3D Object Recognition by Geometric Hashing

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 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...
3D object representation using transform and scale invariant 3D features
AKAGÜNDÜZ, Erdem; Ulusoy, İlkay (2007-10-21)
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 ...
3D object recognition from range images using transform invariant object representation
AKAGÜNDÜZ, erdem; Ulusoy, İlkay (Institution of Engineering and Technology (IET), 2010-10-28)
3D object recognition is performed using a scale and orientation invariant feature extraction method and a scale and orientation invariant topological representation. 3D surfaces are represented by sparse, repeatable, informative and semantically meaningful 3D surface structures, which are called multiscale features. These features are extracted with their scale (metric size and resolution) using the classified scale-space of 3D surface curvatures. Triplets of these features are used to represent the surfac...
3D object recognition using scale space of curvatures
Akagündüz, Erdem; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2011)
In this thesis, a generic, scale and resolution invariant method to extract 3D features from 3D surfaces, is proposed. Features are extracted with their scale (metric size and resolution) from range images using scale-space of 3D surface curvatures. Different from previous scale-space approaches; connected components within the classified curvature scale-space are extracted as features. Furthermore, scales of features are extracted invariant of the metric size or the sampling of the range images. Geometric ...
Yasaroglu, Yagiz; Alatan, Abdullah Aydın (2014-10-30)
A novel watermarking method is presented in which the data embedded into a 3D model is extracted from an arbitrary 2D view by using a perspective projective invariant. The data is embedded into 3D positions of selected interest points on a 3D mesh. Determining the interest point modification vectors for ensuring watermark detection constitutes an important part of the proposed method. Different watermark embedding schemes based on optimization of the watermark function are implemented and evaluated. Another...
Citation Formats
O. Eskizara, E. Akagündüz, and İ. Ulusoy, “3D Object Recognition by Geometric Hashing,” presented at the IEEE 17th Signal Processing and Communications Applications Conference, Antalya, Türkiye, 2009, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/94340.