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Real-time Image Editing and iOS Application with Convolutional Networks

Author
Niu, Ransen
Abstract
This thesis presents a new image editing approach with convolutional networks to automatically alter the image content with a desired attribute and still keep the image photo-realistic. The proposed image editing approach effectively combines the strengths of two prominent images editing algorithms, conditional Generative Adversarial Networks[16] and Deep Feature Interpolation[19], to be time-efficient, memory-efficient, and user-controllable. We also present an inverted deep convolutional network to facilitate the proposed image editing approach. Lastly, we describe the implementation of this image editing approach in an iOS application and demonstrate that this approach is feasible and practical in real-world applications.
Date Issued
2018-05-30Subject
Image Editing; Computer science; Deep Learning; Convolutional Network
Committee Chair
Weinberger, Kilian Quirin
Committee Member
Sine, Wesley
Degree Discipline
Computer Science
Degree Name
M.S., Computer Science
Degree Level
Master of Science
Type
dissertation or thesis