Quantitative Imaging


Publications of R. Li
Articles in journal or book chapters
  1. H. Tan, D. Wang, R. Li, C. Sun, R. Lagerstrom, Y. He, Y. Xue, and T. Xiao. A robust method for high-precision quantification of the complex three-dimensional vasculatures acquired by X-ray microtomography. Journal of Synchrotron Radiation, 23(5):1216--1226, September 2016. [DOI] [bibtex-entry]

  2. R. Li, D. Wang, C. Sun, R. Lagerstrom, H. Tan, Y. He, and T. Xiao. Towards Automated Quantitative Vasculature Understanding via Ultra High-Resolution Imagery. In C. Sun, T. Bednarz, T. D. Pham, P. Vallotton, and D. Wang, editors,Signal and Image Analysis for Biomedical and Life Sciences, chapter 10, pages 177--189. Springer, 2015. [DOI] [bibtex-entry]

  3. K. Mele, R. Li, V. Fazio, and J. Newman. Quantifying the quality of the experiments used to grow protein crystals: the iQC suite. Journal of Applied Crystallography, 47(3):1097--1106, June 2014. [DOI] [bibtex-entry]

  4. R. Li and P. K. Robertson. Towards Perceptual Control of Markov Random Field Textures. In G. Grinstein and H. Levkowitz, editors,Perceptual Issues in Visualization, chapter 10 . Springer Science Business Media, 2013. [bibtex-entry]

  5. Y. Guo and R. Li. A Multi-Agent Machine Learning Framework for Intelligent Energy Demand Management. In G. Trajkovski, editor,Developments in Intelligent Agent Technologies and Multi-Agent Systems: Concepts and Applications, chapter 13 , pages 198--212. IGI, 2011. [bibtex-entry]

  6. G. Platt, J. Li, R. Li, G. Poulton, G. James, and J. Wall. Adaptive HVAC Zone Modeling for Sustainable Buildings. Journal of Energy and Buildings, 42:412--421, April 2010. [DOI] [bibtex-entry]

  7. Y. Guo, A. Zeman, and R. Li. A Reinforcement Learning Approach to Setting. International Journal of Agent Technologies and Systems, 1(2):55--70 , 2009. [bibtex-entry]

  8. L. Hoang, R. Li, S. Ourselin, and J. M. Potter. A Visual Dataflow Language for Image Segmentation and Registration. In J. Filipe, B. Shishkov, M. Helfert, and L. A. Maciaszek, editors,Software and Data Technologies, chapter 9, pages 60--72. Berlin: Springer, 2008 . [bibtex-entry]

  9. R. Li and S. Ourselin. Toward Consistently Behaving Deformable Models for Improved Automation in Image Segmentation. In A. A. Farag and and J. S. Suri, editors,Deformable Models - Biomedical and Clinical Applications, chapter 9 , pages 259--292. Springer, 2007. [bibtex-entry]

  10. J. Broerse, R. Li, and R. Ashton. Ambiguous pictorial depth cues and perceptions of nonrigid motion in the three-loop figure. Perception, 23(9):1049--1062, 1994 . [bibtex-entry]

Conference articles
  1. D. Wang, G. Tuck, R. Little, and R. Li. Advances in the automated detection and recording of capture events from on-vessel video footage. In International Fisheries Observer and Monitoring Conference, Vigo, Spain, 11-15 June 2018. [bibtex-entry]

  2. D. Jenkins, M. Mahoney, R. Roest, H. Lomas, R. Pearce, R. Li, S. Mayo, and D. Wang. Micro-CT analysis of coke and its relationship to coke quality indicators. In 7th International Congress on the Science and Technology of Ironmaking, Cleveland Ohio, USA, 4-7 May 2015. [bibtex-entry]

  3. R. Li, D. R. Jenkins, and R. Pearce. Texture-based identification of inert-maceral derived components in metallurgical coke. In T. Weber, M. J. McPhee, and R. S. Anderssen, editors, MODSIM2015, 21st International Congress on Modelling and Simulation, pages 85--90 , 2015. Modelling and Simulation Society of Australia and New Zealand. [bibtex-entry]

  4. D. Wang, H. Tan, R. Li, Y. He, and T. Xiao. Automated Quantitative Analysis of Ultra High-Resolution 3D images of Vasculature and Microvasculature. In Symposium on Multi-scale and Multi-dimensional Synchrotron Radiation Imaging, Shanghai, China, 3-6 November 2015. [bibtex-entry]

  5. D. Jenkins, M. R. Mahoney, R. H. Pearce, A. D. Miller, S. Mayo, D. Wang, R. Roest, H. Lomas, and R. Li. Micro-CT analysis of the microstructure of metallurgical coke for evaluation of coke quality. In the 2nd International Congress on 3D Materials Science, Annecy, France, 29 June - 2 July 2014. [bibtex-entry]

  6. D. Wang, H. Tan, R. Li, Y. He, and T. Xiao. Automated Quantification of 3D Vasculature Using Synchrotron Radiation X-Ray Microtomography. In The 12th International Conference on X-Ray Microscopy, Melbourne, Australia, 26-31 October 2014. [bibtex-entry]

  7. X. Sirault, J. Fripp, A. Paproki, P. Kuffner, H. Daily, R. Li, and R. Furbank. PlantScan: a three-dimensional phenotyping platform for capturing the structural dynamic of plant development and growth. In R. Sievanen, E. Nikinmaa, C. Godin, A. Lintunen, and P. Nygren, editors, Proceedings of the Seventh International Conference on Functional-Structural Plant Models, pages 45--48, 2013 . [bibtex-entry]

  8. X. Sirault, J. Fripp, A. Paproki, P. Kuffner, C. Nguyen, R. Li, H. Daily, J. Guo, and R. Furbank. PlantScan™: a three-dimensional phenotyping platform for capturing the structural dynamic of plant development and growth. In FSPM2013 Proceedings, 2013. [bibtex-entry]

  9. X. Sirault, J. Fripp, A. Paproki, J. Guo, P. Kuffner, H. Daily, R. Li, and R. Furbank. 3D plant analysis over time: understanding plant architecture, growth and function using imaging technologies. In PhenoDays 2012, 2012 . [bibtex-entry]

  10. V. Hilsenstein, R. Li, and J. A. McCulloch. Machine-vision sensor for prawn aquaculture. In Proceedings of Twenty-sixth International Conference on Image and Vision Computing New Zealand, pages , 2011. [bibtex-entry]

  11. Y. Guo, A. Zeman, and R- Li. Utility Simulation Tool For Automated Energy Demand Side Management. In Proceedinds of International Workshop on Agent Technology for Energy Systems (ATES), 2010. [bibtex-entry]

  12. R. Li, P. Wang, and G. James. Multiscale Adaptive Agent-Based Management of Storage-Enabled Photovoltaic Facilities. In H. Coelho, R. Studer, and M. Wooldridge, editors, Frontiers in Artificial Intelligence and Applications - Proceedings 19th European Conference on Artificial Intelligence, volume 215, pages 151--156, 2010 . [bibtex-entry]

  13. Y. Guo, R. Li, G. Poulton, and A. Zeman. A Simulator for Self-Adaptive Energy Demand Management. In Proceedings of the second IEEE International Conference on Self-Adaptive and Self-Organizing Systems, pages 64--73, 2008 . [bibtex-entry]

  14. Y. Guo, A. Zeman, and R. Li. A Reinforcement Learning Approach to Setting Multi-Objective Goals for Energy Demand Management. In Proceedinds of ALAMAS and ALAG - in Seventh Conference on Autonomous Agents and Multi-Agent Systems, pages 65--72, 2008 . [bibtex-entry]

  15. G. Hu, Y. Guo, and R. Li. A Self-Organizing Nano-Particle Simulator and Its Applications. In Proceedinds of IEEE-NASA Conference on Adaptive Hardware and Systems(AHS 2008), pages 22--25, 2008 . [bibtex-entry]

  16. R. Li, J. Li, G. Poulton, and G. James. Agent-Based Optimisation Systems for Electrical Load Management. In Proceedings of First International Workshop on Optimisation in Multi-Agent Systems (OPTMAS), pages 60--69, 2008 . [bibtex-entry]

  17. R. Li and P. Wang. Pattern Learning and Decision Making in a Photovoltaic Syste. In Lecture Notes in Computer Science - Proceedings of the 7th International Conference on Simulated Evolution and Learning (SEAL), volume 5361/2008, pages 483--492, 2008 . [bibtex-entry]

  18. M. Prokopenko, A. Zeman, and R. Li. Homeotaxis: Coordination with Persistent Time-Loops. In M. Asada, editor, Lecture Notes in Artificial Intelligence - From Animals to Animats 10: the 10th International Conference on Simulation of Adaptive Behavior, pages 403--414, 2008 . Springer, Berlin. [bibtex-entry]

  19. A. Zeman, M. Prokopenko, Y. Guo, and R. Li. Adaptive control of distributed energy management: A comparative study. In Proceedings of the Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems, pages 84--93, 2008 . [bibtex-entry]


Last modified: Fri Dec 3 13:44:06 2021
Maintained by: Changming Sun.

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