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

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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.