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Automatic trimap generation and artifact reduction in alpha matte using unknown region detection

机译:使用未知区域检测自动生成Trimap和减少Alpha遮罩中的伪影

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摘要

Image Matting is an actively researched topic due to its massive impact on the filmmaking industry. It refers to the accurate extraction of the foreground from an image and is a severely under-constrained problem. To constrain this issue, user input in the form of a trimap is required. However, users must generate this trimap manually, which is an exhausting and time-consuming process. To tackle this issue, we develop an expert system which is capable of generating trimaps automatically without any user interaction. We utilize three different techniques which use machine learning at the core. Our expert system receives knowledge from these three techniques which are processed by our system to generate trimaps automatically. We strongly believe that our study will have a strong impact on the filmmaking industry. Our study lays the groundwork for automatic trimap generation. Furthermore, our study can be applied to expert and intelligent systems related to image retrieval and automatic foreground/primary object segmentation in images or videos. We propose a simple yet effective approach to generate optimal trimaps automatically by combining image saliency, graph cut segmentation (lazy snapping), and fuzzy c-means clustering (FCM). Lazy snapping is an interactive segmentation technique that requires foreground and background scribbles as input. Instead of using user-provided foreground scribbles, we utilize a saliency map as foreground scribbles and input it to the lazy snapping. This results in a coarsely segmented foreground object. We use the corners as the background scribbles based on the assumption that most foreground objects are located in the center of the image. To generate an optimal trimap, we locally cluster the boundary region of the foreground segmentation using FCM. We tested our algorithm on alpha matting evaluation and salient object datasets. In addition to generating accurate trimaps automatically, the alpha mattes generated by our optimal trimaps contain fewer artifacts as compared to trimaps generated by previous works. Moreover, the alpha mattes computed using our optimal trimaps were computed faster as compared to computing alpha mattes with trimaps from previous works. We showed in our experiments that optimal trimaps can improve alpha matte quality by reducing artifacts. Finally, our approach does not rely on depth data like the previous methods. Our experiments show the effectiveness of our method. (C) 2019 Elsevier Ltd. All rights reserved.
机译:由于它对电影制作行业的巨大影响,图像遮罩是一个积极研究的话题。它指的是从图像中准确提取前景,这是一个严重不足的问题。为了限制此问题,需要以三图形式输入用户。但是,用户必须手动生成此trimap,这是一个耗时且耗时的过程。为了解决这个问题,我们开发了一个专家系统,该系统能够自动生成三图,而无需任何用户交互。我们利用三种以机器学习为核心的不同技术。我们的专家系统从这三种技术中获取知识,这些知识由我们的系统处理以自动生成三图。我们坚信我们的研究将对电影制作行业产生重大影响。我们的研究奠定了自动三图生成的基础。此外,我们的研究可应用于与图像检索和图像或视频中的自动前景/主要对象分割有关的专家和智能系统。我们提出了一种简单而有效的方法,通过结合图像显着性,图割分割(惰性抓取)和模糊c均值聚类(FCM)来自动生成最佳三图。惰性捕捉是一种交互式分割技术,需要前景和背景涂鸦作为输入。代替使用用户提供的前景涂鸦,我们将显着性图用作前景涂鸦并将其输入到惰性捕捉中。这导致粗略地分割前景对象。基于大多数前景对象位于图像中心的假设,我们将拐角用作背景涂鸦。为了生成最佳三图,我们使用FCM在本地对前景分割的边界区域进行聚类。我们在alpha遮罩评估和显着对象数据集上测试了我们的算法。除了自动生成准确的三位图之外,与之前的作品生成的三位图相比,我们的最佳三位图生成的alpha遮罩包含的伪影更少。此外,与使用先前作品中的三图计算alpha遮罩相比,使用我们的最佳三图计算的alpha遮罩的计算速度更快。我们在实验中表明,最佳的Trimap可通过减少伪像来提高alpha遮罩质量。最后,我们的方法不像以前的方法那样依赖深度数据。我们的实验证明了我们方法的有效性。 (C)2019 Elsevier Ltd.保留所有权利。

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