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## Computer Vision

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

No. of lectures: 8

Prerequisite course: Continuous Mathematics

Aims

The aims of this course are to introduce the principles, models and applications of computer vision. The course will cover image structure and encoding; edge and feature detection; interpretation of surfaces; texture, colour, stereo, and motion; wavelet methods in vision; parameterisations for solids and shapes; visual inference; and strategies for automatic face recognition.

Lectures

• 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.

Objectives

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

Reference book

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

Next: Databases Up: Easter Term 2001: Part Previous: Complexity Theory
Christine Northeast
Wed Sep 20 15:13:44 BST 2000