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Computer Vision
Computer Laboratory > Course material 2003-04 > Computer Vision

Computer Vision

Lecturer: Dr John Daugman
Taken by: Part II

Prerequisite course: Continuous Mathematics


The aims of this course are to introduce the principles, models and applications of computer vision, as well as some mechanisms used in biological visual systems that may inspire design of artificial ones. The course will cover: image formation, structure, and coding; edge and feature detection; neural operators for image analysis; texture, colour, stereo, and motion; wavelet methods in vision; interpretation of surfaces, solids, and shapes; data fusion; visual inference and learning; and approaches to face recognition.


  • Goals of computer vision; why they are so difficult. How images are formed, and the ill-posed problem of making 3D inferences from them about objects and their properties.

  • Image sensing, pixel arrays, CCD cameras, framegrabbers. Elementary operations on image arrays; coding and information measures.

  • Biological visual mechanisms from retina to cortex. Photoreceptor sampling; receptive field profiles; spike train coding; channels and pathways. Neural image encoding operators.

  • Mathematical operators for extracting image structure. Finite differences and directional derivatives. Filters; convolution; correlation. 2D Fourier domain theorems.

  • Edge detection operators; the information revealed by edges. The Laplacian operator and its zero-crossings. Logan's Theorem.

  • Scale-space, multi-resolution representations, causality. Wavelets as visual primitives.

  • Higher level visual operations in brain cortical areas. Multiple parallel mappings; streaming and divisions of labour; reciprocal feedback through the visual system.

  • Texture, colour, stereo, and motion descriptors. Disambiguation and the achievement of invariances.

  • Lambertian and specular surfaces. Reflectance maps. Discounting the illuminant when infering 3D structure and surface properties.

  • Inferring shape from shading: surface geometry. Boundary descriptors; Fundamental Theorem of Curves; codons.

  • Perceptual psychology and visual cognition. Vision as model-building and graphics in the brain. Learning to see.

  • Lessons from neurological trauma and visual deficits. Visual illusions and what they may imply about how vision works.

  • Bayesian inference in vision; knowledge-driven interpretations. Classifiers. Probabilistic methods in vision.

  • Object-centred coordinates. Solid parameterisation and superquadrics. Appearance-based {\em versus} volumetric model-based vision.

  • Vision as a set of inverse problems; mathematical methods for solving them: energy minimisation, relaxation, regularisation.

  • Approaches to face detection, face recognition, and facial interpretation.


At the end of the course students should

  • understand visual processing from both "bottom-up" (data oriented) and "top-down" (goals oriented) perspectives

  • be able to decompose visual tasks into sequences of image analysis operations, representations, specific algorithms, and inference principles

  • understand the roles of image transformations and their invariances in pattern recognition and classification

  • be able to analyse the robustness, brittleness, generalisability, and performance of different approaches in computer vision

  • be able to describe key aspects of how biological visual systems encode, analyse, and represent visual information

  • be able to think of ways in which biological visual strategies might be implemented in machine vision, despite the enormous differences in hardware

  • understand in depth at least one major practical application problem, such as face recognition, detection, and interpretation

Reference book

Shapiro, L., and Stockman, G. (2001). Computer Vision. Prentice Hall.

  • Syllabus
  • Lecture Notes
  • Learning Guide, Lecture Summary, and Worked Examples
  • Past exam questions

  • Assignments from the Learning Guide:

    • (3 Feb 2004): Exercises 2, 4, and 7.
      Please also experiment with the (Matlab-based) Groningen image analysis tool which is available at, using the L2 norm and either your own images or those on the site. (The web-browser Explorer works better with this than Netscape.)

    • (10 Feb 2004): Exercises 5, 10, and 11.

    • (17 Feb 2004): Exercises 1 and 8. Also study this compelling lightness illusion, and this compelling motion illusion, and try to explain them!

    • (26 Feb 2004): Part of this lecture will be an Examples Class, to review the questions set thus far from the Learning Guide.

    • (4-9 March 2004): Exercises 6, 9 and 12.

    • (11 March 2004): Supplementary Review and Examples Class.

Other resources on-line