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2.
Acoustic and Image Vehicle Classification System
by Hoang, Trong Tuyen, M.S.  California State University, Long Beach. 2017: 56 pages; 10604463.
3.
Image classification using invariant local features and contextual information
by Bharath, Ramesh, Ph.D.  National University of Singapore (Singapore). 2015: 178 pages; 10006074.
4.
Thermographic Image Analysis as a Pre-Screening Tool for the Detection of Canine Bone Cancer
by Subedi, Samrat, M.S.  Southern Illinois University at Edwardsville. 2014: 131 pages; 1583211.
5.
Optimized Veterinary Thermographic Image Classification using Support Vector Machines and Noise Mitigation
by Lama, Norsang, M.S.  Southern Illinois University at Edwardsville. 2017: 56 pages; 10616467.
6.
Background and Occlusion Defenses Against Adversarial Examples and Adversarial Patches
by McCoyd, Michael J., Ph.D.  University of California, Berkeley. 2020: 75 pages; 28090541.
7.
Gray-cast iron classification based on graphite flakes using image morphology and neural networks
by Yeliyur Siddegowda, Darshan, M.S.  California State University, Long Beach. 2016: 55 pages; 10017846.
8.
Firmware and classification algorithm development for vehicle classification
by Ravindra, Dilip, M.S.  California State University, Long Beach. 2015: 37 pages; 1603749.
9.
Detection of Canine Bone Cancer Using Artificial Neural Networks
by Gorre, Naveena, M.S.  Southern Illinois University at Edwardsville. 2020: 70 pages; 27957796.
11.
Thermographic Image Analysis with Gabor Filters
by Alvandipour, Mehrdad, M.S.  Southern Illinois University at Edwardsville. 2017: 42 pages; 10616514.
12.
Levee slide detection using synthetic aperture radar magnitude and phase
by Marapareddy, Ramakalavathi, Ph.D.  Mississippi State University. 2015: 196 pages; 3737165.
15.
An investigation of data privacy and utility using machine learning as a gauge
by Mivule, Kato, D.Sc.  Bowie State University. 2014: 262 pages; 3619387.
16.
A computer vision approach to classification of circulating tumor cells
by Hopkins, David, M.S.  Colorado State University. 2013: 93 pages; 1539638.
17.
The ALISA Component Module: General shape classification for digital radiographic images
by Oertel, Carsten K., Ph.D.  The George Washington University. 2009: 276 pages; 3338836.
18.
Local and Deep Texture Features for Classification of Natural and Biomedical Images
by Oraibi, Zakariya Ahmed, Ph.D.  University of Missouri - Columbia. 2019: 135 pages; 22583771.
19.
Machine Learning Methods for 3D Object Classification and Segmentation
by Le, Truc Duc, Ph.D.  University of Missouri - Columbia. 2018: 158 pages; 13877153.
20.
An Application of Deep Learning Models to Automate Food Waste Classification
by Espinoza, Alejandro Zachary, M.S.  Portland State University. 2019: 136 pages; 27662936.
21.
Rapid Classification of Imaged Objects Using Molecular Factor and Multivariate Optical Computing
by Pearl, Megan Renee, Ph.D.  University of South Carolina. 2011: 199 pages; 3469163.
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23.
Geometric representations and deep Gaussian conditional random field networks for computer vision
by Vemulapalli, Raviteja, Ph.D.  University of Maryland, College Park. 2016: 185 pages; 10192530.
24.
Efficient sensing, summarization and classification of videos
by Shroff, Nitesh, Ph.D.  University of Maryland, College Park. 2012: 240 pages; 3543609.
25.
Advanced neural network clustering techniques for liquid crystal texture classification
by Karaszi, Zoltan, M.C.Sc.  Kent State University. 2013: 71 pages; 1555292.
28.
Graphical User Interface for the CVIP MATLAB Toolbox with Application to Syrinx Detection in Veterinary Thermographic Images
by Cuellar Buritica, Julian Rene, M.S.  Southern Illinois University at Edwardsville. 2019: 132 pages; 13900325.
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