Scale invariant representation of 2 5D data

Date

2007-06-13

Author

AKAGUNDUZ, Erdem
ULUSOY PARNAS, İLKAY
BOZKURT, Nesli
Halıcı, Uğur

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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 pitness) measure is defined based on Gaussian curvature calculation, which is performed at various scales on the surface. Finally a "position vs. scale" feature volume is obtained and the graph nodes are extracted from this feature space by volume segmentation techniques.

Subject Keywords

Object detection, Performance evaluation, Feature extraction, Solid modeling, Gaussian processes

URI

https://hdl.handle.net/11511/52695

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Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. AKAGUNDUZ, İ. ULUSOY PARNAS, N. BOZKURT, and U. Halıcı, “Scale invariant representation of 2 5D data,” 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52695.