Abstract
Estimating the geometry relation between two images is one of the key steps for various computer vision tasks including visual tracking, 3D reconstruction from multiple images, optical flow estimation, and camera calibration. The error in the estimation phase can propagate into final accuracy of the aforementioned task, therefore reducing the estimation error has important practical implications. The first step in geometry transformation is establishing point correspondence between two images. Even though, the robust estimators can prune out some outliers later in the calculation pipeline, higher matching accuracy leads to better estimate. In this thesis, we demonstrate how use of multiple level of refinement in multiple scales can increase the accuracy of keypoint matching. Next, we show that the invariant geometry of matches can help us match the images between two images of the same object under different view points. The view invariant parameter that we examine in this thesis is the difference between eigenvalues of homography matrix induces by triplets of points between images. Every triplet of points in the image induce a homography matrix. In the special case of two cameras viewing the same scene under different view points, the homography is reduced to the special case of homology with two equal eigenvalues. This enables us to define a error measurement for the task of template matching with application in view-invariant object retrieval and face recognition. Finally, we discuss the possible attacks on the pipeline of estimating transformation between two images. We propose a method that targets the extraction and matching phases of the pipeline in order to derail the calculation process. Small perturbation is applied on the image patches so that the output geometry matrix is wrong. The magnitude of distortion in the images are kept limited so that human eyes are unable to distinguish the perturbed image. The attack can be conducted on either one (single image attack) or both images (dual image attack).
Notes
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Graduation Date
2023
Semester
Summer
Advisor
Foroosh, Hassan
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Computer Science
Degree Program
Computer Science
Identifier
CFE0009890; DP0028423
URL
https://purls.library.ucf.edu/go/DP0028423
Language
English
Release Date
February 2029
Length of Campus-only Access
5 years
Access Status
Doctoral Dissertation (Campus-only Access)
STARS Citation
Lotfian, Sina, "View Invariant Point Matching with Applications to Object Recognition" (2023). Electronic Theses and Dissertations, 2020-2023. 1919.
https://stars.library.ucf.edu/etd2020/1919
Restricted to the UCF community until February 2029; it will then be open access.