Course pages 2011–12
- Lecture Notes (PDF)
or here is a
"2-up version" of Lecture Notes
(fits better on a monitor)
- Exercises (PDF)
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, motion; wavelet methods for visual
coding and analysis;
interpretation of surfaces, solids, and shapes; data fusion;
probabilistic classifiers; visual inference and learning.
Issues will be illustrated using the examples of
optical character recognition, image retrieval, and 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. Image coding and information measures. Elementary operations on image arrays.
- Biological visual mechanisms from retina to cortex. Photoreceptor sampling; receptive field profiles; spike trains; channels and pathways. Neural image encoding operators.
- Mathematical operations 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.
- Multi-scale feature detection and matching. SIFT (scale-invariant feature transform); pyramids. 2D wavelets as visual primitives. Energy-minimising snakes; active contours.
- 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. Image and motion segmentation.
- Lambertian and specular surfaces. Reflectance maps, and image formation geometry. Discounting the illuminant when infering 3D structure and surface properties.
- Shape representation. Inferring 3D shape from shading; surface geometry. Boundary descriptors; codons; superquadrics and the "2.5-Dimensional" sketch.
- 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 and probabilistic decision-making.
- Model estimation. Machine learning and statistical methods in vision.
- Applications of machine learning in vision: discriminative versus generative methods. Optical character recognition. Content based image retrieval.
- 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 describe and contrast techniques for extracting and representing features, edges, shapes, and textures;
- 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 domain, such as face recognition, detection, and interpretation
* Forsyth D A and Ponce J. (2003). Computer Vision: A Modern Approach. Prentice Hall.
Shapiro L and Stockman G (2001). Computer Vision. (Prentice Hall: ISBN 0-13-030796-3)
Duda R O, Hart P E, and Stork D G (2001). Pattern Classification, 2nd ed. (Wiley: ISBN 0-471-05669-3)
- (week of 23 Jan 2012): Exercises 1 - 5.
- (week of 30 Jan 2012): Exercises 6 - 8.
- (week of 6 Feb 2012): Exercises 9 - 10.
- (week of 13 Feb 2012): Exercises 11 - 12.
- (week of 20 Feb 2012): Exercises 13 - 14. Also study this compelling
lightness illusion, this illustration of
this motion illusion, and this collection of
dynamic, colour, and cognitive illusions, and try to explain them!
More collections exist here and
- (week of 27 Feb 2012): Exercise 15 - 16.
- (week of 5 Mar 2012): Exercise 17. View this 5-minute video about 3-D morphable face representations, and this 1-minute demonstration of generative models for facial expression, applied dynamically to ("talking") paintings and photographs. For background on the case studies in computer vision, here is a paper about face recognition and here is one about iris recognition and another about how it works.
- OpenCV Computer Vision Library
(excellent C++ open source library with interfaces for some other languages)
- Online documentation for the OpenCV Library
- "CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision" (Edinburgh University)"
- Matlab Functions for Computer Vision and Image Processing