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CS6825: Recognition – a sample of CS6825: Recognition – a sample of ApplicationsApplications

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Applications:Applications:

Industrial inspection, quality controlIndustrial inspection, quality control Surveillance and securitySurveillance and security Assisted livingAssisted living Human-computer interfacesHuman-computer interfaces Medical image analysisMedical image analysis Reverse engineeringReverse engineering Image databasesImage databases

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A1. People Tracking ApplicationA1. People Tracking Application

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People FindingPeople Finding Pedestrian findingPedestrian finding

• many pedestrians look like lollipops (hands many pedestrians look like lollipops (hands at sides, torso wider than legs) most of the at sides, torso wider than legs) most of the timetime

• classify image regions, searching over classify image regions, searching over scalesscales

• But what are the features?But what are the features?• Compute wavelet coefficients for Compute wavelet coefficients for

pedestrian windows, average over pedestrian windows, average over pedestrians. If the average is different pedestrians. If the average is different from zero, probably strongly associated from zero, probably strongly associated with pedestrianwith pedestrian

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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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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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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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A2. Face Recognition ApplicationA2. Face Recognition Application

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Finding faces using relationsFinding faces using relations

Strategy:Strategy:• Face is eyes, nose, mouth, etc. with Face is eyes, nose, mouth, etc. with

appropriate relations between themappropriate relations between them• build a specialised detector for each of build a specialised detector for each of

these (template matching) and look for these (template matching) and look for groups with the right internal structuregroups with the right internal structure

• Once we’ve found enough of a face, Once we’ve found enough of a face, there is little uncertainty about where there is little uncertainty about where the other bits could bethe other bits could be

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Finding faces using relationsFinding faces using relations

Strategy: compareStrategy: compare

Notice that once some facialfeatures have been found, theposition of the rest is quitestrongly 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

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DetectionDetection

This means we compare

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IssuesIssues Plugging in values for position of nose, Plugging in values for position of nose,

eyes, etc.eyes, etc.• search for next one given what we’ve foundsearch for next one given what we’ve found

when to stop searchingwhen to stop searching• when nothing that is added to the group could change when nothing that is added to the group could change

the decisionthe 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 nextwhat 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 responseWhat if there’s no nose response• marginalizemarginalize

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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

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A3. SurgeryA3. Surgery

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Application: SurgeryApplication: Surgery To minimize damage by operation planningTo 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 tissueTo remove only affected tissue ProblemProblem

• ensure that the model with the operations ensure that the model with the operations planned on it and the information about the planned on it and the information about the affected tissue lines up with the patientaffected tissue lines up with the patient

• display model information supervised on view of display model information supervised on view of patientpatient

• Big IssueBig Issue: coordinate alignment, as above: coordinate alignment, as above

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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

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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.

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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.

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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.

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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.

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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.