Methods and systems for feature extraction of LIDAR surface manifolds. LIDAR point data with respect to one or more LIDAR surface manifolds can be generated. An AHAH-based feature extraction operation can be automatically performed on the point data for compression and processing thereof. The results of the AHAH-based feature extraction operation can be output as a compressed binary label representative of the at least one surface manifold rather than the point data to afford a high-degree of compression for transmission or further processing thereof. Additionally, one or more voxels of a LIDAR point cloud composed of the point data can be scanned in order to recover the compressed binary label, which represents prototypical surface patches with respect to the LIDAR surface manifold(s).
Please help me write a proper abstract based on the patent claims. CLAIM: 1. (canceled) 2. (canceled) 3. (canceled) 4. (canceled) 5. (canceled) 6. (canceled) 7. (canceled) 8. (canceled) 9. (canceled) 10. (canceled) 11. (canceled) 12. (canceled) 13. (canceled) 14. (canceled) 15. (canceled) 16. (canceled) 17. (canceled) 18. (canceled) 19. (canceled) 20. (canceled) 21. A system for feature extraction of surface manifolds, said system comprising: an optical remote sensor that measures a distance to and/or other properties of a target by illuminating said target with light, wherein said optical remote sensor provides sensor data indicative of at least one surface manifold and wherein point data is generated with respect to said at least one surface manifold; and an AHAH (Anti-Hebbian and Hebbian) based mechanism that performs an AHAH-based feature extraction operation on said point data for compression and processing thereof. 22. The system of claim 21 wherein said optical remote sensor comprises a LIDAR (Light Detection and Ranging) device. 23. The system of claim 21 further comprising scanning at least one voxel of a point cloud composed of said point data in order to recover said compressed binary label which represents prototypical surface patches with respect to said at least one surface manifold. 24. The system of claim 21 wherein said point data comprises LIDAR point data and said surface manifolds comprises LIDAR surface manifolds. 25. The system of claim 24 wherein said LIDAR point data is capable of being represented as a voxel with at least one active grid element and at least one inactive grid element, wherein a voxel grid element is considered active if said voxel grid element contains within a spatial boundary thereof at least one LIDAR point. 26. The system of claim 24 wherein said output of said voxel comprises a sparse binary activation vector including grid elements that are active. 27. The system of claim 21 further comprising a plurality of meta-stable switches, said AHAH based mechanism comprising said plurality of meta-stable switches. 28. The system of claim 27 wherein each meta-stable switch among said plurality of meta-stable switches comprises a self-organizing unit. 29. The system of claim 27 wherein each meta-stable switch among said plurality of meta-stable switches comprises a self-organizing node. 30. The system of claim 27 wherein said AHAH based mechanism comprises at least one memristor composed of said plurality of meta-stable switches. 31. The system of claim 21 wherein said AHAH based mechanism comprises at least one AHAH node comprising a plurality of synapses and associated feedback circuitry. 32. The system of claim 31 wherein said associated feedback circuitry acts on at least one of three electrode configurations including a 1-2 electrode configuration, a 2-1 electrode configuration, or a 2-2 electrode configuration. 33. (canceled) 34. (canceled) 35. (canceled) 36. (canceled)