UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
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 given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Partial Face Detection and Illumination Estimation(2018) Sarkar, Sayantan; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Face Analysis has long been a crucial component of many security applications. In this work, we shall propose and explore some face analysis algorithms which are applicable to two different security problems, namely Active Authentication and Image Tampering Detection. In the first section, we propose two algorithms, “Deep Feature based Face Detection for Mobile Devices” and “DeepSegFace” that are useful in detecting partial faces such as those seem in typical Active Authentication scenarios. In the second section, we propose an algorithm to detect discrepancies in illumination conditions given two face images, and use that as an indication to decide if an image has been tampered by transplanting faces. We also extend the illumination detection algorithm by proposing an adversarial data augmentation scheme. We show the efficacy of the proposed algorithms by evaluating them on multiple datasets.Item Accessible On-Body Interaction for People With Visual Impairments(2016) Oh, Uran Oh; Findlater, Leah; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)While mobile devices offer new opportunities to gain independence in everyday activities for people with disabilities, modern touchscreen-based interfaces can present accessibility challenges for low vision and blind users. Even with state-of-the-art screenreaders, it can be difficult or time-consuming to select specific items without visual feedback. The smooth surface of the touchscreen provides little tactile feedback compared to physical button-based phones. Furthermore, in a mobile context, hand-held devices present additional accessibility issues when both of the users’ hands are not available for interaction (e.g., on hand may be holding a cane or a dog leash). To improve mobile accessibility for people with visual impairments, I investigate on-body interaction, which employs the user’s own skin surface as the input space. On-body interaction may offer an alternative or complementary means of mobile interaction for people with visual impairments by enabling non-visual interaction with extra tactile and proprioceptive feedback compared to a touchscreen. In addition, on-body input may free users’ hands and offer efficient interaction as it can eliminate the need to pull out or hold the device. Despite this potential, little work has investigated the accessibility of on-body interaction for people with visual impairments. Thus, I begin by identifying needs and preferences of accessible on-body interaction. From there, I evaluate user performance in target acquisition and shape drawing tasks on the hand compared to on a touchscreen. Building on these studies, I focus on the design, implementation, and evaluation of an accessible on-body interaction system for visually impaired users. The contributions of this dissertation are: (1) identification of perceived advantages and limitations of on-body input compared to a touchscreen phone, (2) empirical evidence of the performance benefits of on-body input over touchscreen input in terms of speed and accuracy, (3) implementation and evaluation of an on-body gesture recognizer using finger- and wrist-mounted sensors, and (4) design implications for accessible non-visual on-body interaction for people with visual impairments.Item WISE Abstraction Framework for Wireless Networks(2006-08-03) Lee, Seungjoon; Bhattacharjee, Samrat; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Current wireless networks commonly consist of nodes with different capabilities (e.g., laptops and PDAs). Link quality such as link error rate and data transmit rate can differ widely. For efficient operation, the design of wireless networks must take into account such heterogeneity among nodes and wireless links. We present systematic approaches to overcome problems due to heterogeneous node capability and link quality in wireless networks. We first present a general framework called WISE (Wireless Integration Sublayer Extension) that abstracts specific details of low-level wireless communication technologies (e.g., modulation or backoff scheme). WISE provides a set of common primitives, based on which upper-level protocols can operate efficiently without knowing the underlying details. We also present a number of protocol extensions that employ the WISE framework to enhance the performance of specific upper-level protocols while hiding lower-level heterogeneity (e.g., link error rate). Our multihop WLAN architecture improves system performance by allowing client nodes to use multihop paths via other clients to reach an AP. Our geographic routing extension considers both location and link quality in the next hop selection, which leads to optimal paths under certain conditions. To address heterogeneity in node capability, we consider virtual routing backbone construction in two settings: cooperative and selfish. In the cooperative setting, we present a protocol extension that constructs an optimal backbone composed of a small number of high-capability nodes, which can be generalized to a more resilient backbone. For the selfish case, we use game theory and design an incentive-compatible backbone construction scheme. We evaluate our work from multiple perspectives. We use theoretical analysis to prove that our extensions lead to optimal solutions. We use simulations to experiment with our schemes in various scenarios and real-world implementation to understand the performance in practice. Our experiment results show that our schemes significantly outperform existing schemes.