Image Analysis for Shadow Detection, Satellite Image Forensics and Eating Scene Segmentation and Clustering
Abstract
Recent advances in machine learning has enabled notable progress in many aspects of image analysis. In this thesis, we present three applications to exemplify such advancement, including shadow detection, satellite image forensics and eating scene segmentation and clustering. Shadow detection and removal are of great interest to the image processing and image forensics community. In this thesis, we study automatic shadow detection from two different perspectives. First, we propose automatic methods for detecting and removing shadows in color images. Second, we present machine learning based methods to detect if shadows have been removed in an image. In the second part of the thesis, we study image forensics for satellite images. Satellite images have been subjected to various tampering and manipulations due to easy access and the availability of image manipulation tools. In this thesis, we propose methods to automatically detect and localize spliced objects in satellite images. Extracting information from the eating scene captured by images provides new means of studying the relationship between diet and health. In the third part of the thesis, we propose a class-agnostic food segmentation method that is able to segment foods without knowing the food type and a method to cluster eating scene images based on the eating environment.
Degree
Ph.D.
Advisors
Zhu, Purdue University.
Subject Area
Artificial intelligence|Criminology|Logic|Mathematics
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