ACM SIGGRAPH 2004 Papers
An Iterative Optimization Approach for Unified Image Segmentation and Matting
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft scissors: an interactive tool for realtime high quality matting
ACM SIGGRAPH 2007 papers
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Image matting is the process of estimating the foreground and background elements from a single image with limited user input. To solve this severely under-constrained problem, there exist various methods to construct color models for an image. Most previous color models can fail to estimate accurate mattes for complex images of nonlinear color distributions due to their simple color models. In this paper, we present a new dynamic curve color model for image matting that can handle nonlinear color distributions. We show that the colors of a local region can be fit to a curve when the local region includes three types of colors - foreground, background, and unknown mixed colors. Based on these colors in the local region, we adaptively construct a curve color model using a quadratic Bezier curve model. Our curve model allows the derivation of a new closed-form matting equation for estimating alpha values of colors forming a curve. We show that our method estimates alpha mattes more accurately than recent existing methods through visual and quantitative comparisons.