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PhD Defense - Wei Quan
Wed, Jan 15, 2014 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
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PhD Candidate: Wei Quan
Title: Hybrid Methods for Robust Image Matching and Its Application in Augmented Reality
Committee:
Prof. Suya You (chair)
Prof. Ulrich Neumann
Prof. C. C. Jay Kuo (outside member)
This thesis presents new matching algorithms that work robustly in challenging situations. Image matching is a fundamental and challenging problem in vision community due to varied sensing techniques and imaging conditions. While it is almost impossible to find a general method that is optimized for all uses, we focus on those matching problems that are related to augmented reality (AR). Many AR applications have been developed on portable devices, but most are limited to indoor environments within a small workspace because their matching algorithms are not robust out of controlled conditions.
The first part of the thesis describes 2D to 2D image matching problems. Existing robust features are not suited for AR applications due to their computational cost. A fast matching scheme is applied to such features to increase matching speed by up to 10 times without sacrificing their robustness. Lighting variations can often cause match failures in outdoor environments. It is a challenging problem because any change in illumination causes unpredicted changes in image intensities. Some features have been specially designed to be lighting invariant. While these features handle linear or monotonic changes, they are not robust to more complex changes. This thesis presents a line-based feature that is robust to complex and large illumination variations. Both feature detector and descriptor are described in more detail.
The second part of the thesis describes image sequence matching with 3D point clouds. Feature-based matching becomes more challenging due to different structures between 2D and 3D data. The features extracted from one type of data are usually not repeatable in the other. An ICP-like method that iteratively aligns an image with a 3D point cloud is presented. While this method can be used to calculate the pose for a single frame, it is not efficient to apply it for all frames in the sequence. Once the first frame pose is obtained, the poses for subsequent frames can be tracked from 2D to 3D point correspondences. It is observed that not all points on LiDAR are suitable for tracking. A simple and efficient method is used to remove unstable LiDAR points and identify features on frames that are robust in the tracking process. With the above methods, the poses can be calculated more stably for the whole sequence.
With provided solutions to above challenging problems, we have applied our methods in an AR system. We describe each step in building up such a system from data collections and preprocessing, to pose calculations and trackings. The presented system is shown to be robust and promising for most AR-based applications.
Location: Charles Lee Powell Hall (PHE) - 333
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
