Understanding Perceived Sense of Movement in Static Visuals Using Deep Learning

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dc.contributor.advisorDou, Dejing
dc.contributor.authorKale, Shravan
dc.date.accessioned2019-01-11T22:27:08Z
dc.date.available2019-01-11T22:27:08Z
dc.date.issued2019-01-11
dc.description.abstractThis thesis introduces the problem of learning the representation and the classification of the perceived sense of movement, defined as dynamism in static visuals. To solve the said problem, we study the definition, degree, and real-world implications of dynamism within the field of consumer psychology. We employ Deep Convolutional Neural Networks (DCNN) as a method to learn and predict dynamism in images. The novelty of the task, lead us to collect a dataset which we synthetically augmented for spatial invariance, using image processing techniques. We study the methods of transfer learning to transfer knowledge from another domain, as the size of our dataset was deemed to be inadequate. Our dataset is trained across different network architectures, and transfer learning techniques to find an optimal method for the task at hand. To show a real-world application of our work, we observe the correlation between the two visual stimuli, dynamism and emotions.en_US
dc.identifier.urihttps://hdl.handle.net/1794/24184
dc.language.isoen_US
dc.publisherUniversity of Oregon
dc.rightsCreative Commons BY-NC-SA 4.0-US
dc.subjectComputer Visionen_US
dc.subjectDeep learningen_US
dc.titleUnderstanding Perceived Sense of Movement in Static Visuals Using Deep Learning
dc.typeElectronic Thesis or Dissertation
thesis.degree.disciplineDepartment of Computer and Information Science
thesis.degree.grantorUniversity of Oregon
thesis.degree.levelmasters
thesis.degree.nameM.S.

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