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Object Recognition

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. Object Recognition. Tie to SES and Qualitative Spatial Reasoning

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Object Recognition

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  1. 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

  2. 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”?

  3. 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

  4. 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

  5. (f h) (f g)(x) Erosion and Dilation gx(z) = g(z-x), g*(z) = -g(-z) and D[g] is the domain of g Hit-Miss Transform measures how a shape h fits under f using erosion and how a shape m fits above f using dilation

  6. Morphological Shared Weight Neural Networks

  7. .and inputs represent the hit and miss operations performed through the structuring elements Many Variations

  8. Output Plane Original Frame Final Output Target Aim Point Selection First Developed to Find Object (Blazer) in Visible Imagery

  9. Blazer Test Sequences The white pixels are the TAPs selected by the algorithm

  10. A Blazer Sequence

  11. All Twelve Targets Detected with no False Alarms Target Detection in SAR Imagery

  12. Application to Tank Detection in (Processed) LADAR Range Images Trained on 2 frames from one sequence (8 instances) Testing on Different flight Sequence

  13. On To Face Recognition Typical Training Image of Bob

  14. Examples of “Bob” Detection

  15. Examples of “Bob” Detection Bob was found even with eyeglasses and sunglasses! No glasses were included in the training images.

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