IEEE Trans Neural Netw Learn Syst - Targeting accurate object extraction from an image: a comprehensive study of natural image matting.

Tópicos

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Resumo

With the development of digital multimedia technologies, image matting has gained increasing interests from both academic and industrial communities. The purpose of image matting is to precisely extract the foreground objects with arbitrary shapes from an image or a video frame for further editing. It is generally known that image matting is inherently an ill-posed problem because we need to output three images out of only one input image. In this paper, we provide a comprehensive survey of the existing image matting algorithms and evaluate their performance. In addition to the blue screen matting, we systematically divide all existing natural image matting methods into four categories: 1) color sampling-based; 2) propagation-based; 3) combination of sampling-based and propagation-based; and 4) learning-based approaches. Sampling-based methods assume that the foreground and background colors of an unknown pixel can be explicitly estimated by examining nearby pixels. Propagation-based methods are instead based on the assumption that foreground and background colors are locally smooth. Learning-based methods treat the matting process as a supervised or semisupervised learning problem. Via the learning process, users can construct a linear or nonlinear model between the alpha mattes and the image colors using a training set to estimate the alpha matte of an unknown pixel without any assumption about the characteristics of the testing image. With three benchmark data sets, the various matting algorithms are evaluated and compared using several metrics to demonstrate the strengths and weaknesses of each method both quantitatively and qualitatively. Finally, we conclude this paper by outlining the research trends and suggesting a number of promising directions for future development.

Resumo Limpo

develop digit multimedia technolog imag mat gain increas interest academ industri communiti purpos imag mat precis extract foreground object arbitrari shape imag video frame edit general known imag mat inher illpos problem need output three imag one input imag paper provid comprehens survey exist imag mat algorithm evalu perform addit blue screen mat systemat divid exist natur imag mat method four categori color samplingbas propagationbas combin samplingbas propagationbas learningbas approach samplingbas method assum foreground background color unknown pixel can explicit estim examin nearbi pixel propagationbas method instead base assumpt foreground background color local smooth learningbas method treat mat process supervis semisupervis learn problem via learn process user can construct linear nonlinear model alpha matt imag color use train set estim alpha matt unknown pixel without assumpt characterist test imag three benchmark data set various mat algorithm evalu compar use sever metric demonstr strength weak method quantit qualit final conclud paper outlin research trend suggest number promis direct futur develop

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