Spatial Statistics with Image Analysis HT11

News

First lecture on August 30:th 8.15-10.00 in E:1406

Introductory lecture F0 given at the Image analysis (FMA170) course on 2011-10-04.

Examination

Three home assignments/projects. The assignments will be handed out during the 2:nd, 4:th and 6:th course week.
v2: 1) Classic Kriging Handed out 2011-09-05, due 2011-09-23.
v4: 2) Gaussian Markov fields and parameter uncertainty Handed out 2011-09-20, due 2011-10-07.
v6: 3) MRFs, non-Gaussian data, point procceses, etc. Handed out 2011-10-05. Written and oral presentation of project 3.

Schedule

Last updated 2011-10-10.

Day				Lectures (Ch. in the book)	Exercises
v1/35	Tue	30/8	L1	Introduction; Statistical modelling (1.1)
	Thu	1/9	C1		Image data and random distributions in Matlab
	Fri	2/9	L2	Spatial models I; Random fields and covariance models (Ch 3)
v2/36	Tue	6/9	L3	Spatial models II; Classic Kriging and covariance estimation (Ch 3)
	Thu	8/9	C2		Covariance estimation
	Fri	9/9	L4	Spatial models III; Covariance estimation (ML) Discussion of Lab 2 and Home Assignment 1 In M:A
v3/37	Tue	13/9	L5	Markov fields I: Gaussian fields (4.1-4.2.3)
	Thu	15/9	C3		Gaussian Markov random fields
	Fri	16/9	L6	Markov fields II: Gaussian fields (4.2.4-4.2.6)
v4/38	Tue	20/9	L7	Hierarchical models II: Bayesian analysis
	Thu	22/9	C4		Work on HA1!
	Fri	23/9	L8	Classification, EM-algorithm (2.1-2.3)
v5/39	Tue	27/9	L9	EM-algorithm (cont.) (2.1-2.3)		See L8
	Thu	29/9	C5		Classification
	Fri	30/9	L10	Markov fields III: Discrete fields
v6/40	Tue	4/10	L11	Markov fields IV: Parameter estimation for discrete fields
	Thu	6/10	C6		Discrete field simulation and estimation
	Fri	7/10	L12	Discrete field reconstruction, the EM algorithm
v7/41	Tue	11/10	L13	Non-Gaussian data
	Thu	13/10	C7		Work on HA3!
	Fri	14/10	L14	Discussion of projects 1 & 2

General Information

The course is given once a year. It is worth 7.5 ECTS credits and consists of 14 2-hour lectures, 7 3-hour computer exercises, and three home assignments.

Introductory lecture F0 and course plan english (svenska)

Literature

F. Lindgren, Image Modelling and Estimation - A statistical approach. The Compendium is available from the department or from the literature homepage.

Prerequisits

A basic course in mathematical statistics, and at least one of: Markov processes (FMS180) Markov processes (FMSF15/MASC03) Stationary Stochastic Processes (FMS045) Stationary Stochastic Processes (FMSF10/MASC04) Image analysis (FMA170) Familiarity with Matlab is recommended.

Recommended related courses (not prerequisits)

Monte Carlo methods for stochastic inference (FMS091/MASM11) Linear and Logistic Regression (FMSN30/MASM22) Computer vision (FMA270)