Recognition of 3D Objects Based on Implicit Polynomials

Closed-form expressions for a new set of 3D rotation invariants that are linear, quadratic, and angular combinations of implicit polynomial (IP) coefficients are developed. Based on these invariants, we propose a 3D object recognition method that outperforms recognition based on IP fitting after pos...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2010-05, Vol.32 (5), p.954-960
Hauptverfasser:	Ben-Yaacov, H., Malah, D., Barzohar, M.
Format:	Artikel
Sprache:	eng
Schlagworte:	3D object fitting 3D object recognition Algorithms Applied sciences Artificial Intelligence Computer science control theory systems Computer systems and distributed systems. User interface Exact sciences and technology Exact solutions Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Implicit polynomials Intelligence Invariants IP (Internet Protocol) Iterative algorithms Iterative methods Least squares methods Mathematical analysis MPEG 7 Standard Object recognition Pattern analysis Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Polynomials Principal component analysis Recognition rotation-invariant Software Stability Subtraction Technique Surface fitting Tensile stress tensor contraction Three dimensional
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Beschreibung
Zusammenfassung:	Closed-form expressions for a new set of 3D rotation invariants that are linear, quadratic, and angular combinations of implicit polynomial (IP) coefficients are developed. Based on these invariants, we propose a 3D object recognition method that outperforms recognition based on IP fitting after pose estimation, and the MPEG-7 SSD technique.
ISSN:	0162-8828 1939-3539
DOI:	10.1109/TPAMI.2009.197