Computer Science & Engineering Department


CS773C Machine Intelligence Advanced Applications

Spring 2008: Object Recognition

  • Meets: TR 1:00pm - 2:15pm (SEM 257)

  • Instructor: Dr. George Bebis


  • Prerequisites

    Good background in image processing (CS674), computer vision (CS685), pattern recognition (CS679), linear algebra, probabilities, and statistics.

    Texts

    We will not use any text in this course; all of the material will be drawn from lecture notes and research papers.

    Useful Texts


    Computer Vision Resources


    Object Recognition Resources


    Object Recognition Challenges and Datasets


    Segmentation Datasets and Benchmarks


    Useful Software



    Description and Objectives

    Recognizing objects from images has been a challenging task in computer vision. This is because objects may look very different from different viewing positions. The most successful approach is in the context of "model-based" object recognition, where the environment is rather constrained and recognition relies upon the existence of a set of predefined model objects. Given an unknown scene, recognition implies: (i) the identification of a set of features from the unknown scene which approximately match a set of features from a known view of a model object, (ii) the recovery of the geometric transformation that the model object has undergone (i.e., pose recovering) and, (iii) verification that other features coincide with predictions. Since usually there is no a-priori knowledge of which model points correspond to which scene points, recognition can be computationally too expensive, even for a moderate number of models. Our goal in this course would be to study several well known techniques in object recognition.

    This course is primarily intended for highly motivated students interested in doing research in object recognition and computer vision in general. It will be essential for students to have a solid understanding of basic topics in math, such as linear algebra, probability and statistics, and calculus. It will also be useful to have some knowledge of computer vision, image processing, and geometry. In general, the more math a student knows, the easier the course will be.

    Topics


    Course Requirements

    This course is primarily intended for highly motivated students interested in doing research in object recognition and computer vision in general. It will be essential for students to have a solid understanding of basic topics in math, such as linear algebra, probability and statistics, and calculus. It will also be useful to have some knowledge of computer vision, image processing, and geometry. There would be no exams in this course. Grading will be based on paper presentations, reports, class participation, and a project. Details are provided in the course syllabus.

    Syllabus


    Schedule of Presentations



    Video Lectures (VL)



    Papers



    Project Topics




    Department of Computer Science & Engineering, University of Nevada, Reno, NV 89557
    Page created and maintained by: Dr. George Bebis (bebis@cse.unr.edu)