3d geometric hashing using transform invariant features

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Date

2009

Author

Eskizara, Ömer

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3D object recognition is performed by using geometric hashing where transformation and scale invariant 3D surface features are utilized. 3D features are extracted from object surfaces after a scale space search where size of each feature is also estimated. Scale space is constructed based on orientation invariant surface curvature values which classify each surface point's shape. Extracted features are grouped into triplets and orientation invariant descriptors are defined for each triplet. Each pose of each object is indexed in a hash table using these triplets. For scale invariance matching, cosine similarity is applied for scale variant triple variables. Tests were performed on Stuttgart database where 66 poses of 42 objects are stored in the hash table during training and 258 poses of 42 objects are used during testing. %90.97 recognition rate is achieved.

Subject Keywords

Electrical engineering., Computer Hardware.

URI

http://etd.lib.metu.edu.tr/upload/12610546/index.pdf
https://hdl.handle.net/11511/18728

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

Ö. Eskizara, “3d geometric hashing using transform invariant features,” M.S. - Master of Science, Middle East Technical University, 2009.