Imaging Science Seminar: Modeling and Analysis of Plant Roots from 3D Imaging
Imaging Science Seminar
Modeling and Analysis of Plant Roots from 3D Imaging
Dr. Tao Ju
Professor
Department of Computer Science and Engineering
Washington University at St. Louis
Recent advances in imaging (e.g., CT and MRI) have allowed biologists to "see" the structure and growth of plant roots in 3D. This work presents novel contributions to both the fields of plant phenotyping and computer graphics, particularly in shape and topology analysis.
Abstract:
Roots, the "hidden" half of plants, play a vital role in the development and function of plants. Recent advances in imaging (e.g., CT and MRI) have allowed biologists to "see" the structure and growth of roots in 3D. Due to the complexity of roots, computational methods that extract useful biological information (such as root traits and branching hierarchy) from 3D imaging are still under-developed. In this talk, I present our recent work on algorithms and software tools for 3D root modeling and analysis. Our work presents novel contributions to both the fields of plant phenotyping and computer graphics, particularly in shape and topology analysis.
Speaker Bio:
Tao Ju is a Professor in the Department of Computer Science and Engineering at Washington University in St. Louis. He obtained his B.S. and B.A. degrees from Tsinghua University in 2000, and his M.S. and Ph.D. degrees in Computer Science from Rice University in 2005. He conducts research in computer graphics and bio-medical applications, and is particularly interested in geometric modeling and shape analysis. He is currently the Associated Editor-In-Chief for IEEE Transactions on Visualization and Computer Graphics, and has served as associate editors for Computer Graphics Forum, Computer-Aided Design, Graphical Models, and Computational Visual Media. He has served on the program committees of key conferences in computer graphics (Siggraph, Siggraph Asia, Eurographics, SGP, Pacific Graphics, etc.), and has chaired the program committees of Pacific Graphics (2007), SGP (2018), and GMP (2019). His research is funded by NSF and NIH, including an NSF CAREER award in 2009.Dr. Dong’s research lies at the intersection of machine learning, computer vision, computer graphics, and neuroscience, and it seeks solutions to enhance the adaptation capability and computational & energy efficiency of computational imaging systems. In particular, my research encompasses the development of imaging systems and computational algorithms to endow the system with bioplausible homeostasis and apply it to real-world applications. The endowed homeostasis capability can decouple physiology from immediate external conditions and thereby significantly extend the range of available application scenarios.
Intended Audience:
Beginners, undergraduates, graduates, and experts. Those with interest in the topic.
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Event Snapshot
When and Where
Who
Open to the Public
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No