CRII: CHS: Scalable Interactive Image Segmentation through Hierarchical, Query-Driven Processing
CRII:CHS:通过分层、查询驱动的处理进行可扩展的交互式图像分割
基本信息
- 批准号:1657020
- 负责人:
- 金额:$ 12.7万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-08-15 至 2020-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Image segmentation is an indispensable processing tool due to its wide applications in science, medicine, and the arts. The most successful segmentation algorithms map pixels onto a graph, define an energy function on this graph, and cast segmentation as a minimization of this discrete space using graph theory to compute minimum cuts, minimum paths, minimum spanning trees, or random walks on the graph. While segmentations can be calculated automatically, semi-automatic interactive approaches based on user input are often preferred because segmentations can be ill-defined, ambiguous, and/or subjective for many applications. Furthermore, while efficient for small images, graph-based algorithms scale poorly for large imagery, and in recent years consumer and scientific imagery has exploded in size. This work will lay the foundation for novel algorithms for robust interactive segmentation of large imagery that provide actionable real-time feedback independent of the image size, fluid interactions that scale with the segmented object, interactivity without the need for a significant high-performance backend, and the ability to run on modest hardware like mobile devices. The techniques developed in this research will not only provide fundamental contributions within computer science, but will enable significant advancements in applications across the sciences, in medicine and the arts. More immediately, the project will support a graduate student who is a member of an underrepresented minority, and will provide the groundwork for a high-impact dissertation.The work will focus on scalable algorithms for minimum cut and minimum path segmentations. First, the research will target robust, hierarchical segmentation through the use of improved image filtering and the computation of multiple narrow bands. This will improve on the state-of-the-art which currently either produces poor segmentations due to falling into local minima during the optimization, needs a significant high-performance backend, or relies on heavy heuristically-driven preprocessing. Second, the work will design a novel query-driven, view-dependent segmentation that is produced as a user explores the large image and manipulates the segmentation without the need of the full resolution solution. This enables the deferment of the expensive full optimization until after the interaction is completed. User effort for interactions will be independent of the scale of the segmented object. Assuring that the local, view-dependent solution is a valid representation of the full optimization without knowing the solution a priori will constitute a significant advancement to the state-of-the-art in image segmentation.
图像分割在科学、医学和艺术领域有着广泛的应用,是一种必不可少的图像处理工具。最成功的分割算法将像素映射到一个图上,在这个图上定义一个能量函数,并使用图论计算最小切割、最小路径、最小生成树或图上的随机行走,将分割转换为这个离散空间的最小化。虽然分割可以自动计算,但基于用户输入的半自动交互方法通常更可取,因为对许多应用程序来说,分割可能定义不清、模棱两可和/或主观。此外,虽然对小图像有效,但基于图的算法对大图像的可扩展性很差,而且近年来消费者和科学图像的规模呈爆炸式增长。这项工作将为大型图像的鲁棒交互式分割的新算法奠定基础,这些算法可以提供独立于图像大小的可操作的实时反馈、随分割对象缩放的流体交互、不需要重要的高性能后端的交互性,以及在移动设备等适度硬件上运行的能力。在这项研究中开发的技术不仅将为计算机科学提供基础贡献,而且将在科学,医学和艺术领域的应用中取得重大进展。更直接的是,该项目将支持一名研究生,他是一个代表性不足的少数民族的成员,并将为一篇高影响力的论文提供基础。这项工作将集中在最小切割和最小路径分割的可扩展算法上。首先,研究将通过使用改进的图像滤波和多个窄带的计算来实现鲁棒的分层分割。这将改善当前最先进的技术,因为在优化过程中由于陷入局部最小值而产生较差的分割,需要重要的高性能后端,或者依赖于大量启发式驱动的预处理。其次,该工作将设计一种新的查询驱动的,视图依赖的分割,当用户探索大图像并操纵分割时,而不需要全分辨率解决方案。这使得昂贵的全面优化延迟到交互完成之后。用户对交互的努力将与分割对象的规模无关。在不知道先验解决方案的情况下,确保局部的、依赖于视图的解决方案是完整优化的有效表示,将构成图像分割领域最先进的重大进步。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Persistence Atlas for Critical Point Variability in Ensembles
- DOI:10.1109/tvcg.2018.2864432
- 发表时间:2018-07
- 期刊:
- 影响因子:5.2
- 作者:Guillaume Favelier;Noura Faraj;B. Summa;Julien Tierny
- 通讯作者:Guillaume Favelier;Noura Faraj;B. Summa;Julien Tierny
Flexible Live-Wire: Image Segmentation with Floating Anchors
灵活的火线:使用浮动锚点进行图像分割
- DOI:10.1111/cgf.13364
- 发表时间:2018
- 期刊:
- 影响因子:2.5
- 作者:Summa, B.;Faraj, N.;Licorish, C.;Pascucci, V.
- 通讯作者:Pascucci, V.
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Brian Summa其他文献
Brian Summa的其他文献
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{{ truncateString('Brian Summa', 18)}}的其他基金
EAGER: Scalable, Content-Based, Domain-Agnostic Search of Scientific Data through Concise Topological Representations
EAGER:通过简洁的拓扑表示对科学数据进行可扩展、基于内容、与领域无关的搜索
- 批准号:
2136744 - 财政年份:2021
- 资助金额:
$ 12.7万 - 项目类别:
Standard Grant
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