article · 1997

Efficient Pose Clustering Using a Randomized Algorithm

Clark F. Olson

Cited by 0Abstract and details only

Abstract

. Pose clustering is a method to perform object recognition by determining hypothetical object poses and finding clusters of the poses in the space of legal object positions. An object that appears in an image will yield a large cluster of such poses close to the correct position of the object. If there are m model features and n image features, then there are O(m 3 n 3 ) hypothetical poses that can be determined from minimal information for the case of recognition of three-dimensional objects from feature points in two-dimensional images. Rather than clustering all of these poses, we show that pose clustering can have equivalent performance for this case when examining only O(mn) poses, due to correlation between the poses, if we are given two correct matches between model features and image features. Since we do not usually know two correct matches in advance, this property is used with randomization to decompose the pose clustering problem into O(n 2 ) problems, each of which...

Cite this paper

APA 7

Olson, C. F. (1997). Efficient Pose Clustering Using a Randomized Algorithm.

MLA 9

Olson, Clark F. “Efficient Pose Clustering Using a Randomized Algorithm.” 1997.

Chicago

Olson, Clark F. 1997. “Efficient Pose Clustering Using a Randomized Algorithm.”

Harvard

Olson, C.F. (1997) ‘Efficient Pose Clustering Using a Randomized Algorithm’.

BibTeX

@article{olson1997efficient,
  title = {{Efficient Pose Clustering Using a Randomized Algorithm}},
  author = {Olson, Clark F.},
  year = {1997}
}

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