving a semantic description of the scene, to achieve seamless image synthesis, we advocate a segmentation approach designed to maintain spatial and color coherence among soft segments while preserving discontinuities by assigning to each pixel a set of soft labels corresponding to their respective color distributions. We optimize a global objective function, which simultaneously exploits the reliability given by global color statistics and flexibility of local image compositing, leading to an image model where the global color statistics of an image is represented by a Gaussian mixture model (GMM), whereas the color of a pixel is explained by a local color mixture model where the weights are defined by the soft labels to the elements of the converged GMM. Transparency is naturally introduced in our probabilistic framework, which infers an optimal mixture of colors at an image pixel. To adequately consider global and local information in the same framework, an alternating optimization scheme is proposed to iteratively solve for the global and local model parameters. Our method is fully automatic and is shown to converge to a good optimal solution. We perform extensive evaluation and comparison and demonstrate that our method achieves good image synthesis results for image-based applications such as image matting, color transfer, image deblurring, and image colorization.