School of Computer Science

Module 06-25024 (2012)

Robot Vision

Level 4/M

Ales Leonardis Semester 1 20 credits
Co-ordinator: Ales Leonardis
Reviewer: Michael Mistry

The Module Description is a strict subset of this Syllabus Page.

Aims

The aims of this module are to:

  • give an appreciation of the issues that arise when designing computational models that convert visual signals to structural and symbolic descriptions
  • provide an understanding of the state-of-the-art methods and techniques for processing visual information
  • give hands on experience of designing, implementing and testing computer vision algorithms in realistic scenarios
  • encourage independent thought on deep scientific issues related to visual cognition

Learning Outcomes

On successful completion of this module, the student should be able to:

  • design, implement and test simple computer vision algorithms
  • write a detailed report on a computer vision project
  • survey and critically discuss the research literature in one subfield of computer vision
  • demonstrate an understanding of the main computer vision methods and computational models

Teaching methods

2 hrs lectures per week, 4 student presentations, laboratory sessions


Assessment

  • Sessional: 1.5 hour examination (40%), Continuous assessment (team project and presentation) (60%)
  • Supplementary: By repeat only.

Detailed Syllabus

  1. Introduction
    • Why computer/robot vision
    • Applications
    • Computer and human vision perspectives
    • Challenges
  2. Image formation, low level image processing
    • Image acquisition
    • Noise removal (Linear filters, Median filter)
    • Edge detection
  3. Structure extraction
    • Parametric fitting
    • Hough transform
    • RANSAC
  4. Segmentation
    • Clustering
    • K-means
    • Mean-shift
    • Graph-cuts
  5. Local features
    • Interest points
    • Harris detector, Hessian detector
    • SIFT
  6. 3D reconstruction
    • Stereo vision
    • Correspondence
    • Epipolar geometry
  7. Recognition
    • Histograms
    • Subspace representations
    • Principal component analysis
  8. Categorization
    • Bag-of-features
    • Part-based methods
    • Deformable part-based detector
    • Hierarchical compositional architectures
  9. Motion and tracking
    • Optical flow
    • Tracking as detection
    • Kalman filter
  10. Active vision
    • Perception-action cycle
    • Attention
    • Visual servoing

Programmes containing this module