Matching Items (4)
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Description
Image segmentation is of great importance and value in many applications. In computer vision, image segmentation is the tool and process of locating objects and boundaries within images. The segmentation result may provide more meaningful image data. Generally, there are two fundamental image segmentation algorithms: discontinuity and similarity. The idea

Image segmentation is of great importance and value in many applications. In computer vision, image segmentation is the tool and process of locating objects and boundaries within images. The segmentation result may provide more meaningful image data. Generally, there are two fundamental image segmentation algorithms: discontinuity and similarity. The idea behind discontinuity is locating the abrupt changes in intensity of images, as are often seen in edges or boundaries. Similarity subdivides an image into regions that fit the pre-defined criteria. The algorithm utilized in this thesis is the second category.

This study addresses the problem of particle image segmentation by measuring the similarity between a sampled region and an adjacent region, based on Bhattacharyya distance and an image feature extraction technique that uses distribution of local binary patterns and pattern contrasts. A boundary smoothing process is developed to improve the accuracy of the segmentation. The novel particle image segmentation algorithm is tested using four different cases of particle image velocimetry (PIV) images. The obtained experimental results of segmentations provide partitioning of the objects within 10 percent error rate. Ground-truth segmentation data, which are manually segmented image from each case, are used to calculate the error rate of the segmentations.
ContributorsHan, Dongmin (Author) / Frakes, David (Thesis advisor) / Adrian, Ronald (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2015
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Description
This thesis project is designed to provide brands and prospective sponsors with information about the habits and tendencies of different segments of eSports fans in order to better assist them in making decisions regarding sponsorship deals and sponsorship activation. This thesis has been based off of “The World of Games:

This thesis project is designed to provide brands and prospective sponsors with information about the habits and tendencies of different segments of eSports fans in order to better assist them in making decisions regarding sponsorship deals and sponsorship activation. This thesis has been based off of “The World of Games: eSports” and “the eSports Playbook” studies conducted in 2017 by Goldman Sachs and Nielsen respectively. The goals of these studies were to:

1) provide a clear and coherent picture of different eSports demographics
2) understand the consumption habits and psychological tendencies of these groups
3) use data to create marketing strategies tailor made to each cluster group.

These studies were used as a basis to create personas encompassing the traditional sports affiliations eSports users have, as well as their attitudes towards different types of advertisements.

The goal of this project is to create marketing strategies for different types of brands tailormade to specific groups of eSports fans based on their traditional sports fandom. By testing the fandom overlap of the most popular traditional sports with the most popular eSports games, useful connections that tie both fandoms together can be made for brands. Certain endemic and non-endemic brands can use this data to help decide which industry is a better fit financially and demographically. Other brands will be able to use this data to create strong marketing campaigns that span both eSports and traditional sports leagues, delivering a clear and succinct message across multiple platforms.
ContributorsStrauss, Logan James (Author) / McIntosh, Daniel (Thesis director) / Eaton, John (Committee member) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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Description
Detection of extruded features like rooftops and trees in aerial images automatically is a very active area of research. Elevated features identified from aerial imagery have potential applications in urban planning, identifying cover in military training or flight training. Detection of such features using commonly available geospatial data like orthographic

Detection of extruded features like rooftops and trees in aerial images automatically is a very active area of research. Elevated features identified from aerial imagery have potential applications in urban planning, identifying cover in military training or flight training. Detection of such features using commonly available geospatial data like orthographic aerial imagery is very challenging because rooftop and tree textures are often camouflaged by similar looking features like roads, ground and grass. So, additonal data such as LIDAR, multispectral imagery and multiple viewpoints are exploited for more accurate detection. However, such data is often not available, or may be improperly registered or inacurate. In this thesis, we discuss a novel framework that only uses orthographic images for detection and modeling of rooftops. A segmentation scheme that initializes by assigning either foreground (rooftop) or background labels to certain pixels in the image based on shadows is proposed. Then it employs grabcut to assign one of those two labels to the rest of the pixels based on initial labeling. Parametric model fitting is performed on the segmented results in order to create a 3D scene and to facilitate roof-shape and height estimation. The framework can also benefit from additional geospatial data such as streetmaps and LIDAR, if available.
ContributorsKhanna, Kunal (Author) / Femiani, John (Thesis advisor) / Wonka, Peter (Thesis advisor) / Razdan, Anshuman (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2013
Description
Music, which is easier to access than ever before, has become an integral part of many passionate enthusiasts’ lives. As a fellow music-obsessed listener, I set out to create and sell music-related art to other impassioned fans. This thesis contains every step of the business plan for Album Art By

Music, which is easier to access than ever before, has become an integral part of many passionate enthusiasts’ lives. As a fellow music-obsessed listener, I set out to create and sell music-related art to other impassioned fans. This thesis contains every step of the business plan for Album Art By Andrew, including executive decisions based on primary and secondary research, as well as a projected P&L for the first year of operations.
ContributorsMarkau, Andrew (Author) / Eaton, Kate (Thesis director) / Schlacter, John (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Department of Marketing (Contributor)
Created2023-12