April 2000, IEEE ICRA Dudek & Jugessur Dudek & Jugessur, ICRA 200 Robust Place and Object Recognition using Local Appearance based Methods Gregory Dudek and Deeptiman Jugessur Center for Intelligent Machines McGill University + QuickTime™ and a Animation decompressor are needed to see this picture.
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Dudek & Jugessur, ICRA 2000. April 2000, IEEE ICRADudek & Jugessur Robust Place and Object Recognition using Local Appearance based Methods Gregory Dudek.
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April 2000, IEEE ICRA Dudek & Jugessur
Dudek & Jugessur, ICRA 2000.
Robust Place and Object Recognition using Local Appearance based Methods
• Object recognition: what is that thing?– Recognizing a known object from its visual appearance.
– Landmarks, grasping targets, etc.
• Place recognition (coarse localization): what room am I in?– Recognizing the current waypoint on a trajectory,
validating the current locale for the application of a precise localization method, topological navigation.
April 2000, IEEE ICRA Dudek & Jugessur
Dudek & Jugessur, ICRA 2000.
PCA-based recognition.
• Has now become a well established method for image recognition.
• PCA-based recognition: global transform of image with N degrees of freedom into an eigenspace with M << N degrees of freedom.– Freedoms M are the “most important” characteristics of
the set of images being memorized.
• Avoids having to segment image into object & background by using the whole thing.
April 2000, IEEE ICRA Dudek & Jugessur
Dudek & Jugessur, ICRA 2000.
Observations
• Using whole image implies recognizing combination of object AND background.
• Segmenting object from background would avoid dependence on background, but it’s too difficult.
• Using a small sub-region gives a less precise recognition (e.e. the sun-window could come from more than one image), it’s is efficient.
• Many subwindows together can “vote” for an unambiguous recognition.
• If the sub-windows are suitably chosen, they may totally ignore the background.
April 2000, IEEE ICRA Dudek & Jugessur
Dudek & Jugessur, ICRA 2000.
Problem Statement
• Improving the performance of classic PCA based recognition by accounting for:
– Varying backgrounds
– Planar rotations
– Occlusions
• Also (discussed in less detail) – Changes in object pose
– Non-rigid deformation
April 2000, IEEE ICRA Dudek & Jugessur
Dudek & Jugessur, ICRA 2000.
Our key idea(s).
• Use sub-windows: several together uniquely accomplish recognition.
• Sub-windows are selected by an attention operator (several kinds can be used).
• Each sub-window is sampled non-uniformly to weight it towards it’s center.
• Use only the amplitude spectrum to buy rotational invariance.
April 2000, IEEE ICRA Dudek & Jugessur
Dudek & Jugessur, ICRA 2000.
Background
• Standard Appearance Based Recognition– M. Turk and S. Pentland 1991
– S.K. Nayar, H. Murase, S.A. Nene 1994
– H. Murase, S.K. Nayar 1995
– Shortcomings (due to global approach):• Background
• Scale
• Rotations
• Local changes of the image or object
• Occlusion
April 2000, IEEE ICRA Dudek & Jugessur
Dudek & Jugessur, ICRA 2000.
Background (part 2)
• “Enhanced” Local sub-window methods– D. Lowe 1999: scale invariance, simple features.
– C. Schmid 1999: Probabilistic approach based on sub-windows extracted using Harris operator.
– C. Schmid & R. Mohr 1997: numerous sub-windows extracted using Harris operator for database image retrieval (simpler problem).
– K. Ohba & K. Ikeuchi 1997: K.L.T. operator used for the extraction
of sub-windows for the creation of an eigenspace. Only handles occlusion.
• Interest Operator of choice:– D. Reisfeld, H. Wolfson, Y.Yeshurun 1995: Local symmetry operator
April 2000, IEEE ICRA Dudek & Jugessur
Dudek & Jugessur, ICRA 2000.
Approach
• 2 phases:
– Training (off-line) for the entire database of recognizable images:
• Run an interest operator to obtain a saliency map for each image.
• Choose sub-windows around the salient points for each image.
• Select most informative sub-windows and use foveal sampling.
• Create the eigenspace with the processed sub-windows.
– Testing (on-line) for a candidate test image:
• Run the same interest operator to obtain the saliency map.
• Choose the sub-windows and process the information within them.
• Project the sub-windows onto the eigenspace
• Perform classification based on nearest neighbor rules.