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Ph.D. Dissertation Defense: Garrett Warnell
Friday, July 18, 2014
10:00 a.m.
Room 4424
For More Information:
Maria Hoo
301 405 3681
mch@umd.edu

ANNOUNCEMENT: Ph.D. Dissertation Defense
 
Name: Garrett Warnell

Committee:
Professor Rama Chellappa, Chair
Professor Larry Davis
Professor Piya Pal
Professor Amitabh Varshney
Professor John Benedetto (Dean's Representative)

Date/Time: Friday, July 18, 2014, 10:00am

Location: Room 4172 AV Williams Building

Title: Adaptive Sensing and Processing for Some Computer Vision Problems

Abstract:

This dissertation is concerned with adaptive sensing and processing in computer vision, specifically through the application of computer vision techniques to non-standard sensors.

In the first part, we adapt the classical computer vision problem of gradient-based surface reconstruction to the phase unwrapping problem that presents itself in, among other applications, interferometric synthetic aperture radar. Specifically, we propose a new formulation of and solution to the classical two-dimensional phase unwrapping problem. As is usually done, we use the wrapped principal phase gradient field as a measurement of the absolute phase gradient field. Since this model rarely holds in practice, we explicitly enforce integrability of the gradient measurements through a sparse error-correction model. Using a novel energy-minimization functional, we formulate the phase unwrapping task as a generalized lasso problem. We then jointly estimate the absolute phase and the sparse measurement errors using the alternating direction method of multipliers (ADMM) algorithm. Using an interferometric synthetic aperture radar noise model, we evaluate our technique for several synthetic surfaces and compare the results to recently-proposed phase unwrapping techniques. Our method applies new ideas from convex optimization and sparse regularization to the well-studied problem of phase unwrapping.

In the second part, we consider the problem of processing and adjusting a non-traditional compressive sensing (CS) camera in real time such that the number of measurements it collects remains proportional to the amount of foreground information currently present in the scene under observations. We provide two novel adaptive-rate CS strategies for sparse, time-varying signals using side information. Our first method utilizes extra cross-validation measurements, and the second one exploits extra low-resolution measurements. Unlike the majority of current CS techniques, we do not assume that we know an upper bound on the number of significant coefficients that comprise the images in the video sequence. Instead, we use the side information to predict the number of significant coefficients in the signal at the next time instant. For each image in the video sequence, our techniques specify a fixed number of spatially-multiplexed CS measurements to acquire, and adjust this quantity from image to image. Our strategies are developed in the specific context of background subtraction for surveillance video, and we experimentally validate the proposed methods on real video sequences.

Finally, we consider a problem motivated by the application of active pan-tilt-zoom (PTZ) camera control in response to visual saliency. We extend the classical notion of this concept to multi-image data collected using a stationary PTZ camera by introducing the concept of consistency: the requirement that the set of generated saliency maps should each assign the same saliency value to distinct regions of the environment that appear in more than one image. We show that processing each image independently will often fail to provide a consistent measure of saliency, and that using an image mosaic to quantify saliency suffers from several drawbacks. We then propose ray saliency: a mosaic-free method for calculating a consistent measure of bottom-up saliency. Experimental results demonstrating the effectiveness of the proposed approach are presented.
 

This Event is For: Graduate • Faculty

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