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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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10 papers · 1 filter

cs.CV2022

A novel attention model for salient structure detection in seismic volumes

Muhammad Amir Shafiq, Zhiling Long, Haibin Di +1

A new approach to seismic interpretation is proposed to leverage visual perception and human visual system modeling. Specifically, a saliency detection algorithm based on a novel a…

cs.CV20191 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…