24
9.913 Pattern Recognition for Vision Class 8-2 –An Application of Clustering Bernd Heisele

9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele

  • View
    219

  • Download
    2

Embed Size (px)

Citation preview

Page 1: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele

9.913 Pattern Recognition for Vision

Class 8-2 –An Application of ClusteringBernd Heisele

Page 2: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele

Overview

•Problem•Background •Clustering for Tracking•Examples•Literature•Homework

Page 3: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele

Problem

• Detect objects on the road:• Cars, trucks, motorbikes, pedestrians.

Page 4: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele

Image Motion

Page 5: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele

Object Segmentation using Image Motion

Motion-based segmentation

Page 6: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele

Image Motion—Equations for Rigid Motion

Page 7: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele

Image Motion—Estimation Optical Flow

Page 8: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele

Image Motion—Estimation problems

Page 9: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele

Object Segmentation Problem

Page 10: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 11: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 12: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 13: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 14: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 15: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 16: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 17: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 18: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 19: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 20: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 21: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 22: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 23: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele
Page 24: 9.913 Pattern Recognition for Vision Class 8-2 – An Application of Clustering Bernd Heisele

Literature

• B.Heisele, U.Kressel, and W. Ritter.Tracking non-rigid, moving objects based on color cluster flow.Proc. Computer Vision and Pattern Recognition (CVPR), pp. 253-257, San Juan, 1997.

• Clustering Classics: J. MacQueen. Some methods for classification and analysis of multivariate observations. Proc. 5thBerkeley Symp. Mathematics, Statistics and Probablility, pp. 281-297, 1967.

• Y.Linde, A.Buzo, and R. Gray. An algorithm for vectorquantizerdesign. IEEE Transactions on Communications, COM-28/1, pp. 84-95, 1980.