Abstract
Object identification by matching is a central problem in computer vision. A major problem that any object matching method must address is the ability to correctly match an object to its model when parts of the object is missing due to occlusion, shadows, ... etc. In this paper we introduce boundary signatures as an extension to our surface signature formulation. Boundary signatures are surface feature vectors that reflect the probability of occurrence of a feature of a surface boundary. We introduce four types of surface boundary signatures that are constructed based on local and global geometric shape attributes of the boundary. Tests conducted on incomplete object shapes have shown that the Distance Boundary Signature produced excellent results when the object retains at least 70% of its original shape.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mustafa, A. A., Shapiro, L. G. and Ganter, M. A. 1996. “3D Object Recognition from Color Intensity Images”. In the 13th International Conference on Pattern Recognition, Vienna, Austria, pp. 627–631, August 25-30.
Mustafa, A. A., Shapiro, L. G. and Ganter, M. A. 1997. “Object Identification with Surface Signatures”. The 7th International Conference on Computer Analysis of Images and Patterns, Kiel, Germany, pp. 58–65, Sept. 10-12.
Mustafa, A. A., Shapiro, L. G. and Ganter, M. 1999. “3D Object Identification with Color and Curvature Signatures”. Pattern Recognition, Elsevier Science. Vol. 32, No. 3, pg. 1–17.
Hong, D., Sarkodie-Gyan, T., Campbell, A. and Yan, Y. 1998. “A Prototype Indexing Approach To 2-D Object Description and Recognition”. Pattern Recognition, Elsevier Science. Vol. 31, No. 6, pg. 699–725.
Ozcan, E. and Mohan, C. 1997. “Partial shape matching using genetic algorithms”. Pattern Recognition Letters, Elsevier Science.V 18, pg. 987–992.
Roh, K. and Kweon, I. 1998. “2D Object Recognition Using Invariant Contour Descriptor And Projective Refinement”. Pattern Recognition, Elsevier Science. V 31, N 4, pg. 441–455.
Nishida, Hirobumi. 1998. “Matching and Recognition of Deformed Closed Contours Based on Structural Transformation Models”, Pattern Recognition, Elsevier Science. Vol. 31, No. 10, pg. 1557–1571.
Kovalev, V. and Petrou, M. 1996. “Multidimensional Co-occurrence Matrices for Object Recognition and Matching”, GMIP, V58 (3), pp. 187–197.
Mokhtarian, F., Abbasi, S and Kittler, J 1996. “Robust and Eficient Shape Indexing through Curvature Scale Space”. In the British Machine Vision Conference, Edinburgh, pg. 53–62.
Loncaric, Sven 1998. “A survey of shape analysis techniques”. Pattern Recognition, Elsevier Science. Vol. 31, No. 8, pg. 983–1001.
Mustafa, Adnan A. “Object Matching using Shape Surface Signatures”. SPIE conference on Machine Vision Applications in Industrial Inspection IX, San Jose, Ca, Jan. 22-23, 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mustafa, A.A.Y. (2001). Matching Incomplete Objects Using Boundary Signatures. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form 2001. IWVF 2001. Lecture Notes in Computer Science, vol 2059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45129-3_52
Download citation
DOI: https://doi.org/10.1007/3-540-45129-3_52
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42120-7
Online ISBN: 978-3-540-45129-7
eBook Packages: Springer Book Archive