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

图像分割在科学、医学、艺术等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动。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是优选的，因为对于许多应用来说，分割可能是模糊的、模糊的和/或主观的。此外，虽然基于图形的算法对小图像很有效，但对大图像的可伸缩性很差，近年来消费者和科学图像的大小呈爆炸性增长。这项工作将为新的算法奠定基础，这些算法可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动交互、不需要显著高性能后端的交互以及在移动设备等普通硬件上运行的能力，从而为大型图像的稳健交互分割奠定基础。这项研究中开发的技术不仅将在计算机科学领域做出基础性贡献，而且将使科学、医学和艺术领域的应用程序取得重大进展。更直接的是，该项目将支持一名研究生，他是代表人数不足的少数族裔的成员，并将为一篇高影响力的论文奠定基础。工作重点将是最小割和最小路径分段的可扩展算法。首先，这项研究的目标是通过使用改进的图像滤波和多个窄带的计算来实现稳健的分层分割。这将改善目前的最新技术，目前的技术要么由于在优化过程中陷入局部极小而产生较差的分割，要么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颖的查询驱动的、依赖于视图的分割，该分割是在用户探索大图像并操纵分割时产生的，而不需要全分辨率解决方案。这使得昂贵的完全优化可以推迟到交互完成之后。用户用于交互的努力将独立于分割对象的比例。确保局部的、视点相关的解是完全优化的有效表示，而不需要先验地知道解，这将是对图像分割的最新技术的重大进步。

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.