Research – Google Scholar Page

● Z Todd, Li H et al. An Improved Collection Method of High-Resolution Pavement Images and Deep Learning Models for Pavement Distress Detection. Special Report – National Research Council, Transportation Research Board (2024)
● Li, H, and et. al. 3D Material Property Tomographic Reconstruction Using Near-Field Phase Retrieval for Dual-Energy Synchrotron X-ray Imaging, ICTMS (2024)
● H Li, and et. al. Equirectangular Image Data Detection, Segmentation and Classification of Varying Sized Traffic Signs: A Comparison of Deep Learning Methods, Sensors (2023)
● Li H, and et. al. 3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation, The Visual Computer (2022)
● S Li, Li H and et. al. “Shallow U-Net deep learning approach for phase retrieval in propagation-based phase-contrast Imaging”, Proc. SPIE (2022)
● KA Maliszewski, Li H, and et. al. Dispersion-contrast imaging using machine learning, Optical Coherence Tomography and Coherence Domain Optical Methods (2022)
● KM Pavlov, Li H, and et. al. Directional dark-field implicit x-ray speckle tracking using an anisotropic-diffusion Fokker-Planck equation, Physical Review (2021)
● Alloo SJ, Li H, and et. al. Speckle-based x-ray dark-field tomography of an attenuating object, Developments in X-Ray Tomography (2021)
● Pavlov KM, Li H, and et. al. X-ray multi-modal intrinsic-speckle-tracking, Optics (2020)
● Li H, and et. al. – Quantitative material decomposition using linear iterative near-field phase retrieval dual-energy x-ray imaging Physics in Medicine & Biology (2020)
● Pavlov KM, Li H, and et. al. – Single-shot x-ray speckle-based imaging of a single-material object, Physical Review Applied (2020)
● Herbst L, Li T, and Steel M. – Quantifying the accuracy of ancestral state prediction in a phylogenetic tree under maximum parsimony, Journal of Mathematical Biology (2019).
● Li H, and et. al. – Linear Iterative Near-Field Phase Retrieval (LI-PR) for Multi-Energy X-ray Imaging and Material Discrimination, Journal of the Optical Society of America A (2018).
● Kingston A, Li H, and et. al. – Space-filling X-ray source trajectories for efficient scanning in large-angle cone-beam CT, IEEE Transactions on Computational Imaging (2018).
● Li H, and Candy R – Optimising Open-Pit Mining Using Multi-Grid Search, Modsim (2017).
● Bentley M, and Li H, and et. al. – Quantitative Cyber Risk Model and Optimal Mitigation, Modsim (2017).
● Li H, and et. al. – Linear Iterative Phase Retrieval for Dual-Energy X-ray Imaging, ICTMS (2017).
● Li H, and et. al. – Atomic Resolution Binary Tomography of Gold Nanorod Surface Morphology, ICTMS (2017).
● Kingston A, Li H, and et. al. – Optimised x-ray source scanning trajectories for iterative reconstruction in high cone-angle tomography, SPIE Optical Engineering (2016).
● Myers G, Li T, and et. al. – Rapidly-converging multigrid reconstruction of cone-beam tomographic data, SPIE Optical Engineering (2016).
● Li H, and et. al. – 3D X-Ray Source Deblurring in High Cone-Angle Micro-CT, IEEE Transaction on Nuclear Science (2015).
● Li H, and et. al. – Improving spatial-resolution in high cone-angle micro-CT by source deblurring, Developments in X-Ray Tomography IX (2014).
● Kingston A, Li H, and et. al. – Fourier inversion of the mojette transform, 18th IAPR International Conference, Lecture Notes in Computer Science (LNCS): DGCI 2014 8668 (2014).
● Mooers A, Li H, and et. al. – Branch lengths on birth-death trees and the expected loss of phylogenetic diversity, Systematic biology (2012).