Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item SUB-NYQUIST SENSING AND SPARSE RECOVERY OF WIDE-BAND INTENSITY MODULATED OPTICAL SIGNALS(2018) Lee, Robert; Davis, Christopher; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Intensity modulated optical transmitters, wide-bandwidth electro-optical receivers, high-speed digitizers, and digital matched-filters are being used in hybrid lidar-radar systems to measure the range and reflectivity of objects located within degraded visual underwater environments. These methods have been shown to mitigate the adverse effects of the turbid underwater channel due to the de-correlation of the modulated optical signal after undergoing multiple scattering events. The observed frequency-dependent nature of the underwater channel has driven the desire for wider bandwidth waveforms modulated at higher frequencies in order to improve range accuracy and resolution. While the described system has shown promise, the matched filter processing scheme, which is also widely used in the fields of radar and sonar, suffers from inherent limitations. One limitation is based on the achievable range resolution as dictated by the classical time-frequency uncertainty principle, where the bandwidth dictates the measurable resolution. The side-lobes generated during the matched filtering process also present a challenge when trying to detect multiple targets. These limitations are further constrained by currently-available analog-to-digital conversion technologies which restrict the ability to directly sample the wide-band modulated signals. Even in cases where the technology exists that can operate at sufficient rates, often it is prohibitively expensive for many applications and high data rates can pose processing challenges. This research effort addresses both the restrictions imposed by the available analog-to-digital conversion technologies and the limited resolution of the existing time-frequency methods for wide-band signal processing. The approach is based on concepts found within the fields of compressive sensing and sparse signal recovery and will be applied to the detection of objects illuminated with wide-band intensity modulated optical signals. The underlying assumption is that given the directive nature of laser propagation, the illuminated scene is inherently sparse and the limited number of reflecting objects can be treated as point sources. The main objective of this research is to provide results that show, when sampling at rates below those dictated by the traditional Shannon-Nyquist sampling theorem, it is possible to make more efficient use of the samples collected and detect a limited number of reflecting targets using specialized recovery algorithms without reducing system resolution. Through theoretical derivations, empirical simulations, and experimental investigation, it will be shown under what conditions the sub-Nyquist sampling and sparse recovery techniques are applicable, and how the described methods influence resolution, accuracy, and overall performance in the presence of noise.Item SPARSE ACQUISITION AND RECONSTRUCTION FOR SOME COMPUTER VISION PROBLEMS(2011) Reddy, Nagilla Dikpal; Chellappa, Ramalingam; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Sparse representation, acquisition and reconstruction of signals guided by theory of Compressive Sensing (CS) has become an active research research topic over the last few years. Sparse representations effectively capture the idea of parsimony enabling novel acquisition schemes including sub-Nyquist sampling. Ideas from CS have had significant impact on well established fields such as signal acquisition, machine learning and statistics and have also inspired new areas of research such as low rank matrix completion. In this dissertation we apply CS ideas to low-level computer vision problems. The contribution of this dissertation is to show that CS theory is an important addition to the existing computational toolbox in computer vision and pattern recognition, particularly in data representation and processing. Additionally, in each of the problems we show how sparse representation helps in improved modeling of the underlying data leading to novel applications and better understanding of existing problems. In our work, the impact of CS is most felt in the acquisition of videos with novel camera designs. We build prototype cameras with slow sensors capable of capturing at an order of magnitude higher temporal resolution. First, we propose sub-Nyquist acquisition of periodic events and then generalize the idea to capturing regular events. Both the cameras operate by first acquiring the video at a slower rate and then computationally recovering the desired higher temporal resolution frames. In our camera, we sense the light with a slow sensor after modulating it with a fluttering shutter and then reconstruct the high speed video by enforcing its sparsity. Our cameras offer a significant advantage in light efficiency and cost by obviating the need to sense, transfer and store data at a higher frame rate. Next, we explore the applicability of compressive cameras for computer vision applications in bandwidth constrained scenarios. We design a compressive camera capable of capturing video using fewer measurements and also separate the foreground from the background. We model surveillance type videos with two processes, a slower background and a faster but spatially sparse foreground such that we can recover both of them separately and accurately. By formulating the problem in a distributed CS framework we achieve state-of-the-art video reconstruction and background subtraction. Subsequently we show that if the camera geometry is provided in a multi-camera setting, the background subtracted CS images can be used for localizing the object and tracking it by formulating its occupancy in a grid as a sparse reconstruction problem. Finally, we apply CS to robust estimation of gradients obtained through photometric stereo and other gradient-based techniques. Since gradient fields are often not integrable, the errors in them need to be estimated and removed. By assuming the errors, particularly the outliers, as sparse in number we accurately estimate and remove them. Using conditions on sparse recovery in CS we characterize the distribution of errors which can be corrected completely and those that can be only partially corrected. We show that our approach has the important property of localizing the effect of error during integration where other parts of the surface are not affected by errors in gradients at a particular location. This dissertation is one of the earliest to investigate the implications of compressive sensing theory to some computer vision problems. We hope that this effort will spur more interest in researchers drawn from computer vision, computer graphics, computational photography, statistics and mathematics.Item Robust and Efficient Inference of Scene and Object Motion in Multi-Camera Systems(2009) Sankaranarayanan, Aswin C; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Multi-camera systems have the ability to overcome some of the fundamental limitations of single camera based systems. Having multiple view points of a scene goes a long way in limiting the influence of field of view, occlusion, blur and poor resolution of an individual camera. This dissertation addresses robust and efficient inference of object motion and scene in multi-camera and multi-sensor systems. The first part of the dissertation discusses the role of constraints introduced by projective imaging towards robust inference of multi-camera/sensor based object motion. We discuss the role of the homography and epipolar constraints for fusing object motion perceived by individual cameras. For planar scenes, the homography constraints provide a natural mechanism for data association. For scenes that are not planar, the epipolar constraint provides a weaker multi-view relationship. We use the epipolar constraint for tracking in multi-camera and multi-sensor networks. In particular, we show that the epipolar constraint reduces the dimensionality of the state space of the problem by introducing a ``shared'' state space for the joint tracking problem. This allows for robust tracking even when one of the sensors fail due to poor SNR or occlusion. The second part of the dissertation deals with challenges in the computational aspects of tracking algorithms that are common to such systems. Much of the inference in the multi-camera and multi-sensor networks deal with complex non-linear models corrupted with non-Gaussian noise. Particle filters provide approximate Bayesian inference in such settings. We analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and in particular concentrate on implementations that have minimum processing times. The last part of the dissertation deals with the efficient sensing paradigm of compressing sensing (CS) applied to signals in imaging, such as natural images and reflectance fields. We propose a hybrid signal model on the assumption that most real-world signals exhibit subspace compressibility as well as sparse representations. We show that several real-world visual signals such as images, reflectance fields, videos etc., are better approximated by this hybrid of two models. We derive optimal hybrid linear projections of the signal and show that theoretical guarantees and algorithms designed for CS can be easily extended to hybrid subspace-compressive sensing. Such methods reduce the amount of information sensed by a camera, and help in reducing the so called data deluge problem in large multi-camera systems.