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Active Appearance Models

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08:43 PM ET (US)
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boots and handbags
10:23 PM ET (US)
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Wanda Z.
04:59 AM ET (US)
If one is worried about differentiating between the different type of vehicles in the active appearance models, why not give each one their own logo design? Could just be your basic color/shape scheme. From there, designers can tweak the algorithm setup to give trainees the proper challenge level.

I've never taken part myself, but have had the chance to observe AAM training and it's pretty neat!
02:20 AM ET (US)
This would be possible if almost all private cars requiring drivers, which are not in use and parked 90% of the time, would be traded for public self-driving taxis that would be in near constant use.
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  Spam messages 15-10 deleted by QuickTopic between 08-18-2010 02:03 AM and 07-21-2006 09:03 AM
Tomasz Malisiewicz
12:52 AM ET (US)
One could imagine building some sort of bag-of-texton-words classifier that can predict whether a face or a car exists inside an image window, and then attempt to fit an AAM model for that object class inside the window (like Nik suggested).

When attempting to use AAMs for cars, it appears that one would need a collection of spatial models (one for each aspect). Faces are somewhat easy, because they are essentially 2D objects that are rich with internal features (the eyes, nose, and mouth are almost always seen in the same spatial configuration) while cars look differently from different points of view.

If one can do what I outline above, then I imagine that AAM-style model fitting can be useful for distinguishing cars from buses or cows from horses (something that bags of words might not be good at). One could probably use one AAM for car/bus like things and another AAM for four legged mammals. Then the distinction between cars and buses or cows and horses could be achieved by building a classifier on top of the AAM parameter space.
Nik Melchior
11:31 PM ET (US)
As Tomasz mentioned, a good initialization is required for the use of this method. This confuses me because the last sentence of the abstract and the paper itself states that the intended application of this work is "locating deformable objects". Since AAMs need to rely on some other method for estimating the location of the objects, it seems like they would be better suited for tasks that use the converged model parameters to describe intraclass variation. The person or expression could be identified by matching in parameter space after the best fit is found.
Pete Barnum
10:19 PM ET (US)
Although it may not work for any object class, maybe it would be useful for specific classes similar to faces, such as cars. Could AAMs be used to easily find intraclass variation within cars and perhaps find them in a scene?
Krishnan Ramnath
09:19 PM ET (US)
One of the really good theses on Face Recognition Across Pose by Vetters group:
The results are on the CMU PIE(Pose Illumination Expression) dataset and the FERET dataset.
Edited 03-28-2006 09:19 PM
Krishnan Ramnath
09:11 PM ET (US)
As with most model based approaches, AAMs too require training data with hand labeled landmarks and for every object we need to build a separate model to perform recognition. However, AAMs can be very useful for "face" recognition and face expression analysis. Tasks such as "registration" and "pose normalization" become easy with model based approaches.
Tomasz Malisiewicz
07:45 PM ET (US)
Problems with this approach are: it requires expensive training data with marked landmarks, it requires a good initialization, and it doesn't easily scale to multiple object classes. AAMs are probably very good for face tracking, but I doubt they are powerful enough for 'object recognition.'
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