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William J. Beksi

Name

[Beksi, William J.]
  • Assistant Professor, Department of Computer Science & Engineering

Professional Preparation

    • 2018 Ph.D. in Computer ScienceUniversity of Minnesota
    • 2016 M.S. in Computer ScienceUniversity of Minnesota
    • 2002 B.S. in Mathematics and Computer ScienceStevens Inst. of Tech

Appointments

    • Sept 2018 to Present Assistant Professor
      University of Texas at Arlington

Memberships

  • Membership
    • Sept 2018 to Present IEEE

Awards and Honors

    • Sep  2018 Rising STARs sponsored by University of Texas
    • Dec  2017 UMII MnDRIVE Ph.D. Fellowship sponsored by University of Minnesota Informatics Institute

Research and Expertise

  • Robotics, Computer Vision, Artificial Intelligence, Applied Topology

    I am the founder and director of the Robotic Vision Laboratory (RVL). The focus of the RVL is on the challenge of applying computer vision  to robotics. We believe that the ability to visually perceive, understand, and respond to the complex world around us is crucial for the next generation of robots in manufacturing, transportation, construction, infrastructure inspection, environmental monitoring, agriculture, healthcare, space exploration, defense, and the home.

Publications

      Journal Article 2019
      • W.J. Beksi and N. Papanikolopoulos. A Topology-based Descriptor for 3D Point Cloud Modeling: Theory and Experiments, Image and Vision Computing, 2019.

        {Journal Article }

      Conference Paper 2018
      • W.J. Beksi and N. Papanikolopoulos. Signature of Topologically Persistent Points for 3D Point Cloud Description, IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, pp. 3229-3234, 2018.

        {Conference Paper }

      Journal Article 2016
      • D. Fehr, W.J. Beksi, D. Zermas and N. Papanikolopoulos. Covariance Based Point Cloud Descriptors for Object Detection and Recognition, Computer Vision and Image Understanding, 142, pp. 80-93, 2016.

        {Journal Article }

      Conference Paper 2016
      • W.J. Beksi and N. Papanikolopoulos. 3D Point Cloud Segmentation Using Topological Persistence, IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, pp. 5046-5051, 2016.

        {Conference Paper }
      2016
      • W.J. Beksi and N. Papanikolopoulos. 3D Region Segmentation Using Topological Persistence, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea, pp. 1079-1084, 2016.

        {Conference Paper }

      Technical Report 2015
      • W.J. Beksi, K. Choi, D. Canelon and N. Papanikolopoulos. The Microvision Robot and its Capabilities, Technical Report TR 15-003, University of Minnesota, Department of Computer Science and Engineering, 2015.

        {Technical Report }

      Conference Paper 2015
      • W.J. Beksi and N. Papanikolopoulos. Object Classification Using Dictionary Learning and RGB-D Covariance Descriptors, IEEE International Conference on Robotics and Automation (ICRA), Seattle, USA, pp. 1880-1885, 2015.

        {Conference Paper }
      2015
      • W.J. Beksi, J. Spruth and N. Papanikolopoulos. CORE: A Cloud-based Object Recognition Engine for Robotics, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, pp. 4512-4517, 2015.

        {Conference Paper }

      Conference Paper 2014
      • D. Fehr, W.J. Beksi, D. Zermas and N. Papanikolopoulos. RGB-D Object Classification Using Covariance Descriptors, IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, pp. 5467-5472, 2014.

        {Conference Paper }
      2014
      • D. Fehr, W.J. Beksi, D. Zermas and N. Papanikolopoulos. Occlusion Alleviation through Motion Using a Mobile Robot, IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, pp. 3179-3184, 2014.

        {Conference Paper }
      2014
      • W.J. Beksi and N. Papanikolopoulos. Point Cloud Culling for Robot Vision Tasks Under Communication Constraints, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, USA, pp. 3747-3752, 2014.

        {Conference Paper }

Courses

      • CSE 6367-001 COMPUTER VISION

        The objective of this course is to provide an introduction to the fundamental concepts of computer vision - how to make computers make sense of images. Topics include: projective geometry, camera geometry and calibration, linear filters, edge detection, feature descriptors, segmentation, epipolar geometry, stereo systems, motion and tracking, 3D reconstruction, image-based rendering, 3D point cloud processing, object recognition, and scene understanding. This course is suitable for gaining a solid technical background and as a preparation for more advanced work in computer vision.

        Spring - Regular Academic Session - 2019Contact info & Office Hours
      • CSE 5360-020 Artificial Intelligence I

        This course provides an introduction to the fundamental concepts of Artificial Intelligence (AI). Topics include: agents, search (search space, uninformed and informed search), game playing, planning, knowledge representation (logical encodings of domain knowledge, ontologies), and the programming language Lisp. The course is suitable to gain a solid technical background and as a preparation for more advanced work in AI.

        Fall - Regular Academic Session - 2018Contact info & Office Hours

Service to the Community

  • Appointed
    • Sept 2018 to  Present Reviewer for journals

      IEEE Transactions on Robotics, IEEE Transactions on Intelligent Transportation Systems, Computer Vision and Image Understanding, Image and Vision Computing, Robotics and Autonomous System

    • Sept 2018 to  Present Reviewer for conferences

      IEEE International Conference on Robotics and Automation (ICRA), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE International Conference on Computer Vision (ICCV), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Robotics: Science and Systesm (RSS), International Symposium on Multi-Robot and Multi-Agent Systems (MRS)