3D object representation using transform and scale invariant 3D features

Date

2007-10-21

Author

AKAGÜNDÜZ, Erdem
Ulusoy, İlkay

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

38
views

0
downloads

An algorithm is proposed for 3D object representation using generic 3D features which are transformation and scale invariant. Descriptive 3D features and their relations are used to construct a graphical model for the object which is later trained and then used for detection purposes. Descriptive 3D features are the fundamental structures which are extracted from the surface of the 3D scanner output. This surface is described by mean and Gaussian curvature values at every data point at various scales and a scale-space search is performed in order to extract the fundamental structures and to estimate the location and the scale of each fundamental structure.

Subject Keywords

Shape, Inference, Algorithm

URI

https://hdl.handle.net/11511/40319

DOI

https://doi.org/10.1109/iccv.2007.4408835

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Extraction of 3D transform and scale invariant patches from range scans Akagunduz, Erdern; Ulusoy, İlkay (2007-06-22) An algorithm is proposed to extract transformation and scale invariant 3D fundamental elements from the surface structure of 3D range scan data. The surface is described by mean and Gaussian curvature values at every data point at various scales and a scale-space search is performed in order to extract the fundamental structures and to estimate the location and the scale of each fundamental structure. The extracted fundamental structures can later be used as nodes in a topological graph where the links betw...
3D Object Recognition by Geometric Hashing Eskizara, Omer; Akagündüz, Erdem; Ulusoy, İlkay (2009-01-01) Using transform invariant 3D fatures obtained from a database of 3D range images, geometric hashing is applied for the purpose of 3D object recognition. Mean (H) and Gaussian (K) curvature values within a scale-space of the surface is used Since H and K values are used and a scale-space of the surface is constructed the method is independent of transformation and resolution. The method is tested on the Stuttgart 3D range image database [1].
3D face detection using transform invariant features AKAGÜNDÜZ, erdem; Ulusoy, İlkay (2010-06-24) A generic, transform invariant 3D facial feature detection method based on mean (H) and Gaussian (K) curvature analysis is proposed. A scale space of the HK values is constructed differently from the previous HK attempts. The 3D features are extracted from this scale space and used in a global topology, which is trained with a Gaussian model using only faces with neutral and frontal poses. The model is then tested against 1323 faces with various poses and expressions. The method is compared with four other ...
Shape Optimizations of Metallic Sheets Using a Multigrid Approach Altinoklu, Askin; Karaova, Gokhan; Ergül, Özgür Salih (2017-09-27) We present a novel multigrid approach for the shape optimizations of corrugated metallic sheets by using genetic algorithms (GAs) and the multilevel fast multipole algorithm (MLFMA). The overall mechanism is obtained by an efficient integration of GAs and MLFMA, while the optimizations are improved by applying multiple grids at different layers. We show that the multigrid approach provides more effective optimizations than the conventional no-grid optimizations that employ the discretization nodes directly....
3d geometric hashing using transform invariant features Eskizara, Ömer; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2009) 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 eac...

Citation Formats

E. AKAGÜNDÜZ and İ. Ulusoy, “3D object representation using transform and scale invariant 3D features,” 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40319.