Computer Vision

Lecturer: Dr J.G. Daugman (jgd1000@cl.cam.ac.uk )

No. of lectures: 8

Prerequisite course: Continuous Mathematics

Overview.

Goals of computer vision; why they are so difficult. Image sensing, pixel arrays, CCD cameras, framegrabbers.

Sampling theory.

Finite differences and directional derivatives. Filtering; 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. Texture, colour, stereo, and motion descriptors. Disambiguation.

Lambertian and specular surfaces.

Reflectance maps. Bayesian inference in vision; knowledge-driven interpretations.

Inferring shape from shading: surface geometry.

Boundary descriptors; Fundamental Theorem of Curves; codons.

Object-centred coordinates.

Solid parameterisation. Superquadrics. Inverse problems; energy minimisation, relaxation, regularisation.

Model-based vision.

Appearance-based versus volumetric models. Applications and case studies. Face recognition.

Reference book:

Jain, R., Kasturi, R., & Schunck, B.G. (1995). Machine Vision. McGraw-Hill.