Ohio University

Learning & Intelligent Systems Lab

Journal Articles, Conference Proceedings and Dissertations/Theses

2025

  1. Majumder, R., Wang, Z., Yue, Y., Kalita, M., Liu, J. (2025), Enforcing Graph Structures to Enhance Key Information Extraction in Document Analysis. 2025 VisAPP Conference.
  2. Nagura, D., Bihl, T., Liu, J. (2025), Reinforcement Learning with Human Experience (RLHE) for Racing Car Games. 2025 ASEE Conference.

2024

  1. Wang, J., Liu, J. (2024) Semi-Supervised Learning and Focal Masking for Vessel Segmentation in X-ray Coronary Angiography. IEEE ICMLA'2024. [link] [bib]
  2. Nagura, D., Bihl, T., Liu, J. (2024) Boosting Race Car Performance Through Reinforcement Learning from Ai Feedback (RLAIF). [link] [bib]
  3. Qin, X., Song, S., Brengman, J., Bartone, C., & Liu, J. (2024) Robust FOD Detection using Frame Sequence-based DEtection TRansformer (DETR) (2024). IEEE Conference on Artificial Intelligence. [link] [bib]
  4. Bahamondes Lorca, V. A., Ávalos-Ovando, O., Sikeler, C., Ijäs, H., Santiago, E. Y., Skelton, E., ... & Govorov, A. O. (2024). Lateral Flow Assay Biotesting by Utilizing Plasmonic Nanoparticles Made of Inexpensive Metals─ Replacing Colloidal Gold. Nano Letters. [link] [bib
  5. Abishek Lakandri, "Exploring GANs with Conv-TasNet: Adversarial Training for Speech Separation (2024)", MSCS Thesis [link]
  6. Di Caterina, G., Zhang, M., & Liu, J. (2024). Theoretical advances and practical applications of spiking neural networks. Frontiers in Neuroscience, 18, 1406502. [link]
  7. Bihl, T., Farr, P., Di Caterina, G., Vicente-Sola, A., Manna, D., Kirkland, P., ... & Combs, K. (2024). Exploring spiking neural networks (SNN) for low Size, Weight, and Power (SWaP) benefits. [pdf]

2023

  1. Yue, Y., Baltes, M., Abuhajar, N., ... & Liu, J. (2023). Spiking neural networks fine-tuning for brain image segmentation. Frontiers in Neuroscience, 17. [pdf]
  2. Song, S., Qin, X., Brengman, J., Bartone, C., & Liu, J. (2023). Holistic FOD Detection Via Surface Map and Yolo Networks. In 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE. [pdf] [bib]
  3. Zhang, Y., Liu, J. (2023). Vertex-based Networks to Accelerate Path Planning Algorithms. IEEE Interntional Workshop on Machine Learning for Signal Processing (MLSP'23) (pp. 1-6), IEEE. [pdf]
  4. Baltes, M., Abujahar, Yue, Y., T., Smith, C. D., Liu, J. (2023). Joint ANN-SNN Co-training for Object Localization and Image Segmentation. IEEE Interntional Conference on Acoustics, Speech, and Signal Processing (ICASSP'23) [pdf]
  5. Yue, Y., Baltes, M., Abujahar, N., Sun, T., Smith, C. D., Bihl, T., & Liu, J. (2023). Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation. IEEE International Symposium on Biomedical Imaging (ISBI'23) [pdf]
  6. Marc Batles, Hybrid ANN-SNN Co-Training for Object Localization and Image Segmentation, M.S. thesis, April 2023. [OhioLINK]

2022

  1. Tao Sun, Time-domain Deep Neural Networks for Speech Separation, PhD dissertation, May 2022. [OhioLINK]
  2. Song, S., Saunders, K., Yue, Y., & Liu, J. (2022). Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning. IEEE ICMLA'22. [pdf]
  3. Sun, T., Abuhajar, N., Gong, S., Wang, Z., Smith, C. D., Wang, X., ... & Liu, J. (2022). Individualized Conditioning and Negative Distances for Speaker Separation. IEEE ICMLA'22 [pdf]
  4. Liao, B., Chen, Y., Wang, Z., Smith, C. D., & Liu, J. (2022). A Comparative Study on 1.5 T-3T MRI Conversion through Deep Neural Network Models. IEEE ICMLA'22 [pdf]
  5. Shuyu Gong, Listening Longer to Hear Better: Dilated FCNs for Speech Enhancement, M.S. Thesis, May 2022. [pdf]

2021

  1. Colton C. Smith, The Evaluation of Current Spiking Neural Network Conversion Methods in Radar Data, M.S. thesis, Aug. 2021. [OhioLINK]
  2. Sun et al,, Boosting the Intelligibility of Speech Enhancement Networks Through Self-Supervised Representations), IEEE ICMLA'21, [[pdf]]
  3. Abujahar et al, Network Compression and Frame Stitching for Efficient and Robust Speech Enhancement, NAECON 2021 [pdf]
  4. Smith et al, Evaluation of Spiking Neural Networks in Radar, NAECON 2021 [link] [pdf]
  5. Song et al, Vision-based Collision Avoidance through Deep Reinforcement Learning, IEEE NAECON 2021 [pdf]
  6. McGee et al, Network Fusion for Radar Emitter Detection, NAECON 2021 [link] [pdf]

2020

  1. Zhewei Wang, Fully Convoultional Networks (FCNs) for Medical Image Segmentation, December, 2020, [OhioLINK]
  2. Wang et al, Wang et. al, QuPath Pipeline for Accurate Cell and Colloid Segmentation, Cytometry A [link][pdf]
  3. Yiran Liu, Consistent and Accurate Face Tracking and Recognition in Videos, M.S. Thesis, May 2020. [OhioLINK]

2019

  1. Yani Chen, Deep Learning based 3D Image Segmnetation Methods and Applications, Ph.D. disseratation, May 2019. [OhioLINK]
  2. Gong, S., Wang, Z., Sun, T., Zhang, Y., Smith, C., Xu, L., Liu, J. (2019). Dilated FCN: Listening Longer to Hear Better. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA'19) ;[link]
  3. Wang, Z., Cai, W., Rudmann, D., Liu, J., Rosol, T. (2019). Automatic Segmentation of Thyroid Colloid and Follicular Cells through QuPath Srcripting. STP 38th Annual Symposium Environmental Toxicologic Pathology and One Health;[link]
  4. Wang, Z., Cai, W., Smith, C., Kantake, N., Rosol, T., Liu, J. (2019). Residual Pyramid FCN for Robust Follicle Segmentation. 2019 IEEE International Symposium on Biomedical Imaging (ISBI 2019);[link]

2018

  1. Shi, B., Liu, J. (2018). Nonlinear Metric Learning for kNN and SVMs through Geometric Transformations. Neurocomputing;[link]
  2. Wang, Z., Cai, W., Kantake, N., Liu, J., Rosol, T. (2018). Neural Networks and Deep Learning to Develop Algorithms for Automated Image Analysis of Thyroid Hypertrophy. 2018 ACVP Annual Meeting;[link]
  3. Wang, Z., Smith, C., Liu, J. (2018). Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation. 2018 International Conference on Machine Learning in Medical Imaging;[link]
  4. Wang, Z., Shi, B., Smith, C., Liu, J. (2018). Nonlinear Metric Learning through Geodesic Interpolation within Lie Groups. International Conference on Pattern Recognition (ICPR'2018);[link]
  5. Chen, Y., Shi, B., Zhang, P., Smith, C., Liu, J. (2018). Multi-modal Feature Fusion via Deep Networks for AD/MCI Diagnosis. 2018 IEEE International Symposium on Biomedical Imaging (ISBI'2018).
  6. Chen, Y., Wang, Z., Smith, C., Liu, J. (2018). 3D Brain Tumor Segmentation via Sequential FCN. 2018 IEEE International Symposium on Biomedical Imaging (ISBI'2018).

2017

  1. Zhang, P., Shi, B., Smith, C., Liu, J. (2017). Learning Feature Transformations to Improve Semi-Supervised Classification. Pattern Recognition.
  2. Shi, B., Chen, Y., Zhang, P., Smith, C., Liu, J. (2017). Nonlinear feature transformation and deep fusion for Alzheimer's Disease staging analysis. Frankfurt, D60486 Germany: Pattern Recognition; 63: pp. 487-498. [ link ]
  3. Chen, Y., Shi, B., Wang, Z., Sun, T., Smith, C., Liu, J. (2017). Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble. Machine Learning on Medical Imaging;[link]
  4. Zhang, P., Shi, B., Smith, C., Liu, J. (2017). Nonlinear Feature Space Transformation to Improve the Prediction of MCI to AD Conversion. Medical Image Computing and Computer Assisted Interventions Conference (MICCAI' 2017);[link]
  5. Chen, Y., Shi, B., Zhang, P., Smith, C., Wang, Z., Liu, J. (2017). Hippocampus Segmentation through Multi-view Ensemble ConvNets. 2017 IEEE International Symposium on Biomedical Imaging (ISBI 2017);[link]

2016

  1. Hobbs, K., Zhang, P., Shi, B., Smith, C., Liu, J. (2016). Quad-mesh Coordinate Modeling and its applications in Neuroimages. computerized graphics medical imaging.
  2. Zhang, P., Shi, B., Smith, C., Liu, J. (2016). Nonlinear Metric Learning for Semi-Supervised Learning via Coherent Point Drifting . IEEE International Conference on Machine Learning and Applications (ICMLA'2016);[link]
  3. Zhang, P., Shi, B., Smith, C., Liu, J. (2017). Nonlinear Feature Space Transformation to Improve the Prediction of MCI to AD Conversion. Medical Image Computing and Computer Assisted Interventions Conference (MICCAI' 2017);[link]
  4. Hobbs, K., Zhang, P., Shi, B., Smith, C., Liu, J. (2016). Quad-mesh Based Radial Distance Biomarkers for Alzheimer's Disease,. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI'2016); 19-23.[link]

2015

  1. Liu, J., Ramakrishnan, S., Khuder, S., Kaw, M., Muturi, H., Lester, S., Lee, S., Fedorova, L., Kim, A., Mohamed, I., Gatto-Weis, C., Eisenmann, K., Conran, P., Najjar, S. (2015). High-calorie diet exacerbates prostate neoplasia in mice with haploinsufficiency of Pten tumor suppressor gene.. 3. Molecular metabolism; 4: 186-98.
  2. Chen, Y., Shi, B., Smith, C., Liu, J. (2015). Nonlinear Feature Transformation and Deep Fusion for Alzheimer’s Disease Staging Analysis. LNCS . Switzerland: Machine Learning in Medical Imaging (MLMI'2015); 9352: pp. 304-312.[link]
  3. Zhang, P., Shi, B., Smith, C., Liu, J. (2017). Nonlinear Feature Space Transformation to Improve the Prediction of MCI to AD Conversion. Medical Image Computing and Computer Assisted Interventions Conference (MICCAI' 2017);[link]
  4. Shi, B., Chen, Y., Hobbs, K., Smith, C., Liu, J. (2015). Nonlinear Metric Learning for Alzheimer's Disease Diagnosis with Integration of Longitudinal Neuroimaging Features. 1-901725-53-7. British Machine Vision Conference (BMVC'2015);[link]

2014

  1. Arum, O., Boparai, R., Saleh, J., Wang, F., Dirks, A., Turner, J., Kopchick, J., Liu, J., Khardori, R., Bartke, A. (2014). Specific suppression of insulin sensitivity in growth hormone receptor gene-disrupted (GHR-KO) mice attenuates phenotypic features of slow aging.. 6. Aging cell; 13: 981-1000.
  2. Shi, B., Wang, Z., Liu, J. (2014). Distance-informed metric learning for Alzheimer’s Disease Staging. 2014 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'2014);[link]
  3. Zhang, P., Shi, B., Smith, C., Liu, J. (2017). Nonlinear Feature Space Transformation to Improve the Prediction of MCI to AD Conversion. Medical Image Computing and Computer Assisted Interventions Conference (MICCAI' 2017);[link]
  4. Hobbs, K., Zhang, P., Liu, J. (2014). Inherent Radial Distances for Robust Hippocampal Atrophy Estimation in Alzheimer’s Disease. 2014 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'2014).

2013 and prior

  1. Xu, H., Liu, J. (2013). Spatial-awareness Spectral Embedding (SASE) for Robust Shape Matching. International Conference on Acoustics, Speech, and Signal Processing (ICASSP'2013) ;[link]
  2. Shi, B., Liu, J., Berryman, D., List, E., Kelder, B., Kopchick, J. (2013). Development of a whole-body-mouse statistical shape atlas for obesity research. 2013 BMES Annual Meeting.
  3. Xu, H., Zhang, P., Liu, J. (2013). Towards the Identification of Shape Biomarker(s) for Alzheimer's Disease (AD) based on a Spectral Shape Analysis Framework. 2013 BMES Annual Meeting;[link]
  4. Shi, B., Liu, J., Xie, S., Berryman, D., List, E. (2013). Robust Separation of Visceral and Subcutaneous Adipose Tissues in Micro-CT of Mice. The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'2013);[link]
  5. Colvin, R., Liu, J. (2012). Proceedings from the Great Lakes Bioinformatics Conference 2011. Preface. BMC Bioinformatics; 13 Suppl 2: I1.
  6. Liu, J., Colvin, R. (2012). Preface. S-2. BMC Bioinformatics; 13: I1. [link]
  7. Xie, S., Liu, J., Smith, C. (2012). Riemannian Shape Analysis Based on Meridian Curves. 1. IEEE 11th International Conference on Machine Learning and Applications (ICMLA'2012); 1: 532-537.[link]
  8. Xie, S., Liu, J., Smith, C. (2012). Curve Skeleton-based Shape Representation and Classification. International Conference on Image Processing (ICIP) 2012;[link]
  9. Xu, H., Liu, J., Smith, C. (2012). Robust and efficient point registration based on clusters and Generalized Radial Basis Functions (C-GRBF). international conference on image processing (ICIP'2012);[link]
  10. Zhang, W., Liu, J., Liu, Z. (2012). Adaptive re-transmission scheme for wireless mobile networking and computing. 2012 International Conference on Systems and Informatics (ICSAI'2012); 56 - 62 .[link]
  11. Zhang, W., Liu, J., Liu, Z. (2012). Adaptive re-transmission scheme for wireless mobile networking and computing.. Qingdao: Systems and Informatics (ICSAI), 2012 International Conference on;[link]
  12. Xie, S., Liu, J., Smith, C. (2012). A New Shape Analysis Framework based on Curve Skeletons. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'2012); 701-704.[link]
  13. Shi, B., Liu, J. (2012). Regularity-guaranteed transformation estimation in medical image registration. Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83141W; 8314:[link]
  14. Shi, B., Liu, J. (2011). Non-Twist Regularization for Deformation Estimation. London: Medical Image Analysis and Understanding (MIAU'2011); 151-156.[link]
  15. Mourning, C., Nykl, S., Xu, H., Chelberg, D., Liu, J. (2010). GPU acceleration of robust point matching. 417--426.
  16. Nykl, S., Mourning, C., xu, H., Chelberg, D., Liu, J. (2010). Lecture Notes in Computer Science 6455, Advances in Visual Computing, Chapter Title: GPU Acceleration of Robust Point Matching. Advances in Visual Computing. Berlin Heidelberg: Springer-Verlag; 6455: 417-426.[link]
  17. Liu, J., Smith, C., Chebrolu, H. (2009). Automatic Multiple Sclerosis detection based on integrated square estimation. Computer Vision and Pattern Recognition Workshops (CVPRW 2009);[link]
  18. Liu, J., Chelberg, D., Smith, C., Chebrolu, H. (2009). A Local Likelihood-based Level Set Segmentation Method for Brain MR Images. F09. International Journal of Tomography and Statistics; 12:[link]
  19. Smith, C., Chebrolu, H., Markesbery, W., Liu, J. (2008). Improved predictive model for pre-symptomatic mild cognitive impairment and Alzheimer's disease. 10. Neurological Research; 30: 1091-1096.[link]
  20. Liu, J., wang, y. (2008). Segmentation-Assisted Image Registration for Brain Morphological Analysis. 5. International Journal of Computational Science; 2: 690-707.
  21. Xie, S., Liu, J., Berryman, D., List, E., Smith, C., Chebrolu, H. (2007). A Robust Image Segmentation Model Based on Integrated Square Estimation. International Symposium on Visual Computing (ISVC'2007);[link]
  22. Liu, J., Smith, C., Chebrolu, H. (2007). Automatic subcortical structure segmentation using probabilistic atlas. International Symposium on Visual Computing (ISVC'2007);[link]
  23. Liu, J., Smith, C., Chebrolu, H. (2007). Automatic Subcortical Structure Segmentation using Local Likelihood-based Active Contour. 3D Segmentation in The Clinic: A Grand Challenge 2007; pp. 91-98.[link]
  24. Liu, J., Chelberg, D., Chebrolu, H., Smith, C. (2007). Distribution-based Level Set Segmentation for Brain MR Images. Proceedings of the British Machine Vision Conference;[link]
  25. Liu, J. (2007). A Local Probabilistic Prior-Based Active Contour Model for Brain MR Image Segmentation. Asian Conference on Computer Vision (ACCV'2007); pp 956-964.[link]
  26. Liu, J., Wang, Y., Liu, J. (2006). A Unified Framework for Segmentation-Assisted Image Registration. Asian Conference on Computer Vision (ACCV 2006); pp 405-414.[link]
  27. Liu, J. (2006). Robust Image Segmentation using Local Median. Computer and Robot Vision, 2006. The 3rd Canadian Conference on;[link]
  28. Li, C., Liu, J., Fox, M. (2005). Segmentation of External Force Field for Automatic Initialization and Splitting of Snakes. 11. Pattern Recognition; 38: 1947-1960.[link]
  29. Cao, L., Harrington, P., Liu, J. (2005). SIMPLISMA and ALS Applied to Two-dimensional Nonlinear Wavelet Compressed Ion Mobility Spectra of Chemical Warfare Agent Simulants. 8. Analytic Chemistry; 77: 2575-2586.[link]
  30. Li, C., Liu, J., Fox, M. (2005). Segmentation of edge preserving gradient vector flow: an approach toward automatically initializing and splitting of snakes. Computer Vision and Pattern Recognition (CVPR'2005);[link]
  31. Liu, J. (2005). Segmentation guided registration for medical images. SPIE Medical Imaging;[link]
  32. Wang, Y., Liu, J. (2005). Segmentation Guided Robust Multimodal Image Registration Using Local Correlation. Annual International Conference of the IEEE ESBS (ESBC'05);[link]
  33. Liu, J. (2005). Vector-Valued Local Frequency Representation for Robust Multimodal Image Registration. Annual International Conference of the IEEE ESBS (ESBC'05);[link]
  34. Liu, J., Wei, M., Liu, J. (2004). Artifact reduction in mutual-information-based CT-MR image registration. Proceedings of SPIE Medical Imaging; [link]
  35. Liu, J. (2005). Segmentation guided registration for medical images. SPIE Medical Imaging;[link]
  36. Liu, J., Wei, M., Liu, J. (2004). Artifact reduction in mutual-information-based CT-MR image registration. Proceedings of SPIE Medical Imaging; [link]
  37. Yang, L., Welch, L., Liu, J., Cavanaugh, C. (2003). A Robust QoS Forecasting Technique For Dynamic Distributed Real-Time Testbed. New Orleans, LA: IEEE CAMP 2003 International Workshop on Computer Architectures for Machine Perception.
  38. Liu, J., Liu, J. (2003). Artifacts reduction in mutual information-based image registration using prior information. international conference on image processing 2003;[link]
  39. Yang, L., Liu, J., Cavanaugh, C., Welch, L. (2003). A L2E-Based QoS Forecasting Algorithm for a Dynamic, Distributed Real-Time Systems. Las Vegas, NV: The 2003 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'03); 424-429.
  40. Liu, J., Vemuri, B., Bova, F. (2002). Efficient Multimodal Image Registration using Local Frequency Maps. 3. Secaucus, NJ: Machine Vision and Application/Springer-Verlag New York Inc.; 13: 149-163.[link]
  41. Liu, J., Vemuri, B., Marroquin, J. (2002). Local Frequency Representation for Robust Multi-modal Image Registration. 5. IEEE Transactions on Medical Imaging; 21: 462-469.[link]
  42. Liu, J., Vemuri, B. (2001). Fast Non-rigid Multimodal Image Registration Using Local Frequency Maps. 1-901725-53-7. Medical Image Computing and Computer Assisted Interventions Conference (MICCAI'2001).
  43. Liu, J. (2001). Regularized Quadrature Filters for Local Frequency Estimation: Application to Multimodal Volume Image Registration. Vision Modeling and Visualization Conference 2001 (VMV-01);[link]
  44. Liu, J., Vemuri, B., Marroquin, J. (2001). Robust Multimodal Image Registration Using Local Frequency Representations. 1-901725-53-7. Information Processing in Medical Imaging (IPMI'01);[link]
  45. Liu, J., Vemuri, B., Bova, F. (2000). Multimodal image registration using local frequency. 1-901725-53-7. Workshop of Computer Vision and Applications (WACV'00) [link]

Patents

  • Zhu, J., Wilhelm, J., Williams II, R., Uijt de Haag, M., Bartone, C., Liu, J., Chelberg, D., Liu, C., DiBenedetto, M. An Integrated, Scalable All-Weather, All-Terrain, All-Time, Autonomous Perimeter Monitoring and Ground Inspection System, Provisional patent application. OU16018.

    • Book Chapters in Scholarly Books

  • Liu, J. (2011). Segmentation-Assisted Registration for Brain MR Images. Springer Science ;[link]
  • Liu, J. (2008). A Unified Framework for Segmentation-assisted Image Registration. 14. Recent Advances in Computational Sciences, Jorgensen/ Shen/Shu/Yan eds. / World Scientific; 1: 243-254.
  • Liu, J., Wang, Y. (2008). A Unified Framework for Segmentation-assisted Image Registration,. World Scientific; 243-254.
  • Liu, J. (2007). Deformable Model-based Image Registration. Springer; 1: 517-542.
  • Liu, J. (2007). 15. Deformable Models: Biomedical and Clinical Applications, Suri/Farag, eds.,; 1: 517-542.