Browsing by Subject "Segmentation"
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Item A comparison of the effects of mobile device display size and orientation, and text segmentation on learning, cognitive load, and user perception in a higher education chemistry course(2015-05) Karam, Angela Marie; Resta, Paul E.; Liu, Min; Hughes, Joan E.; Riegle-Crumb, Catherine; Matthew, EastinThis study aimed to understand the relationship between mobile device screen display size (laptops and smartphones) and text segmentation (continuous text, medium text segments, and small text segments) on learning outcomes, cognitive load, and user perception. This quantitative study occurred during the spring semester of 2015. Seven hundred and seventy-one chemistry students from a higher education university completed one of nine treatments in this 3x3 research design. Data collection took place over four class periods. The study revealed that learning outcomes were not affected by the mobile screen display size or orientation, nor was working memory. However, user perception was affected by the screen display size of the device, and results indicated that participants in the sample felt laptop screens were more acceptable for accessing the digital chemistry text than smartphone screens by a small margin. The study also found that neither learning outcomes, nor working memory was affected by the text segmentation viewed. Though user perception was generally not affected by text segmentation, the study found that for perceived ease of use, participants felt medium text segments were easier to learn from than either continuous or small test segments by a small margin. No interaction affects were found between mobile devices and text segmentation. These findings challenge the findings of some earlier studies that laptops may be better for learning than smartphones because of screen size, landscape orientation is better for learning than portrait orientation in small screen mobile devices, and meaningful text segments may be better for learning than non-meaningful, non-segmented, or overly segmented text. The results of this study suggest that customizing the design to the smartphone screen (as opposed to a one-size-fits-all approach) improves learning from smartphones, making them equal to learning from laptops in terms of learning outcomes and cognitive load, and in some cases, user perspective.Item Identifying key disseminators in social commerce : a segmentation study from the gatekeeping perspective(2012-05) Chen, Yizhuo; Krifa, Mourad; Lee, Hyun-Hwa; Reed, AnnIn recent years, social commerce sites such as Groupon and LivingSocial have achieved great success in attracting new consumers and increasing store traffic for a growing number of businesses. However, it is still unclear how the information flow to reach new consumers is generated. Understanding this information flow is the key to the question of what lead to the success of these companies. In the online context, the key information disseminators can have both a large-scale network and a decisive influence on the nodes that are connected closely to them, indicating an important pattern of consumer purchase process. Here, we argue that one of the prominent advantages of social commerce is the information dissemination process, during which word of mouth (WOM) is generated to boost consumer traffic. In the present study, we conduct a cluster analysis to segment online shoppers according to their information dissemination contribution. Gatekeeping theory was used for conceptualizing consumers who tend to disseminate more commercial information and WOM in social commerce, providing us the theoretical basis for clustering consumers. Our findings suggest that a sizable proportion of consumers constituted the gatekeeper group (approximately 25%). Gatekeepers tend to be highly active in both finding the outside source of information and connecting it with inside social networks. In addition, different aspects of the potential to become gatekeepers divide the rest of the consumers into two groups. To date, the present research is the first to explore online consumer segmentations using the gatekeeping perspective.Item Region detection and matching for object recognition(2013-08) Kim, Jaechul; Grauman, Kristen Lorraine, 1979-In this thesis, I explore region detection and consider its impact on image matching for exemplar-based object recognition. Detecting regions is important to provide semantically meaningful spatial cues in images. Matching establishes similarity between visual entities, which is crucial for recognition. My thesis starts by detecting regions in both local and object level. Then, I leverage geometric cues of the detected regions to improve image matching for the ultimate goal of object recognition. More specifically, my thesis considers four key questions: 1) how can we extract distinctively-shaped local regions that also ensure repeatability for robust matching? 2) how can object-level shape inform bottom-up image segmentation? 3) how should the spatial layout imposed by segmented regions influence image matching for exemplar-based recognition? and 4) how can we exploit regions to improve the accuracy and speed of dense image matching? I propose novel algorithms to tackle these issues, addressing region-based visual perception from low-level local region extraction, to mid-level object segmentation, to high-level region-based matching and recognition. First, I propose a Boundary Preserving Local Region (BPLR) detector to extract local shapes. My approach defines a novel spanning-tree based image representation whose structure reflects shape cues combined from multiple segmentations, which in turn provide multiple initial hypotheses of the object boundaries. Unlike traditional local region detectors that rely on local cues like color and texture, BPLRs explicitly exploit the segmentation that encodes global object shape. Thus, they respect object boundaries more robustly and reduce noisy regions that straddle object boundaries. The resulting detector yields a dense set of local regions that are both distinctive in shape as well as repeatable for robust matching. Second, building on the strength of the BPLR regions, I develop an approach for object-level segmentation. The key insight of the approach is that objects shapes are (at least partially) shared among different object categories--for example, among different animals, among different vehicles, or even among seemingly different objects. This shape sharing phenomenon allows us to use partial shape matching via BPLR-detected regions to predict global object shape of possibly unfamiliar objects in new images. Unlike existing top-down methods, my approach requires no category-specific knowledge on the object to be segmented. In addition, because it relies on exemplar-based matching to generate shape hypotheses, my approach overcomes the viewpoint sensitivity of existing methods by allowing shape exemplars to span arbitrary poses and classes. For the ultimate goal of region-based recognition, not only is it important to detect good regions, but we must also be able to match them reliably. A matching establishes similarity between visual entities (images, objects or scenes), which is fundamental for visual recognition. Thus, in the third major component of this thesis, I explore how to leverage geometric cues of the segmented regions for accurate image matching. To this end, I propose a segmentation-guided local feature matching strategy, in which segmentation suggests spatial layout among the matched local features within each region. To encode such spatial structures, I devise a string representation whose 1D nature enables efficient computation to enforce geometric constraints. The method is applied for exemplar-based object classification to demonstrate the impact of my segmentation-driven matching approach. Finally, building on the idea of regions for geometric regularization in image matching, I consider how a hierarchy of nested image regions can be used to constrain dense image feature matches at multiple scales simultaneously. Moving beyond individual regions, the last part of my thesis studies how to exploit regions' inherent hierarchical structure to improve the image matching. To this end, I propose a deformable spatial pyramid graphical model for image matching. The proposed model considers multiple spatial extents at once--from an entire image to grid cells to every single pixel. The proposed pyramid model strikes a balance between robust regularization by larger spatial supports on the one hand and accurate localization by finer regions on the other. Further, the pyramid model is suitable for fast coarse-to-fine hierarchical optimization. I apply the method to pixel label transfer tasks for semantic image segmentation, improving upon the state-of-the-art in both accuracy and speed. Throughout, I provide extensive evaluations on challenging benchmark datasets, validating the effectiveness of my approach. In contrast to traditional texture-based object recognition, my region-based approach enables to use strong geometric cues such as shape and spatial layout that advance the state-of-the-art of object recognition. Also, I show that regions' inherent hierarchical structure allows fast image matching for scalable recognition. The outcome realizes the promising potential of region-based visual perception. In addition, all my codes for local shape detector, object segmentation, and image matching are publicly available, which I hope will serve as useful new additions for vision researchers' toolbox.Item Segmentation of highway networks for maintenance operations(2016-05) Kim, Moo Yeon; Williamson, Sinead; Prozzi, Jorge A.Pavement maintenance and rehabilitation (M&R) is important for transportation agencies to have a sustainable transportation infrastructure. In maintenance operations, obtaining limits of homogeneous sections is a key problem because appropriate segmentation can help yield a more cost effective M&R plan. The purpose of this study is to present the result of investigation on various research works and to suggest the direction of developing an enhanced methodological framework. Existing approaches for pavement segmentation was explored through a literature review and data analysis. Autocorrelation tests, change-point approaches, a Bayesian method, and a hidden Markov model were performed using pavement condition data. Future work directions were suggested to develop a segmentation method capable of handling the issues found in the study.Item Segmenting participants of a charity sport event(2014-08) Ogura, Toshiyuki; Green, B. ChristineThe increased competition among charity sport events (CSEs) require charity organizations to utilize more sophisticated marketing programs - segmenting and targeting diverse participants more effectively. The study examines the effectiveness of demographic, psychographic, behavioral segmentation variables. In-depths interviews with 14 participants were conducted to obtain profiles of the four segments of survivor-centered teams, family and friends, company-sponsored teams, and other organization teams. The distinct profile of each segment had a combination of psychological, behavioral and demographic characteristics. Participation mode was identified as a proxy segmentation variable that can be easily obtained by event organizers at the time of participant registration Management of participant segments was discussed.Item Strategy for construction of polymerized volume data sets(Texas A&M University, 2006-04-12) Aragonda, PrathyushaThis thesis develops a strategy for polymerized volume data set construction. Given a volume data set defined over a regular three-dimensional grid, a polymerized volume data set (PVDS) can be defined as follows: edges between adjacent vertices of the grid are labeled 1 (active) or 0 (inactive) to indicate the likelihood that an edge is contained in (or spans the boundary of) a common underlying object, adding information not in the original volume data set. This edge labeling ??polymerizes?? adjacent voxels (those sharing a common active edge) into connected components, facilitating segmentation of embedded objects in the volume data set. Polymerization of the volume data set also aids real-time data compression, geometric modeling of the embedded objects, and their visualization. To construct a polymerized volume data set, an adjacency class within the grid system is selected. Edges belonging to this adjacency class are labeled as interior, exterior, or boundary edges using discriminant functions whose functional forms are derived for three local adjacency classes. The discriminant function parameter values are determined by supervised learning. Training sets are derived from an initial segmentation on a homogeneous sample of the volume data set, using an existing segmentation method. The strategy of constructing polymerized volume data sets is initially tested on synthetic data sets which resemble neuronal volume data obtained by three-dimensional microscopy. The strategy is then illustrated on volume data sets of mouse brain microstructure at a neuronal level of detail. Visualization and validation of the resulting PVDS is shown in both cases. Finally the procedures of polymerized volume data set construction are generalized to apply to any Bravais lattice over the regular 3D orthogonal grid. Further development of this latter topic is left to future work.Item The use of archetypes in advertising : how brands can remain relevant in a rapidly changing advertising industry through the concept of archetypes(2013-05) Jasso, Luis Raul; Burns, Neal M., 1933-Archetypes, as defined by Carl Jung, are "a universal and recurring image, pattern, or motif representing a typical human experience." Thus they are thoughtful representations of human characteristics that essentially describe universal human motivations. In advertising, archetypes have been used to help define brands and present them to consumers in a meaningful way. This report proposes to validate the usage of archetypes as a tool to bolster the storytelling aspect of brands to the consumer. The suggestion here is that brands that are challenged in reaching today’s complex global consumer can evoke the desired brand meaning by incorporating values of the appropriate archetypes. The author also believes that understanding an individual’s archetypes can help uncover insights that relate to aspects of their values and attitudes for brands and, in that way, those archetypes can be a reasonable predictor of affective reactions to symbolic advertisement communication.