Prerequisite courses: Continuous Mathematics, Probability
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 for visual coding
and analysis; interpretation of surfaces, solids, and shapes; data fusion;
probabilistic classifiers; visual inference and learning. Several of these
issues will be illustrated in the topic of 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
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 trains;
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
inferring 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.
Solid parameterisation and superquadrics.
Appearance-based versus volumetric model-based vision.
Vision as a set of inverse problems; mathematical methods
for solving them: energy minimisation,
Approaches to face detection, face recognition, and facial
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
* Shapiro, L. & Stockman, G. (2001). Computer Vision. Prentice Hall.