@inproceedings{ecf4114d60bd4e1aa53a0659be131534,
title = "Probabilistic hypothesis generation for rapid 3D object recognition",
abstract = "A major concern in practical vision systems is how to retrieve the best matched models without exploring all possible object matches. This research presents probabilistic hypothesis generation based on indexing approach for the rapid recognition of three dimensional objects. We have defined the discriminatory power of a feature for a model object is defined in terms of a posteriori probability. This measure displays belief that a model appears in the scene after a feature is observed. We compute off-line the discriminatory power of features for model objects from CAD model data using computer graphic techniques. In order to speed up the indexing or selection of correct objects, we generate and verify the object hypotheses for features detected in a scene in the order of the discriminatory power of these features for model objects. Experimental results on synthetic and real range images show the effectiveness of our probabilistic method for hypothesis generation.",
keywords = "3D, Indexing, Object recognition, Probabilistic",
author = "Yi, \{June Ho\}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 4th International Workshop on Visual Form, IWVF4 2001 ; Conference date: 28-05-2001 Through 30-05-2001",
year = "2001",
doi = "10.1007/3-540-45129-3\_58",
language = "English",
isbn = "3540421203",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "630--639",
editor = "Carlo Arcelli and \{di Baja\}, \{Gabriella Sanniti\} and Cordella, \{Luigi P.\}",
booktitle = "Visual Form 2001 - 4th International Workshop on Visual Form, IWVF4, Proceedings",
}