activity
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

collaborators

17 papers

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…

eess.IV2020

Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset

Yuting Hu, Zhiling Long, Anirudha Sundaresan +4

Tactile sensing or fabric hand plays a critical role in an individual's decision to buy a certain fabric from the range of available fabrics for a desired application. Therefore, t…

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…

eess.IV201915 cited

Multiresolution Analysis and Learning for Computational Seismic Interpretation

Motaz Alfarraj, Yazeed Alaudah, Zhiling Long +1

We explore the use of multiresolution analysis techniques as texture attributes for seismic image characterization, especially in representing subsurface structures in large migrat…

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…