THESIS
2014
xiv, 58 pages : illustrations ; 30 cm
Abstract
Digital image matting is a classical topic in computer vision society and has
a wide application in film production, graphic design and image editing societies.
Thus has aroused more and more attention recently. It basically refers to the
problem of extracting the region of interest such as a foreground object from an
image based on user inputs like scribbles or trimap. More specifically, we need
to accurately estimating the foreground color, background color, and an opacity
(or transparency) value of each pixel for an input image. Matting is an ill-posed
problem inherently since we need to output three images (foreground image,
background image and alpha matte) out of only one input image. Therefore,
in order to solve matting problem, various assumptions have been made to help...[
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Digital image matting is a classical topic in computer vision society and has
a wide application in film production, graphic design and image editing societies.
Thus has aroused more and more attention recently. It basically refers to the
problem of extracting the region of interest such as a foreground object from an
image based on user inputs like scribbles or trimap. More specifically, we need
to accurately estimating the foreground color, background color, and an opacity
(or transparency) value of each pixel for an input image. Matting is an ill-posed
problem inherently since we need to output three images (foreground image,
background image and alpha matte) out of only one input image. Therefore,
in order to solve matting problem, various assumptions have been made to help
constrain it, and based on different assumptions, lots of matting algorithms were
proposed. Nevertheless, natural image matting is still not an easy task for ideal
alpha matte generating.
There are three main methodological parts in the thesis. Firstly, in order to
gain more insights of matting problem, we start with a comprehensive survey and
analysis of the existing matting literature. We observe that there are three key
components in better estimating the alpha values, that is, the design of matting
laplacian matrix, the definition of neighborhood and the choices of feature space.
Based on this observation, we introduce a unified framework for digital image
matting, which provides the possibility of obtaining a better understanding and
direction of further improvement for image matting problem. The experimental
results tested on different matting algorithms further prove the feasibility of our
proposed framework.
Then, in the second part, inspired by closed-form matting and color clustering
matting, we firstly develop an adaptive sample clustering criterion to automatically
assign either local or nonlocal neighborhood to each pixel. After that,
in order to enhance matting accuracy, we improve the nonlocal clustering performance
by introducing a new feature selection parameter to choose preferred
feature space for different images in a fully automatic way. And finally we solve
the problem using a closed form solution. Experimental results show that our algorithm
achieves equal or even better performance among many state-of-the-art
matting techniques.
Apart from the hybrid method we adopted in the work proposed in the second
part of our methodology, we find that there are still possibility to achieve
better performance via a totally novel idea, that is, in all the conventional matting
literature, people use compositing equation to describe the relation between
original image and corresponding alpha matte. In contrast, in this part, we introduce
a novel feature based compositing equation which encodes not only color
information as in previous work but also coordinate information. Then, for the
purpose of better preserving the intrinsic nonlocal structure of natural images, we
propose an alpha-feature model based on the new compositing equation. Finally,
we derive our matting method with closed-form solution achieved by using feature
clustering ball model. Experimental results show that the proposed method
outperforms most state-of-the-art matting methods.
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