Three Problems in Digital Image Processing: Alignment of Data-Bearing Halftone Images, Surface Coding, and Matching Consumer Photos of Fashion Items with On-Line Images
Abstract
Digital image processing techniques have many significant applications in industry. In this thesis, we focus on three problems in digital image processing. These three problems involve halftone images, information encoding and decoding, image alignment, and deep learning. Specifically, the first problem is based on data-bearing halftone images, which are an aesthetically pleasing alternative to barcodes. We address the issues generated in the cameracaptured image alignment process. We perform some theoretical analysis and validate it by simulation. We also provide an optimal solution to the problem. The second problem is about the alignment technique on a 3D surface. We develop a pipeline of surfaces coding to solve the alignment issues on 3D surfaces, which includes oblique surfaces and cylindrical surfaces. The third problem is related to image retrieval. We propose a deep learning based solution to the fashion image retrieval task. Fashion image retrieval is significant to improve the customers’ experience in online shopping. A fast, accurate shopping item information retrieval system based on the customers’ uploaded image has been built by us. A novel solution is provided, and it achieves state-of-art accuracy in shopping items’ information retrieval.
Degree
Ph.D.
Advisors
Allebach, Purdue University.
Subject Area
Artificial intelligence|Design|Mathematics
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