2. 1 CS6825: Recognition – a sample of Applications

3. 2 Applications: Industrial inspection, quality control Industrial inspection, quality control Surveillance and security Surveillance and security Assisted living Assisted living Human-computer interfaces Human-computer interfaces Medical image analysis Medical image analysis Reverse engineering Reverse engineering Image databases Image databases

4. 3 A1. People Tracking Application

5. 4 People Finding Pedestrian finding Pedestrian finding many pedestrians look like lollipops (hands at sides, torso wider than legs) most of the timemany pedestrians look like lollipops (hands at sides, torso wider than legs) most of the time classify image regions, searching over scalesclassify image regions, searching over scales But what are the features?But what are the features? Compute wavelet coefficients for pedestrian windows, average over pedestrians. If the average is different from zero, probably strongly associated with pedestrianCompute wavelet coefficients for pedestrian windows, average over pedestrians. If the average is different from zero, probably strongly associated with pedestrian

6. 5 Figure from, “A general framework for object detection,” by C. Papageorgiou, M. Oren and T. Poggio, Proc. Int. Conf. Computer Vision, 1998, copyright 1998, IEEE

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8. 7

9. 8 A2. Face Recognition Application

10. 9 Finding faces using relations Strategy: Strategy: Face is eyes, nose, mouth, etc. with appropriate relations between themFace is eyes, nose, mouth, etc. with appropriate relations between them build a specialised detector for each of these (template matching) and look for groups with the right internal structurebuild a specialised detector for each of these (template matching) and look for groups with the right internal structure Once we’ve found enough of a face, there is little uncertainty about where the other bits could beOnce we’ve found enough of a face, there is little uncertainty about where the other bits could be

11. 10 Finding faces using relations Strategy: compare Strategy: compare Notice that once some facial features have been found, the position of the rest is quite strongly constrained. Figure from, “Finding faces in cluttered scenes using random labelled graph matching,” by Leung, T. ;Burl, M and Perona, P., Proc. Int. Conf. on Computer Vision, 1995 copyright 1995, IEEE

12. 11 Detection This means we compare

13. 12 Issues Plugging in values for position of nose, eyes, etc. Plugging in values for position of nose, eyes, etc. search for next one given what we’ve foundsearch for next one given what we’ve found when to stop searching when to stop searching when nothing that is added to the group could change the decisionwhen nothing that is added to the group could change the decision i.e. it’s not a face, whatever features are added ori.e. it’s not a face, whatever features are added or it’s a face, and anything you can’t find is occludedit’s a face, and anything you can’t find is occluded what to do next what to do next look for another eye? or a nose?look for another eye? or a nose? probably look for the easiest to findprobably look for the easiest to find What if there’s no nose response What if there’s no nose response marginalizemarginalize

14. 13 Figure from, “Finding faces in cluttered scenes using random labelled graph matching,” by Leung, T. ;Burl, M and Perona, P., Proc. Int. Conf. on Computer Vision, 1995 copyright 1995, IEEE

15. 14 A3. Surgery

16. 15 Application: Surgery To minimize damage by operation planning To minimize damage by operation planning To reduce number of operations by planning surgery To reduce number of operations by planning surgery To remove only affected tissue To remove only affected tissue Problem Problem ensure that the model with the operations planned on it and the information about the affected tissue lines up with the patientensure that the model with the operations planned on it and the information about the affected tissue lines up with the patient display model information supervised on view of patientdisplay model information supervised on view of patient Big Issue: coordinate alignment, as aboveBig Issue: coordinate alignment, as above

17. 16 MRI CTI NMI USI Reprinted from Image and Vision Computing, v. 13, N. Ayache, “Medical computer vision, virtual reality and robotics”, Page 296, copyright, (1995), with permission from Elsevier Science

18. 17

19. 18 Figures by kind permission of Eric Grimson; further information can be obtained from his web site http://www.ai.mit.edu/people/welg/welg.html.

20. 19 Figures by kind permission of Eric Grimson; further information can be obtained from his web site http://www.ai.mit.edu/people/welg/welg.html.

21. 20 Figures by kind permission of Eric Grimson; further information can be obtained from his web site http://www.ai.mit.edu/people/welg/welg.html.

22. 21 Figures by kind permission of Eric Grimson; further information can be obtained from his web site http://www.ai.mit.edu/people/welg/welg.html.

23. 22 Figures by kind permission of Eric Grimson; further information can be obtained from his web site http://www.ai.mit.edu/people/welg/welg.html.