A Closed-Loop Filtering Framework for Active Contours

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The project described in this proposal seeks to a develop a framework for generating robustcomputer vision algorithms for use in closed-loop systems. A principal goal of the research endeavouris to examine the role that control theory may play in enhancing closed-loop computer visionalgorithms.Intellectual Merit. As a sensor, the imaging system can be wrought with noise, either throughthe actual sensing process or through the geometry of the imaged scene. Consequently, the computervision process can be interpreted as a signal processing task in the presence of noise and uncertainy.This is further compounded for closed-loop vision systems, because the control effort can induceadditional disturbances.Through an analysis of the classical Luenberger observer for finite- dimensional systems, the PIproposes to systematically build up a similar framework for filtering of closed curves, which are theby-products of th e computer vision algorithms known as active contours. Essentially, this researchendeavour involves the development of a Kalman filter algorithm for closed curves, and involves aprincipled predict and update scheme. The main investigative challenge lies in the fact that closedcurves form an infinite-dimensional space which classical state-space observer theory is not capableof handling.Broader Impact. The successful consideration of computer vision algorithms as components ofa control and dynamical system has the ability to tremendously increase their level of robustness,without severely complicating the nature of their processing. As a particular application demonstratingthe potential impact and challenges of this research avenue within a specific area, the PIseeks to study the closed-loop control and estimation of bio-membranes at the mico-scale. The observerconcept described herein has the capacity to improve the signal generated from vision-basedalgorithms that form the main information pathway of a closed-loop process.The proposed education plan incorporates many of these ideas into existing computer visioncourses, as they are essential components in the use of computer vision for feedback-driven systems.Secondly, summer research and senior-design projects are anticipated to motivate the importanceof control and dynamical systems theory for computer vision.