Computer Vision

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ComputerVisionComputerScienceTripos:16 Lecturesby J G Daugman1. computervision;why theyareso di Imagesensing,pixelarrays, Biologicalvisualmechanisms,fromretinato Edgedetectionoperators; Higherbrainvisualmechanisms;streaming; Texture,colour,stereo, ; ers; a setof :facedetectionandrecognition; thiscoursearetointroducetheprinciples,mo delsandapplicationsof com-putervision,as wellas somemechanismsusedin biologicalvisualsystemsthatmay inspiredesignof arti :imageformation,structure,andcoding;edge andfeaturedetection;neuraloperatorsforim ageanalysis;texture,colour,stereo,motion ;waveletmethods forvisualcodingandanalysis;interpretatio nof surfaces,solids,andshapes;datafusion;pro babilisticclassi ers;visualinferenceandlearning; Goalsof computervision.

11. Perceptual psychology and cognition. Vision as model-building. 12. Lessons from neurological trauma and de cits. Visual illusions. 13. Bayesian inference in vision. Classi ers; probabilistic methods. 14. Appearance-based versus model-based vision. Query languages. 15. Vision as a set of inverse problems. Regularisation. 16.

  Computer, Inference, Vision, Perceptual, Computer vision