AutoKey: human assisted key extraction

T. Mitsunaga, Taku Yokoyama, T. Totsuka
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引用次数: 56

Abstract

Key extraction is an inverse problem of finding the foreground, the background, and the alpha from an image and some hints. Although the chromakey solves this for a limited case (single background color), this is often too restrictive in practical situations. When the extraction from arbitrary background is necessary, this is currently done by a time consuming manual task. In order to reduce the operator load, attempts have been made to assist operators using either color space or image space information. However, existing approaches have their limitations. Especially, they leave too much work to operators. In this paper, we present a key extraction algorithm which for the first time, addresses the problem quantitatively. We first derive a partial differential equation that relates the gradient of an image to the alpha values. We then describe an efficient algorithm that provides the alpha values as the solution of the equation. Along with our accurate motion estimation technique, it produces correct alpha values almost everywhere, leaving little work to operators. We also show that a careful design of the algorithm and the data representation greatly improves human interaction. At every step of the algorithm, human interaction is possible and it is intuitive. CR Categories: I.3.3 [Computer Graphics]: Picture / Image Generation; I.4.6 [Image Processing]: Segmentation Edge and feature detection; I.4.7 [Image Processing]: Feature Measurement; I.5.2 [Pattern Recognition]: Design Methodology Feature evaluation and selection. Additional
AutoKey:人工辅助密钥提取
关键提取是一个反向问题,从图像和一些提示中找到前景、背景和alpha。虽然chromakey在有限的情况下(单一背景颜色)解决了这个问题,但在实际情况下,这通常过于限制。当需要从任意背景中提取时,这是一个耗时的手动任务。为了减少操作员的负荷,已经尝试使用颜色空间或图像空间信息来辅助操作员。然而,现有的方法有其局限性。特别是,它们给操作员留下了太多的工作。本文提出了一种密钥提取算法,首次定量地解决了这一问题。我们首先推导出一个偏微分方程,该方程将图像的梯度与alpha值联系起来。然后,我们描述了一种有效的算法,该算法提供alpha值作为方程的解。随着我们精确的运动估计技术,它产生正确的alpha值几乎无处不在,留下很少的工作给操作员。我们还表明,算法和数据表示的精心设计大大提高了人类的互动。在算法的每一步,人类互动是可能的,它是直观的。CR分类:I.3.3[计算机图形学]:图片/图像生成;I.4.6【图像处理】:分割边缘和特征检测;I.4.7【图像处理】:特征测量;I.5.2【模式识别】:设计方法学特征评估与选择。额外的
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