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©Michael J. BlackCS143 Intro to Computer VisionBrown University Introduction to Computer Vision Michael J. Black Oct 2004 Object Recognition.

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Presentation on theme: "©Michael J. BlackCS143 Intro to Computer VisionBrown University Introduction to Computer Vision Michael J. Black Oct 2004 Object Recognition."— Presentation transcript:

1 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Introduction to Computer Vision Michael J. Black Oct 2004 Object Recognition

2 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Goals Last class: * Finished PCA. Today * Object Recognition, Light, Cameras. Wednesday * light and cameras wrap-up and DFFS (Alex)

3 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Object Recognition In Assignment 2 you learn a model of mouths. - purely based on appearance - aligned data - one class (with some variation) - un-occluded - standard viewpoint

4 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Fei-Fei Li.

5 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Fei-Fei Li.

6 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Fei-Fei Li.

7 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Fei-Fei Li.

8 ©Michael J. BlackCS143 Intro to Computer VisionBrown University One-Shot Learning Blickets

9 ©Michael J. BlackCS143 Intro to Computer VisionBrown University One-Shot Learning Fei-Fei Li.

10 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Fei-Fei Li.

11 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Fei-Fei Li.

12 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Fei-Fei Li.

13 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Representing Objects Fei-Fei Li. Need to represent the appearance of parts and their spatial orientation.

14 ©Michael J. BlackCS143 Intro to Computer VisionBrown University

15 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Parts Learn parts from examples. Find interesting points (structure tensor), find similar ones, use PCA to model them. From: Rob Fergus http://www.robots.ox.ac.uk/%7Efergus/

16 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Shape Given a “vocabulary” of parts, learn a model of their spatial relationships From: Rob Fergus http://www.robots.ox.ac.uk/%7Efergus/

17 ©Michael J. BlackCS143 Intro to Computer VisionBrown University

18 ©Michael J. BlackCS143 Intro to Computer VisionBrown University

19 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Representing Objects Fei-Fei Li.

20 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Recognizing Objects Fei-Fei Li.

21 ©Michael J. BlackCS143 Intro to Computer VisionBrown University Recognizing Objects Fei-Fei Li.

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