Gurman Gill - Research

Research Overview

I am broadly interested in learning visual phenomenon from images by designing features and employing classification techniques. My overarching goal is to involve students to employ image analysis and learning for tasks originating in different STEM fields. Towards that goal, I am currently working on projects involving Computed Tomography (CT) scans of human lungs, microscopic images of geological rocks (in collaboration with Dr. Matty Mookerjee, Department of Geology) and digital images of animals in the wild (in collaboration with Dr. Chris Halle at the Center for Environmental Inquiry). More information regarding these projects can be found here. SSU students who are interested in these projects are welcome to contact me!

Publication List

  • Mookerjee, M., Chan, M.A., Gil, Y., Gill, G., Goodwin, C., Pavlis, T.L., Shipley, T.F., Swain, T., Tikoff, B. and Vieira, D. Cyberinfrastructure for collecting and integrating geology field data: Community priorities and research agenda. Recent Advancement in Geoinformatics and Data Science, 2023 Jan 25.
  • Quinn, C.A., Burns, P., Gill, G., Baligar, S., Snyder, R.L., Salas, L., Goetz, S.J. and Clark, M.L., 2022. Soundscape classification with convolutional neural networks reveals temporal and geographic patterns in ecoacoustic data. Ecological Indicators, 138, p.108831. (PDF link)
  • J. Chavez, M. Clark and G.Gill, Utilizing Deep Learning for Mapping Dozer Lines from Aerial Imagery, Computer Science Conference for CSU Undergraduates, CSCSU, April 2022. (PDF link)
  • J. Granados, C. Halle and G. Gill, Classifying False Alarms In Camera Trap Images Using Convolutional Neural Networks, 2020 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA Dec. 2020. (PDF link)
  • J. B. Martinez and G. Gill, "Comparison of Pre-Trained vs Domain-Specific Convolutional Neural Networks for Classification of Interstitial Lung Disease," 2019 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2019, pp. 991-994. (PDF link)
  • G. Gill and R.R. Beichel, An approach for reducing the error rate in automated lung segmentation, Computers in Biology and Medicine, vol. 76, pages 143-153, Sep. 2016. (PDF link)
  • G. Gill and R. R. Beichel, Lung Segmentation in 4D CT Volumes based on Robust Active Shape Model Matching, International Journal of Biomedical Imaging, vol. 3015, Article ID 125648, 9 pages, Sep. 2015. (PDF link)
  • G. Gill and R. R. Beichel, Segmentation of Lungs with Interstitial Lung Disease in CT Scans: A TV-L1 Based Texture Analysis Approach, Advances in Visual Computing, LNCS 8887, pp. 511-520, 2014. (PDF link)
  • G. Gill, M. Toews and R. R. Beichel, Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach, International Journal of Biomedical Imaging, vol. 2014, Article ID 479154, 7 pages, 2014. doi:10.1155/2014/479154. (PDF link)
  • G. Gill, C. Bauer and R. R. Beichel, A Method for Avoiding Overlap of Left and Right Lungs in Shape Model Guided Segmentation of Lungs in CT Volumes, Medical Physics, Vol. 41, 101908, 2014, doi: 10.1118/1.4894817. (PDF link)
  • G. Gill, M. Toews and R. R. Beichel, An Automated Initialization System for Robust Model-Based Segmentation of Lungs in CT Data, 5th International Workshop on Pulmonary Image Analysis, pp. 111-122, 2013.
  • G. Gill and M. Levine, Multi-View Object Detection based on Spatial Consistency in a Low Dimensional Space, German Association for Pattern Recognition, LNCS 5748, pp. 211-220, 2009. (PDF link)
  • G. Gill and M. Levine, Incorporating Shape Features in an Appearance-Based Object Detection System, Computer Analysis of Images and Patterns, LNCS 5702, pp. 269-276, 2009. (PDF link)
  • G. Gill and M. Levine, A Single Classifier for View-Invariant Multiple Object Class Recognition, British Machine Vision Conference, volume 1, pages 257-266, 2006. (PDF link)

Posters with undergraduate/high school students

  • L. Carmona, J.Thomas, M.Mookerjee and G.Gill, Deriving Crystallographic Segmentation through Image Processing, 38th Annual CSU Student Research Competition, Cal Poly San Luis Obispo, April 2024.
  • T. Whitmarsh, M.Clark, and G.Gill, Predicting Canopy Detection in Sonoma County using Convolutional Neural Networks, 37th Annual CSU Student Research Competition, San Diego State University, Pomona, April 2023.
  • Iota, S., Liu, J., Lyu, M., Pan, B., Wang, X., Gil, Y., AbdAlmageed, W., Gill, G. and Mookerjee, M., 2021, December. Automatic Detection and Classification of Rock Microstructures through Machine Learning. In AGU Fall Meeting Abstracts (Vol. 2021, pp. EP15H-08). (Web link)
  • A. Encarnacion, C. Rosales, K. Drake, M. Mookerjee and G.Gill, A Pipeline for Automatically Classifying Shear-Sense Indicating Clasts in Photomicrographs, Virtual Poster presentation at NSF EarthCube Annual Meeting, Jun. 2021.(Poster from Brown, Jed; Schreiber, Lynne; Cramer, Catherine (2021). 2021 EarthCube Annual Meeting - Poster Session. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5436438.v1. This work was also presented at SSU Symposium April 2021)
  • A. Dewey, J. Calderon Chavez, V. Valenzuela, A. Silveria, C. Quinn, M. Clark and G.Gill, Using Machine Learning to Measure Biodiversity from Sound Recordings, 35th Annual CSU Student Research Competition, California State University Polytechnic, Pomona, April 2021. (Winner in the Engineering and Computer Science category)
  • A. Encarnacion, B. Katin, M. Mookerjee and G.Gill, Building a Geological Cyber-infrastructure: Automatically Detecting Clasts in Photomicrographs, Poster presentation at EarthCube Annual Meeting (San Diego), held virtually due to Covid-19, Jun. 2020. (Web link) (Pre-print)
  • J. Rodriguez and G.Gill, Wildlife Location and Classification Through YOLO and CNNs, 2020 Sonoma State University Virtual Research Gallery, May 2020. (Web link)
  • J. Robinson, M. Mookerjee and G.Gill, Building a Geological Cyber-infrastructure: Classifying Shear Sense of Clasts in Photomicrographs, Poster presentation at American Geophysical Union (AGU) Fall Meeting, San Francisco, Dec. 2019. (Web link)
  • J. Bautista-Martinez, M. Mookerjee and G.Gill, Building a Geological Cyber-infrastructure: Classifying Clasts in Photomicrographs, Poster presentation at EarthCube Annual Meeting, Denver, June 2019. (PDF link)
  • J. Robinson, C. Meyer and G.Gill, A web-framework for bringing machine learning to the everyday user, SSU Science Symposium of Research and Creativity, April 2019. (PDF link)
  • J. Bautista-Martinez, S. Penna and G.Gill, Applications of Convolutional Neural Network Model for classifying Interstitial Lung Disease images from Computed Tomography scans. Selected to represent Sonoma State University (acceptance rate ~43%) at the 33rd Annual CSU Student Research Competition, CSU Fullerton, April 2019. (PDF link)
  • S. Penna, C. Havranek and G.Gill, A computational framework based on convolutional neural network for classifying interstitial lung disease in computed tomography scans, Student poster presentation at CSUPERB annual symposium (acceptance rate ~74% across all CSUs), Jan 2019. (PDF link)
  • B. Cogan, M. Puryear and G.Gill, Towards building a geological cyber-infrastructure: classifying sigma-clast images in photomicrographs, Student poster presentation at CCSC Southwest Region conference, March 2018. Recipient of Best poster award. (PDF link)
  • J. Meixensperger, S. Perry and G.Gill, Performance of traditional image processing and convolutional neural network in classifying interstitial lung disease, Student poster presentation at CCSC Southwest Region conference, March 2018 (Recipient of 2nd Best poster award) and SSU Science Symposium of Research and Creativity, May 2018 (Recipient of the “Bright Idea” award). (PDF link)
  • J. Hagle and G.Gill, Using pre-trained convolutional neural networks to classify wildlife animals, Student poster presentation at CCSC Southwest Region conference, March 2018. (PDF link)
  • J. Granados and G.Gill, Using pre-trained convolutional neural networks to classify interstitial lung diseases in computed tomography scans, Student poster presentation at CSUPERB annual symposium (acceptance rate ~71% across all CSUs), January 2018. (PDF link)
  • S. Nadendla, J. Granados and G. Gill, Using Deep Learning to Classify Animals in the Wild, Student poster presentation at SHIP research symposium, September 2017. (PDF link)
  • C. Calloway, A. Pineda and G. Gill, Object Classification using machine learning on fMRI scans, Student poster presentation at SSU research symposium, May 2017. (PDF link)
  • N. Shively, G. Gill and R. Balakumar, EEG Signal Processing: Understanding Brainwaves through Machine Learning, Student poster presentation at CSU research competition, April 2016
  • A. Smith and G. Gill, Computational classification techniques for neuroimaging: A machine learning based approach, Student poster presentation at CSU research competition, April 2016

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