Robust estimation of adaptive tensors of curvature by tensor voting

Although curvature estimation from a given mesh or regularly sampled point set is a well-studied problem, it is still challenging when the input consists of a cloud of unstructured points corrupted by misalignment error and outlier noise. Such input is ubiquitous in computer vision. In this paper, w...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2005-03, Vol.27 (3), p.434-449
Hauptverfasser:	TONG, Wai-Shun, TANG, Chi-Keung
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Artificial Intelligence Clouds Computer errors Computer science control theory systems Computer vision Curvature curvature tensor Error correction Errors Exact sciences and technology Feedback Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Index Terms- Curvature Information Storage and Retrieval - methods Mathematical analysis Misalignment Noise Noise robustness Numerical Analysis, Computer-Assisted Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Quantization Reproducibility of Results Scattering Sensitivity and Specificity Studies Subtraction Technique Surface fitting Tensile stress tensor voting Tensors Voting
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!