EPSRC Winter School in Mathematics for Data Modelling

Applications to Machine Vision

author:Andrew Blake, Microsoft Research
published: Feb. 5, 2008, recorded: January 2008, views: 606

Slides

Slides
0:00	Markov Random Field Models and Optimization in image processing and vision
1:07	Spatial inference problems in vision and Spatial inference problems in vision and
5:18	Segmentation
10:04	Simple segmentation --- Ising prior
12:18	Segmentation artefacts --- Ising prior
12:20	Simple segmentation --- Ising prior
12:38	Segmentation artefacts --- Ising prior
13:33	Simple segmentation --- Ising prior
15:37	Segmentation artefacts --- Ising prior
15:39	Boykov-Jolly contrast-sensitive segmentation
18:25	GrabCut: partially supervised inference
23:15	Optimizing Markov Random Fields
26:05	Graph Cut engine for Markov segmentation
33:49	Ford-Fulkerson Min-cut/Max Flow
36:12	Example: optimization as graph cut problem
38:38	Example: graph cut optimization – min cut/max flow
38:48	Ford-Fulkerson Min-cut/Max Flow
40:03	Example: optimization as graph cut problem
40:13	Example: graph cut optimization – min cut/max flow
44:14	Augmenting flow
46:04	Graph cut – regularity (submodularity)
49:13	Regularity: achieving canonical form (1)
49:52	Regularity: achieving canonical form (2)
51:09	Graph cut – regularity (submodularity)
53:21	Dynamic graph cut – Markov editing.
54:45	Dynamic graph cuts (1)
54:49	Dynamic graph cuts (2)
54:50	Dynamic graph cuts (3)
55:05	Dynamic graph cuts (4)
55:39	Stereo webcam
57:12	Live Stereo Segmentation
58:00	Background substitution

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Description

This presentation describes novel approaches to spatial inference problems in vision and image processing. Markov random field models are described for image restoration, foreground segmentation, graph cutting and stereo matching.