Zhiling Long, Ph.D.
Affiliations: | 2007 | Electrical and Computer Engineering | Mississippi State University, Starkville, MS, United States |
Area:
Electronics and Electrical EngineeringGoogle:
"Zhiling Long"Parents
Sign in to add mentorNicolas H. Younan | grad student | 2007 | Mississippi State University | |
(Statistical image modeling in the contourlet domain with application to texture segmentation.) |
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Publications
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Alfarraj M, Alaudah Y, Long Z, et al. (2018) Multiresolution analysis and learning for computational seismic interpretation The Leading Edge. 37: 443-450 |
Long Z, Alaudah Y, Ali Qureshi M, et al. (2018) A comparative study of texture attributes for characterizing subsurface structures in seismic volumes Interpretation. 6: T1055-T1066 |
Hu Y, Long Z, AlRegib G. (2018) A High-Speed, Real-Time Vision System for Texture Tracking and Thread Counting Ieee Signal Processing Letters. 25: 758-762 |
Shafiq MA, Alshawi T, Long Z, et al. (2017) The role of visual saliency in the automation of seismic interpretation Geophysical Prospecting. 66: 132-143 |
Long Z, Alaudah Y, Qureshi MA, et al. (2015) Characterization of Migrated Seismic Volumes using Texture Attributes: A Comparative Study Seg Technical Program Expanded Abstracts |
Wang Z, Hegazy T, Long Z, et al. (2015) Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning Geophysics. 80: WD101-WD116 |
Long Z, Alregib G. (2015) Saliency detection for videos using 3D FFT local spectra Proceedings of Spie - the International Society For Optical Engineering. 9394 |
Long Z, Wei W, Turlapaty A, et al. (2013) Fusion of Radiation and Electromagnetic Induction Data for Buried Radioactive Target Detection and Characterization Ieee Transactions On Nuclear Science. 60: 1126-1133 |
Long Z, Du Q, Younan NH. (2013) Multiscale spectral-spatial classification for hyperspectral imagery International Geoscience and Remote Sensing Symposium (Igarss). 1051-1054 |
Long Z, Younan NH. (2013) Multiscale texture segmentation via a contourlet contextual hidden Markov model Digital Signal Processing: a Review Journal. 23: 859-869 |