Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
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
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Are different temperament traits involved in adapting to routine and novel situations?(2021) Shoplik, Helena; Teglasi, Hedwig; Counseling and Personnel Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Individual differences in adaptability, defined as ease of responding to changes, was initially suggested as a temperamental disposition, observable during the first years of life (Thomas & Chess, 1977), but turned out to be a more complex phenomenon with contributions from multiple temperamental traits (Teglasi, 1998). Temperament traits contribute differently depending on the functional requirements of routine and familiar contexts for reactive and self-regulatory processes. The current study utilizes parent-reported temperament traits measured by the Structured Temperament Interview (STI) and by a well-respected temperament measure (the Child Behavior Questionnaire; CBQ), as well as correlates of adaptive responsiveness (e.g. social competence and emotion understanding) to highlight the role that emotions play in adjustment to familiar and novel contexts. Part of an archival data set, pre-schoolers’ parents completed the CBQ (Rothbart, et al., 2001) and the STI (Teglasi, unpublished) and reported how well their child adapted in novel and routine contexts. Children completed the Emotion Comprehension Test (ECT; Teglasi, unpublished) and teachers filled out the Social Competence Behavior Evaluation (SCBE; Freniere & Dumas, 1995). Results provided support for conceptualising temperament traits as working together like a team—the addition of one temperament trait can change the expression of another. Additionally, different traits emerged as unique predictors in novel and routine situations, even when controlling for the overlap between those situations and other traits. Finally, this study continued to expand on a new construct, Resistance to Emotional Attention, which captures the function of attention as it relates to emotional stimuli.Item Context Driven Scene Understanding(2015) Chen, Xi; Davis, Larry S; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Understanding objects in complex scenes is a fundamental and challenging problem in computer vision. Given an image, we would like to answer the questions of whether there is an object of a particular category in the image, where is it, and if possible, locate it with a bounding box or pixel-wise labels. In this dissertation, we present context driven approaches leveraging relationships between objects in the scene to improve both the accuracy and efficiency of scene understanding. In the first part, we describe an approach to jointly solve the segmentation and recognition problem using a multiple segmentation framework with context. Our approach formulates a cost function based on contextual information in conjunction with appearance matching. This relaxed cost function formulation is minimized using an efficient quadratic programming solver and an approximate solution is obtained by discretizing the relaxed solution. Our approach improves labeling performance compared to other segmentation based recognition approaches. Secondly, we introduce a new problem called object co-labeling where the goal is to jointly annotate multiple images of the same scene which do not have temporal consistency. We present an adaptive framework for joint segmentation and recognition to solve this problem. We propose an objective function that considers not only appearance but also appearance and context consistency across images of the scene. A relaxed form of the cost function is minimized using an efficient quadratic programming solver. Our approach improves labeling performance compared to labeling each image individually. We also show the application of our co-labeling framework to other recognition problems such as label propagation in videos and object recognition in similar scenes. In the third part, we propose a novel general strategy for simultaneous object detection and segmentation. Instead of passively evaluating all object detectors at all possible locations in an image, we develop a divide-and-conquer approach by actively and sequentially evaluating contextual cues related to the query based on the scene and previous evaluations---like playing a ``20 Questions'' game---to decide where to search for the object. Such questions are dynamically selected based on the query, the scene and current observed responses given by object detectors and classifiers. We first present an efficient object search policy based on information gain of asking a question. We formulate the policy in a probabilistic framework that integrates current information and observation to update the model and determine the next most informative action to take next. We further enrich the power and generalization capacity of the Twenty Questions strategy by learning the Twenty Questions policy driven by data. We formulate the problem as a Markov Decision Process and learn a search policy by imitation learning.Item Model-driven and Data-driven Approaches for some Object Recognition Problems(2011) Gopalan, Raghuraman; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recognizing objects from images and videos has been a long standing problem in computer vision. The recent surge in the prevalence of visual cameras has given rise to two main challenges where, (i) it is important to understand different sources of object variations in more unconstrained scenarios, and (ii) rather than describing an object in isolation, efficient learning methods for modeling object-scene `contextual' relations are required to resolve visual ambiguities. This dissertation addresses some aspects of these challenges, and consists of two parts. First part of the work focuses on obtaining object descriptors that are largely preserved across certain sources of variations, by utilizing models for image formation and local image features. Given a single instance of an object, we investigate the following three problems. (i) Representing a 2D projection of a 3D non-planar shape invariant to articulations, when there are no self-occlusions. We propose an articulation invariant distance that is preserved across piece-wise affine transformations of a non-rigid object `parts', under a weak perspective imaging model, and then obtain a shape context-like descriptor to perform recognition; (ii) Understanding the space of `arbitrary' blurred images of an object, by representing an unknown blur kernel of a known maximum size using a complete set of orthonormal basis functions spanning that space, and showing that subspaces resulting from convolving a clean object and its blurred versions with these basis functions are equal under some assumptions. We then view the invariant subspaces as points on a Grassmann manifold, and use statistical tools that account for the underlying non-Euclidean nature of the space of these invariants to perform recognition across blur; (iii) Analyzing the robustness of local feature descriptors to different illumination conditions. We perform an empirical study of these descriptors for the problem of face recognition under lighting change, and show that the direction of image gradient largely preserves object properties across varying lighting conditions. The second part of the dissertation utilizes information conveyed by large quantity of data to learn contextual information shared by an object (or an entity) with its surroundings. (i) We first consider a supervised two-class problem of detecting lane markings from road video sequences, where we learn relevant feature-level contextual information through a machine learning algorithm based on boosting. We then focus on unsupervised object classification scenarios where, (ii) we perform clustering using maximum margin principles, by deriving some basic properties on the affinity of `a pair of points' belonging to the same cluster using the information conveyed by `all' points in the system, and (iii) then consider correspondence-free adaptation of statistical classifiers across domain shifting transformations, by generating meaningful `intermediate domains' that incrementally convey potential information about the domain change.Item Rover: Architectural Support for Exposing and Using Context(2010) Almazan, Christian Butiu; Agrawala, Ashok K; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Technology has advanced to the point where many people feel it has created a world with an insurmountable amount of information. Information includes messages people send to each other, logged data from their activities, and the services available to them. This problem has been exaggerated in modern societies by high availability of Internet connectivity. All types of information contains context, whether they have been stated explicitly or understood implicitly. Understanding, handling, and using context represents one of the most critical steps towards coping with the amount of information available today. In this dissertation, we examine two topics: context and the design of a context-aware platform. We describe fundamental types of context associated with every piece of information and discuss issues which may occur when implementing a system which utilizes context. We present a context-aware platform called Rover. The Rover architecture provides a conceptual framework geared towards understanding how application developers can utilize a variety of aspects of context to assist the development of modern applications. To aid developers in figuring out what context may be useful in their application, we describe the concept of a Rover ecosystem: a logical organization analogous to how similar groups of people interact with each other. We also discuss how information and context can be shared between ecosystems. To examine the feasibility of the Rover architecture's conceptual framework, we have implemented a reference implementation of the core unit of a Rover ecosystem: the Rover server. We discuss the details of the Rover server and describe the implementation of an emergency response application which demonstrates the utility of the conceptual framework.