RI: Small: Geometry- and Symmetry-Driven Computer Vision Methods for High-Throughput Automated Microscopic Imaging

人类不仅在复杂的一般问题（如物体检测/识别）上优于当前的计算机视觉方法，而且在特定领域的任务（如哺乳动物胚胎延时视频中的细胞计数和细胞分裂检测）上也优于当前的计算机视觉方法。该项目开发了一个关键的计算机视觉组件，以达到生物应用中形状识别的类人识别性能。该项目为生物学研究提供自动化方法和软件工具。该项目还开发了一个框架，用于解决几何中的基本问题，为计算机视觉研究做出贡献。本研究基于对对称和基于对称测度的六维直方图的构建。它假设物体的形状属性可以通过边缘直方图鲁棒地表示。研究小组开发了一种通过执行边缘直方图的乘积来构建形状属性的方法，以创建更高层次的形状描述。该项目的短期目标是将这一概念应用于计数和跟踪早期小鼠和人类胚胎中的重叠细胞。这项工作的结果包括一个小鼠胚胎中细胞分裂的层次树数据库，直到8个细胞，这对于分析生命早期阶段的特定基因至关重要。中期目标是开发一个可供所有研究人员使用的形状数据库，在那里可以测试其他计算机视觉方法。长期目标是解决理解图像形状这一具有挑战性的问题。该项目影响了图像中物体的检测和识别以及形状的数学描述的发展。它还影响了从大数据中学习理论的发展，因为它研究了高维数据的形状结构。

Humans not only outperform current Computer Vision methods in complex general problems such as object detection/recognition, but also in domain-specific tasks such as counting cells and detecting cell divisions in time-lapse videos of mammalian embryos. This project develops a key computer vision component to reach human-like recognition performance in shape recognition for biology applications. The project provides automated methods and software tools for biology research. The project also develops a framework for addressing fundamental issues in geometry to contribute to computer vision research. This research is rooted on pair wise symmetry and the construction of six dimensional histograms from symmetry measures. It hypothesizes that shape properties of objects can be robustly represented by marginalizing this histogram. The research team develops a method to build shape properties by performing products of marginalized histograms as to create higher level shape descriptions. The short-term goal of this project is to apply this concept to count and track overlapping cells in early mouse and human embryos. The outcome of the work includes a database of hierarchical trees of cell divisions in a mouse-embryo up to the 8-cell is essential for the analysis of particular genes in the early phases of life. The mid-term goal is to develop a shape database available to all researchers, where other computer vision methods can be tested. The long-term goal is to crack the challenging problem of understanding shapes in images. This project impacts the development of detection and recognition of objects in images and the mathematical description of shapes. It also impact on the development of a theory of leaning from big data, as it investigates shape-structures of high dimensional data.