Scale Invariant Sillhouette Features

Date

2013-01-01

Author

Akagündüz, Erdem

Metadata

Show full item record

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

Item Usage Stats

98
views

0
downloads

In this study, a feature extractor and a global descriptor for closed planar curves, i.e. silhouettes, are proposed. Initially, the closed curve is arc-length sampled and the Gaussian scale-space is constructed. Using the absolute curvature values and orientations of the curves within the higher scale levels, scale invariant features are obtained. These features are transformed into a global descriptor, namely the feature images, and shape recognition is performed. The proposed method is evaluated using a ship silhouette image set and the results show good success rates with low computation burden.

URI

https://hdl.handle.net/11511/94418

Conference Name

21st Signal Processing and Communications Applications Conference (SIU)

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Scale invariant representation of 2 5D data AKAGUNDUZ, Erdem; ULUSOY PARNAS, İLKAY; BOZKURT, Nesli; Halıcı, Uğur (2007-06-13) In this paper, a scale and orientation invariant feature representation for 2.5D objects is introduced, which may be used to classify, detect and recognize objects even under the cases of cluttering and/or occlusion. With this representation a 2.5D object is defined by an attributed graph structure, in which the nodes are the pit and peak regions on the surface. The attributes of the graph are the scales, positions and the normals of these pits and peaks. In order to detect these regions a "peakness" (or pi...
Shape recognition using orientational and morphological scale-spaces of curvatures Akagündüz, Erdem (2015-10-01) In this study, a scale-invariant representation for closed planar curves (silhouettes) is proposed. The orientations of all points within the Gaussian scale-space of the curve are extracted. This orientation scale-space is used to create the silhouette orientation image in which the positions of each pixel indicate the curve's pixel positions and scales, whereas the colour represents orientation. The representation is extracted for multiple levels of the morphological scale-space of the silhouette. The prop...
Shape recognition with generalized beam angle statistics Tola, OO; Arica, N; Yarman Vural, Fatoş Tunay (2004-04-30) In this study, we develop a new shape descriptor and matching algorithm in order to find a given template shape in an edge detected image without performing boundary extraction. The shape descriptor based on Generalized Beam Angle Statistics (GBAS) defines the angles between the lines connecting each boundary point with the rest of the points, as random variable. Then, it assigns a feature vector to each point using the moments of beam angles. The proposed matching algorithm performs shape recognition by ma...
Accurate equivalent models of sandwich laminates with honeycomb core and composite face sheets via optimization involving modal behavior Cinar, Okan; Erdal Erdoğmuş, Merve; Kayran, Altan (2017-03-01) An approach is introduced for determining accurate two-dimensional equivalent laminated models of sandwich laminates with honeycomb core and composite facesheets by optimization involving modal behavior. The approach relies on minimizing the objective function which is defined as the sum of the square of the differences between the natural frequencies of the honeycomb sandwich laminate estimated by the finite element analysis of the 3D detailed model with the actual honeycomb core geometry and by the 2D equ...
3d face representation and recognition using spherical harmonics Tunçer, Fahri; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2008) In this study, a 3D face representation and recognition method based on spherical harmonics expansion is proposed. The input data to the method is range image of the face. This data is called 2.5 dimensional. Input faces are manually marked on the two eyes, nose and chin points. In two dimensions, using the marker points, the human face is modeled as two concentric half ellipses for the selection of region of interest. These marker points are also used in three dimensions to register the faces so that the n...

Citation Formats

E. Akagündüz, “Scale Invariant Sillhouette Features,” presented at the 21st Signal Processing and Communications Applications Conference (SIU), CYPRUS, 2013, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/94418.