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  • CMPEN 497 Special Topics - Humanoid Robotics

    •    We study the kinematics and dynamics of state-of-the-art, programmable humanoid robots, from principles of physics, computer vision, and AI to physical simulations and executions on real humanoid robots. We also explore the use of stability analysis in motion.
      Spring 23 Highlights
    Dr. Yanxi Liu, Dr. Robert Collins
    Spring 2023
        
  • CMPEN 497 Special Topics - Humanoid Robot Simulation

    •    This course investigates state of the art, programmable humanoid robot kinematics and dynamics using a combination of principles of physics, computer vision, and AI. Students learn to model and utilize 17-degrees of freedom humanoid robots with enhanced stability, sensory feedback, and actions.
      Spring 21 Highlights
    Dr. Yanxi Liu, Dr. Robert Collins
    Spring 2021
    Undergraduate Courses
        
  • CMPSC 458 Fundamentals of Computer Graphics

    •   Fundamentals of computer graphics; approaches and mathematical techniques that allow a computer to digitally render a scene, advanced methods of image analysis yielding more realistic textures and environments. Topics include transformation, projection, illumination models, shading, hidden lines/surface elimination, viewing, color, raytracing, physics-based graphics, and IRB, in particular image-based texture synthesis.
      Spring 09 Highlights   Fall 09 Highlights   Fall 15 Highlights   Fall 18 Highlights   Fall 19 Highlights
      Fall 20 Highlights   Fall 21 Highlights   Fall 22 Highlights
    Dr. Yanxi Liu
    Spring 2007, Spring 2008, Spring 2009, Fall 2009, Fall 2010, Fall 2011, Fall 2012, Fall 2014, Fall 2015, Fall 2016, Fall 2017, Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2022
  • CMPEN/EE 454 Computer Vision I

    •    Introduction to Computer Vision
    Dr. Robert Collins, Jesse Scott (Spring 2019)
    Spring 2005, Fall 2005, SPring 2006, Fall 2006, Spring 2007, Fall 2007, Fall 2008, Fall 2009, Fall 2010, Fall 2014, Fall 2015, Fall 2016, Fall 2017, Fall 2018, Spring 2019, Fall 2020
  • CMPEN/EE 455 Digital Image Processing

    •    Introduction to Digital Image Processing
    Dr. Robert Collins
    Fall 2011, Fall 2012
    Graduate Courses
        
  • CSE 597 Group Theory Based Pattern Discovery

    •   This is a course on computational methods for real world digital data, which can be across scale, modality and application domains. The content is based on a unique combination of group theory, statistical learning theory, and human/animal perception, with an emphasis on discovering visually appealing patterns automatically with extensive hands-on trainings on large data sets.
    Dr. Yanxi Liu
    Fall 2021
    Similar courses offered Fall 2005 (CMU), Spring 2006, Fall 2006, Fall 2007, Spring 2009, Fall 2011, Fall 2012, Winter 2014 (Stanford), Spring 2015 (Tsinghua), Fall 2016 (ETH), Fall 2019
  • CSE 583 Pattern Recognition and Artificial Intelligence

    •   Decision-theoretic classification, discriminant functions, pattern processing and feature selection, syntactic pattern recognition, shape analysis and recognition.
    Dr. Yanxi Liu
    Fall 2008, Fall 2009, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021
  • CSE 586/EE 554 Topics in Computer Vision

    •   Mathematical Tools for Computer Vision
    Dr. Robert Collins
    Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020
  • CSE 597F/598C Computational Photography

    •   Computational photography combines elements of optics, graphics, and computer vision to enhance or extend the capabilities of digital photography.
    Dr. Robert Collins, Dr. David Capel and Dr. Yanxi Liu
    Spring 2010, Spring 2016
  • CSE 598C Vision-Based Tracking

    •   Detecting and Tracking Moving Objects in Video
    Dr. Robert Collins
    Spring 2006, Fall 2010, Fall 2012
  • CSE 598D Computational Regularity on Interdisciplinary, Large Data Sets

    •   This is a course on computational methods from real world digital data, across scale, modality and application domains, for pattern discovery, comparison and learning. The content is based on a unique combination of group theory, statistical learning theory, and human/animal perception, with an emphasis on discovering visually appealing patterns automatically and hands-on trainings on large data sets.
    Dr. Yanxi Liu
    Fall 2012
  • CSE 598G Computational Regularity

    •   This is a course on computational methods for real applications, based on a unique combination of group theory, pattern theory, statistical learning theory and human/animal/machine perception models.
    Dr. Yanxi Liu
    Fall 2011
  • CSE 598E Machine Learning for Computational Regularity

    •   Symmetry perception and saliency detection, regularity-based segmentation, and group theory.
    Dr. Yanxi Liu
    Fall 2009
  • CSE 597E Visual Salience Seminar

    •   What Captures our Attention, and Why?
    Dr. Robert Collins
    Spring 2007
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