CRII: CHS: Scalable Interactive Image Segmentation through Hierarchical, Query-Driven Processing

图像分割在科学、医学和艺术领域有着广泛的应用，是一种不可或缺的处理工具。 最成功的分割算法将像素映射到图上，在该图上定义能量函数，并使用图论将分割转换为该离散空间的最小化，以计算图上的最小割、最小路径、最小生成树或随机游走。 虽然分割可以自动计算，但基于用户输入的半自动交互式方法通常是优选的，因为对于许多应用，分割可能是不明确的、模糊的和/或主观的。 此外，虽然对小图像有效，但基于图形的算法对大图像的扩展性很差，近年来消费者和科学图像的大小爆炸式增长。 这项工作将为大型图像的鲁棒交互式分割提供独立于图像大小的可操作的实时反馈，与分割对象缩放的流体交互，无需显着高性能后端的交互性以及在移动的设备等适度硬件上运行的能力奠定基础。 在这项研究中开发的技术不仅将在计算机科学中提供基础性的贡献，而且将使跨科学，医学和艺术的应用取得重大进展。 更直接的是，该项目将支持一个研究生谁是一个代表性不足的少数民族的成员，并将提供一个高影响力的论文的基础。工作将集中在最小切割和最小路径分割的可扩展算法。 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目标鲁棒的、分层的分割。 这将改善目前由于在优化期间落入局部最小值而产生差的分割的现有技术，需要显著的高性能后端，或者依赖于繁重的硬件驱动的预处理。 其次，这项工作将设计一种新的查询驱动的，视图相关的分割，这是作为一个用户探索的大图像和操纵的分割，而不需要的全分辨率解决方案。 这使得昂贵的完全优化延迟到交互完成之后。 用户交互的努力将独立于分割对象的规模。 确保局部的，视图相关的解决方案是一个有效的表示的充分优化，而不知道解决方案的先验将构成一个显着的进步，以国家的最先进的图像分割。

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.