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Kursusoversigt

Kursusbeskrivelse

Kursusnavn (dansk):	Mønstergenkendelse og datamatsyn
Kursusnavn (engelsk):	Pattern Recognition and Computer Vision
Semester:	Forår 2001
Udbydes under:	cand.it., multimedieteknologi (mmt)
Omfang i ECTS:	7,50
Kursussprog:	Dansk
Kursushjemmeside:	https://learnit.itu.dk
Min. antal deltagere:	10
Forventet antal deltagere:	0
Maks. antal deltagere:	20
Formelle forudsætninger:	Bestået "Introduktion til digital billedbehandling" eller tilsvarende. Matematisk modenhed svarende til bestået 1. år af matematikstudiet.
Læringsmål:	To give a basic knowledge of classical and modern methods for recognition of patterns, shapes, and objects.
Fagligt indhold:	The course covers classical and modern methods for recognition of patterns, shapes and objects. Although most of the methods are applicable for any type of data, we will concentrate on data obtained from images. Such data might be intensity values or values extracted by previous analysis. A feature vector of data can be classified to one among a set of prototypes using either statistical techniques or non-parametric methods. In the first case the distribution of the observation vector for each prototype must be known/estimated in advance during a training period. In the latter case a typical approach will be to classify the vector to the nearest neighbour vector previously seen. This approach also is relevant if the number of objects and their typical characteristics is unknown. Here the goal is to group the set of vectors into prototypes. After a brief introduction to some basic statistical terminology and methods, the plan is to go through: Cluster analysis, Minimal spanning tree, Kd-trees, Nearest neighbour classification, Vector quantization, K-means algorithm, EM-algorithm, Principal component analysis, other transforms useful for dimensionality reduction, Classification trees, Mahalanobis distance, Discriminant analysis, Neural nets, Geometric hashing, and Minimal Description length, and Support Vector Machines. An important part of the course will be to show the application of the methods. To accomplish this we will go through a number of papers describing real applications. Among these are: - A method for shape recognition (Active shape models) - A method using vector quantization in image coding - A system for face recognition used for entrance security - A system for searching an image database for similar images - A system for real-time recognition of hand alphabet gestures Supervision of projects is offered. First in November a voluntary project is formulated.
Læringsaktiviteter:	Forelæsninger, ugeopgaver, frivilligt skriftligt projekt
Eksamensform og -beskrivelse:	X. experimental examination form (7-scale; external exam), 13-skala, Intern censur Aktiv deltagelse samt godkendelse af et antal ugeopgaver. Hvis udbredt ønske kan der afholdes mundtlig eksamen.
Litteratur udover forskningsartikler:	Noter samt udleverede artikler