TEACH VISION David Young January 1994 Suggested reading for an introductory course on Computer Vision. -- Teach Files -------------------------------------------------------- The main sequence of teach files is TEACH VISION1 ... VISION7. To access these files, you must first load LIB POPVISION. -- Reading ------------------------------------------------------------ A note on mathematics: Sooner or later in vision work you will hit some mathematical formalism. Many of the authors below have found mathematics to be the most succinct way to express some of their arguments. However, most of the underlying ideas can be understood without recourse to mathematics, and the lectures will avoid its use as far as possible. Do not be discouraged by mathematical expressions in books; read round them, try to work out what is going on in general terms from the english text and diagrams, try different sources, and ask for an alternative explanation in seminars or tutorials. Mathematics apart, some of these references assume a fair amount of prior understanding - do not expect to be able to tackle everything. You may find the full bibliography below helpful if you decide to do a vision extended essay or project. General Reading Sharples et al. (1989) is a quick and effective way into the topic. Winston (1981), Charniak & McDermott (1985), and Mayhew & Frisby (1984) all give broad accounts of AI approaches to vision. Marr (1982) is the source of many central ideas. Fischler & Firschein (1987b) is a good collection of important papers. Boyle & Thomas (1988) is an introductory text which may be helpful for some technical aspects, particularly of low-level vision. Bruce & Green (1985) is an excellent introduction, but isn't particularly oriented towards AI/KBS. Sonka et al. (1993) is an up-to-date introduction mostly oriented towards low-level vision. -- Bibliography ------------------------------------------------------- [Square-bracketed expressions are University of Sussex shelf marks.] Ballard D.H. & Brown C.M. (1982). 'Computer Vision'. Englewood Cliffs, NJ: Prentice-Hall. [QZ 314 Bal] Generally a very technical presentation, with a quite liberal sprinkling of mathematics. A rich source of algorithms for all aspects of computer vision, though it is sometimes hard work to understand what they do and their limitations are not always brought out. Barrow H.G. (1987). Learning receptive fields. Proceedings of the IEEE first annual international conference on neural networks, June 1987. [See David Young to borrow a copy.] Competitive learning produces some surprising results when applied to low-level vision. Barrow H.G. & Tenenbaum J.M. (1981). Interpreting line drawings as three-dimensional surfaces. Artificial Intelligence, 17, 75-116. [QE 1 Art]. Reprinted in Brady (1981), pp. 75-116. Interesting introduction and discussion with a hard centre. Blake A. & Yuille A. (1992). (Eds.) 'Active Vision'. Cambridge, MA: MIT Press. [TA 1632 Act] Boden M.A. (1977). 'Artificial Intelligence and Natural Man'. Brighton: Harvester. [QZ 1240 Bod] Chapters 8 and 9 are largely a thorough run-down of the AI line-drawing analysis work up to 1977. Boden M.A. (1988). 'Computer Models of Mind: Computational Approaches in Theoretical Psychology'. Cambridge: CUP. [QZ 1250 Bod] Chapters 2 and 3 give very interesting discussions of a variety of approaches. Boyle R.D. & Thomas R.C. (1988). 'Computer Vision: A First Course'. Oxford: Blackwell Scientific. [TA 1632 Boy] A reasonably general introduction, though very sketchy in some areas. Brady, M. (1981). (Ed.) 'Computer Vision'. Amsterdam: North-Holland. [QZ 1240 Art] Various important papers. Brooks R.A. (1981). Symbolic reasoning among 3-D models and 2-D images. Artificial Intelligence, 17, 285-348. [QE 1 Art]. Reprinted in Brady (1981), pp. 285-348. A general philosophy of model-based vision and details (somewhat technical) of the ACRONYM system. Brown C.M. (1988). (Ed.) 'Advances in Computer Vision' (2 Vols). Hillsdale, NJ: Lawrence Erlbaum. [QE 1 Adv] By no means a complete view of the field, but in interesting set of chapters by some important researchers; some present a slightly idiosyncratic viewpoint. Bruce V. & Green P.R. (1985). 'Visual Perception: Physiology, Psychology and Ecology'. London: Lawrence Erlbaum. [QZ 314 Bru] An excellent general introduction, embracing and comparing all kinds of approaches, though not particularly oriented to AI methods. Very accessible. Charniak E. & McDermott D. (1985). 'Introduction to Artificial Intelligence'. Reading, MA: Addison-Wesley. [QZ 1240 Cha] Chapter 3 is a brief and rather patchy but quite effective inroduction. Clear, mostly unmathematical. Fischler M.A. & Firschein O. (1987a). 'The Eye, the Brain, and the Computer'. Reading, MA: Addison-Wesley. [QZ 1250 Fis] Beautifully presented and interesting text which has a much broader scope than computer vision. Fischler M.A. & Firschein O. (1987b). (Eds.) 'Readings in Computer Vision: Issues, Problems, Principles and Paradigms'. Los Altos, CA: Morgan-Kaufmann. [Not yet in library.] Collection of significant papers. Frisby J.P. (1979). 'Seeing: Illusion, Brain and Mind'. Oxford: Oxford University Press. [QZ 314 Fri] Very well presented introduction to the psychophysics and physiology of vision, with stimulating examples. Includes exciting spectacles. Gibson J.J. (1966). 'The Senses Considered as Perceptual Systems'. Boston: Houghton Mifflin. [QZ 310 Gib] Gibson J.J. (1979). 'The Ecological Approach to Visual Perception'. Boston: Houghton Mifflin. [QZ 314 Gib] People often love or hate Gibson's books. They are entirely unmathematical. They ignore AI completely. Many of his ideas are very stimulating. Gonzalez R.C. & Wintz P. (1987). 'Digital Image Processing'. Reading, MA: Addison Wesley. [TA 1632 Gon] A fairly technical but comprehensive discussion of image processing techniques. Not particularly relevant to AI/KBS but lots of details of filtering methods and Fourier theory. Gonzalez R.C. & Woods R.E. (1992). 'Digital Image Processing'. Reading, MA: Addison Wesley. [QE 1890 Gon] Updated version of Gonzalez & Wintz. Haralick R.M. & Shapiro L.G. (1992). 'Computer and Robot Vision' (2 Vols). Reading, MA: Addison-Wesley. [TA 1632 Har] Very full technical treatment, with many references and a lot of mathematical detail. Better structured than Ballard & Brown. Hildreth E.C. (1984). Computations underlying the measurement of visual motion. Artificial Intelligence, 23, 309-354. [QE 1 Art]. Reprinted in Richards & Ullman (1987), pp. 99-146. Hogg D. (1983). Model-based vision: a program to see a walking person. Image and Vision Computing, 1, 5-20. [See David Young for a copy] Description of WALKER. Horn B.K.P. (1986). 'Robot Vision'. Cambridge MA: MIT Press. [TJ 211.3 Hor] A recent description of MIT work across the spectrum, but very mathematical. Horn B.K.P. & Schunck B.G. (1981). Determining optical flow. Artificial Intelligence, 17, 185-204. [QE 1 Art]. Reprinted in Brady (1981), pp. 185-204. The smoothing approach to extracting optic flow. Mathematical, but maybe worth looking at the introduction and discussion. Lee D.N. (1980). The optic flow field: the foundation of vision. Philosophical Transactions of the Royal Society of London, B 290, 169-179. [QP 1 Phi] The use of optic flow information from the psychological point of view. Neglects the problem of determining the flow. Mostly accessible, some relatively simple mathematics. Lee D.N. & Young D.S. (1985). Visual timing of interceptive action. In D.J. Ingle, M. Jeannerod & D.N. Lee (Eds.), 'Brain Mechanisms and Spatial Vision' (pp. 1-30). Dordrecht: Martinus Nijhoff. [QZ 314 Bra] How to use optic flow to control actions. Again assumes you can extract it, and inclined to muddle computational and algorithmic levels. Other papers in this volume may also be of interest. Marr D. (1982). 'Vision: a Computational Investigation into the Human Representation and Processing of Visual Information'. San Francisco: W.H. Freeman. [QZ 1240 Mar] One of the most important recent books on vision. Mostly very readable. Very personal. Embraces AI, psychology and neurophysiology. Mayhew J.E.W & Frisby J.P. (1981). Psychophysical and computational studies towards a theory of human stereopsis. Artificial Intelligence, 17, 349-386. [QE 1 Art]. Reprinted in Brady (1981), pp. 349-386. Psychophysics and computer modelling of stereopsis. Mayhew J. & Frisby J. (1984). Computer vision. In T. O'Shea & M. Eisenstadt (Eds.), 'Artificial Intelligence: Tools, Techniques and Applications' (pp. 301-357). New York: Harper & Row. [QZ 1240 Art] A good, clear, general introduction, covering the main AI approaches in a critical and accessible way. Murray D.W. (1987). Model-based recognition using 3D structure from motion. Image and Vision Computing, 5, 85-90. [See David Young to borrow a copy.] A powerful search method for model matching. Nishihara H.K. (1981). Intensity, visible-surface, and volumetric representations. Artificial Intelligence, 17, 265-284. [QE 1 Art]. Reprinted in Brady (1981), pp. 265-284. An alternative discussion of the Marr levels of understanding and representations. Pentland A.P. (1986). (Ed.) 'From Pixels to Predicates: Recent Advances in Computational and Robotic Vision'. Norwood NJ: Ablex. [TA 1632 Fro] Some worthwhile papers in this collection. Richards W. & Ullman S. (1987). (Eds.) 'Image Understanding 1985-86'. Norwood NJ: Ablex. [QZ 1390 Ima] A good collection of recent research. Rumelhart D.E. & McClelland J.L. (1986). 'Parallel Distributed Processing: Explorations in the Microstructure of Cognition', Volume 1: 'Foundations'. Cambridge MA: MIT Press. [QZ 1000 Rum] An important source for this expanding field. Sharples M., Hogg D., Hutchison C., Torrance S. & Young D. (1989). 'Computers and Thought: An Introduction to Cognitive Science and Artificial Intelligence'. MIT Press - Bradford Books. Chapter 9 shows a variety of techniques applied to a very specific problem. Sloman A. (1978). 'The Computer Revolution in Philosophy: Philosophy, Science and Models of Mind'. Hassocks, Sussex: Harvester. [QZ 1240 Slo] Chapter 9 treats vision as one aspect of the general problems of AI, with a few very specific examples. Sonka M., Hlavac V. & Boyle R. (1993). 'Image Processing, Analysis and Machine Vision'. London: Chapman & Hall Computing. [TA 1632 Son] Good clear coverage of low-level vision and image processing. Winston P.H. (1981). 'Artificial Intelligence' (2nd ed.). Reading, MA: Addison-Wesley. [QZ 1240 Win] Chapter 3 deals with the line-labelling technique; Chapter 10 gives a brief overview of AI vision methods. --- _______________________$popvision/teach/vision --- _________Copyright __________University __of ______Sussex _____1994. ___All ______rights _________reserved.