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20182022
most citedSubsurface structure analysis using computational interpretation and learning: A visual signal processing perspective

74 citations · 139 across the 14 of their papers we have counts for

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Showing 2019 · cs.CVShow all

6 papers · 2 filters

cs.CV2019★ 1 cited

Multi-level Texture Encoding and Representation (MuLTER) based on Deep Neural Networks

Yuting Hu, Zhiling Long, Ghassan AlRegib

In this paper, we propose a multi-level texture encoding and representation network (MuLTER) for texture-related applications. Based on a multi-level pooling architecture, the MuLT…

cs.CV2019

Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons

Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi +1

In this paper, we propose a novel approach for saliency detection for seismic applications using 3D-FFT local spectra and multi-dimensional plane projections. We develop a projecti…

cs.CV2019

Understanding spatial correlation in eye-fixation maps for visual attention in videos

Tariq Alshawi, Zhiling Long, Ghassan AlRegib

In this paper, we present an analysis of recorded eye-fixation data from human subjects viewing video sequences. The purpose is to better understand visual attention for videos. Ut…

cs.CV2019

Characterization of migrated seismic volumes using texture attributes: a comparative study

Zhiling Long, Yazeed Alaudah, Muhammad Ali Qureshi +5

In this paper, we examine several typical texture attributes developed in the image processing community in recent years with respect to their capability of characterizing a migrat…

cs.CV2019

SalSi: A new seismic attribute for salt dome detection

Muhammad Amir Shafiq, Tariq Alshawi, Zhiling Long +1

In this paper, we propose a saliency-based attribute, SalSi, to detect salt dome bodies within seismic volumes. SalSi is based on the saliency theory and modeling of the human visi…

cs.CV2019★ 19 cited

Unsupervised uncertainty estimation using spatiotemporal cues in video saliency detection

Tariq Alshawi, Zhiling Long, Ghassan AlRegib

In this paper, we address the problem of quantifying reliability of computational saliency for videos, which can be used to improve saliency-based video processing and enable more…