3d geometric hashing using transform invariant features

Eskizara, Ömer
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.


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 face reconstruction using stereo images and structured light
Öztürk, Ahmet Oğuz; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2007)
Nowadays, 3D modelling of objects from multiple images is a topic that has gained great recognition and is widely used in various fields. Recently, lots of progress has been made in identification of people using 3D face models, which are usually reconstructed from multiple face images. In this thesis, a system including stereo cameras and structured light is built for the purpose of 3D modelling. The system outputs are 3D shapes of the face and also the texture information registered to this shape. Althoug...
An FPGA implementation of real-time electro-optic & IR image fusion
Çölova, İbrahim Melih; Akar, Gözde; Department of Electrical and Electronics Engineering (2010)
In this thesis, a modified 2D Discrete Cosine Transform based electro-optic and IR image fusion algorithm is proposed and implemented on an FPGA platform. The platform is a custom FPGA board which uses ALTERA Stratix III family FPGA. The algorithm is also compared with state of the art image fusion algorithms by means of an image fusion software application GUI developed in Matlab®. The proposed algorithm principally takes corresponding 4x4 pixel blocks of two images to be fused and transforms them by means...
SystemC implementation with analog mixed signal modeling for a microcontroller
Mert, Yakup Murat; Aşkar, Murat; Department of Electrical and Electronics Engineering (2007)
In this thesis, an 8-bit microcontroller, PIC 16F871, has been implemented using SystemC with classical hardware design methods. Analog modules of the microcontroller have been modeled behaviorally with SystemC-AMS which is the analog and mixed signal extensions for the SystemC. SystemC-AMS provides the capability to model non-digital modules and synchronization with the SystemC kernel. In this manner, electronic systems that have both digital and analog components can be described and simulated very effect...
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].
Citation Formats
Ö. Eskizara, “3d geometric hashing using transform invariant features,” M.S. - Master of Science, Middle East Technical University, 2009.