INSPIRE: Symmetry Group-based Regularity Perception in Human and Computer Vision

该INSPIRE奖部分由计算机和信息科学与工程局信息和智能系统司的鲁棒智能（RI）计划以及社会，行为和经济科学局行为和认知科学司的感知，行动和认知（PAC）计划资助。这项研究整合了理论，实验和算法的推力，以构建一个新的概念框架，用于预测和理解全方位的规律性感知，无论是在人类中，通过测量人类大脑激活和行为，并在机器中，通过计算机视觉中的自适应对称检测的计算框架。在自然场景中检测模式的能力满足了关键的生物需求，同时为机器智能带来了巨大的计算困难。人类和计算机对图案规则性的感知的研究主要集中在双边对称性上，尽管除了反射之外还有各种各样的规则图案。该项目的一个独特之处是使用对称群理论作为人类和计算机感知模式研究的组织原则。对称群理论，由它的子群层次结构实例化，提供了一个正式的和详尽的分类所有的规则模式。该项目位于计算机科学，心理学，神经科学和数学之间的跨学科联系。这项研究的成果可能会改变人类模式感知理论，并在真实的世界模式的鲁棒自动检测方面取得巨大飞跃。由于模式无处不在，这项研究影响了所有信息处理系统，这些系统受到难以手动探索的大型数字数据集的挑战。通过跨学科研讨会、出版物、数据共享、课堂讲座、博士后和学生培训，系统地与各自的研究界进行外联，进一步加强了其影响力。应用包括医学和监控数据中的异常检测;人造环境中的移动的机器人定位;以及通用模式索引和检索。

This INSPIRE award is partially funded by the Robust Intelligence (RI) Program in the Division of Information and Intelligent Systems in the Directorate for Computer and Information Science and Engineering, and the Perception, Action and Cognition (PAC) Program in the Division of Behavioral and Cognitive Sciences in the Directorate for Social, Behavioral and Economic Sciences.This research integrates theoretical, experimental and algorithmic thrusts to construct a novel conceptual framework for predicting and understanding the full range of regularity perception, both in humans, by measuring human brain activation and behavior, and in machines, through a computational framework for adaptive symmetry detection in computer vision. The ability to detect patterns in natural scenes serves critical biological needs while posing substantial computational difficulties for machine intelligence. Research on human and computer perception of pattern regularity has primarily focused on bilateral symmetry, despite a wide variety of regular patterns beyond reflection. A unique feature of the proposed project is to use symmetry group theory as an organizing principle for the study of both human and computer perception of patterns. Symmetry group theory, instantiated by its subgroup hierarchy, provides a formal and exhaustive categorization of all regular patterns. The project sits at an interdisciplinary nexus between computer science, psychology, neuroscience, and mathematics. The outcomes of this research could potentially transform the theory of human pattern perception and make a quantum leap in robust automatic detection of real world regularities. Because patterns are ubiquitous, this research impacts all information processing systems challenged by large digital datasets that are hard to explore manually. Its impact is strengthened further by a systemic outreach to the respective research communities through interdisciplinary workshops, publications, data sharing, classroom lectures, postdoc and student training. Applications include anomaly detection in medicine and surveillance data; mobile robot localization in man-made environments; and generic pattern indexing and retrieval.