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

INSPIRE：人类和计算机视觉中基于对称群的规则感知

• 批准号: 1248076
• 负责人: Yanxi Liu
• 金额: $ 80万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1248076&HistoricalAwards=false
• 关键词: INSPIRE; Symmetry; Group; based; Regularity

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.

INSPIRE奖的部分资金来自计算机和信息科学与工程局信息和智能系统部的稳健智能(RI)计划，以及社会、行为和经济科学局行为和认知科学部的感知、行动和认知(PAC)计划。这项研究整合了理论、实验和算法的推动力，构建了一个新的概念框架，通过测量人脑的激活和行为，以及通过计算机视觉中自适应对称检测的计算框架，在人类和机器中预测和理解全方位的规律性感知。检测自然场景中的模式的能力满足了关键的生物学需求，同时也给机器智能带来了巨大的计算困难。关于人类和计算机对图案规律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律图案。该项目的一个独特之处是使用对称群论作为研究人类和计算机对图案的感知的组织原则。对称群理论，由它的子群层次结构实例化，提供了对所有规则模式的形式化和穷尽的分类。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交汇点。这项研究的结果可能会改变人类模式感知的理论，并在对现实世界规律的稳健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受到大型数字数据集挑战的信息处理系统，这些数据集很难手动探索。通过跨学科讲习班、出版物、数据共享、课堂讲座、博士后和学生培训，系统地向各自的研究界拓展，进一步加强了它的影响。应用包括医学和监测数据中的异常检测；人造环境中的移动机器人定位；以及通用模式索引和检索。