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- Member of: ASU Electronic Theses and Dissertations
Description
Many communities that once relied on the extractive industries have since turned to tourism to find another source of income. These communities are primarily old mining towns. Since these towns have started to reinvent themselves, they have become important places of study. Previous literature has found specific factors that are common in tourist shopping villages. Currently, there is not much research that has explored the affect the streetscape and servicescape have on visitor experiences. Existing research focuses on urban shopping settings such as shopping malls. This study interviewed employees and surveyed visitors in two suburban tourist shopping villages in Arizona. More specifically, it is aimed to explore how the streetscapes and servicescapes in tourist shopping villages influence visitors' overall experience, intent to return to the village, and their purchasing behavior. This study adds to the current literature on tourist shopping villages and the streetscapes and servicescapes as there is a limited amount of information available. To date, the majority of scholarly information available describes the factors of tourist shopping villages and does not attempt to identify their importance for tourists. This study may serve as a stepping platform for future research. The findings of this study offer important implications for destination marketing organizations, different stakeholders of tourism, and the policy makers. This study primarily focuses on the tourists' view of tourist shopping villages, and can offer insight into how to increase visitor spending.
ContributorsCox, Jordan (Author) / Timothy, Dallen J. (Thesis advisor) / Chhabra, Deepak (Thesis advisor) / Carlson, David L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Over the past decade, advancements in neural networks have been instrumental in achieving remarkable breakthroughs in the field of computer vision. One of the applications is in creating assistive technology to improve the lives of visually impaired people by making the world around them more accessible. A lot of research in convolutional neural networks has led to human-level performance in different vision tasks including image classification, object detection, instance segmentation, semantic segmentation, panoptic segmentation and scene text recognition. All the before mentioned tasks, individually or in combination, have been used to create assistive technologies to improve accessibility for the blind.
This dissertation outlines various applications to improve accessibility and independence for visually impaired people during shopping by helping them identify products in retail stores. The dissertation includes the following contributions; (i) A dataset containing images of breakfast-cereal products and a classifier using a deep neural (ResNet) network; (ii) A dataset for training a text detection and scene-text recognition model; (iii) A model for text detection and scene-text recognition to identify product images using a user-controlled camera; (iv) A dataset of twenty thousand products with product information and related images that can be used to train and test a system designed to identify products.
This dissertation outlines various applications to improve accessibility and independence for visually impaired people during shopping by helping them identify products in retail stores. The dissertation includes the following contributions; (i) A dataset containing images of breakfast-cereal products and a classifier using a deep neural (ResNet) network; (ii) A dataset for training a text detection and scene-text recognition model; (iii) A model for text detection and scene-text recognition to identify product images using a user-controlled camera; (iv) A dataset of twenty thousand products with product information and related images that can be used to train and test a system designed to identify products.
ContributorsPatel, Akshar (Author) / Panchanathan, Sethuraman (Thesis advisor) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020