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Object Recognition Scenario? Landmark Detection (objects and humans) –Cluttered Environment –Levels of Occlusion –Types Color Shape Texture –Dynamic confusers –3-D objects and/or 2-D projections
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Object Recognition Tie to SES and Qualitative Spatial Reasoning Investigate relative to needs of Working Memory –Use only the sophistication required Object evidence as features for “chunks”?
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Object Recognition What methods to compare? –MSNN –Lowe's scale-invariant keypoints as features –Valvanis's neuro-fuzzy algorithm –NASA JSC's template Combinations (fusion) Hybrids (like perhaps soft templates) First task: Determine strengths and weaknesses of approaches
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Morphological Shared Weight Neural Networks A heterogeneous image-based neural network composed of two cascaded sub-nets for the feature extraction and classification Image Windows are inputs to the feature extraction layer –passed through kernels that can perform non-linear mappings using morphological structuring elements Simultaneously learn feature extraction and classification for a particular object from small amount of training data –Vehicle, structure, face Trained Network is “scanned” over test image to produce output plane Target Aim Point Selection Algorithm completes the job
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g x (z) = g(z-x), g * (z) = -g(-z) and D[g] is the domain of g (f g)(x) (f h) Erosion and Dilation Hit-Miss Transform measures how a shape h fits under f using erosion and how a shape m fits above f using dilation
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Morphological Shared Weight Neural Networks
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. and inputs represent the hit and miss operations performed through the structuring elements Many Variations
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First Developed to Find Object (Blazer) in Visible Imagery Original Frame Output Plane Target Aim Point Selection Final Output
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Blazer Test Sequences The white pixels are the TAPs selected by the algorithm
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A Blazer Sequence
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All Twelve Targets Detected with no False Alarms Target Detection in SAR Imagery
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Application to Tank Detection in (Processed) LADAR Range Images Trained on 2 frames from one sequence (8 instances) Testing on Different flight Sequence
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On To Face Recognition Typical Training Image of Bob
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Examples of “Bob” Detection
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Bob was found even with eyeglasses and sunglasses! No glasses were included in the training images.
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