A Lexical Analogy to Feature Matching and Pose Estimation

Published

January 1, 2002

Author(s)

John A. Horst

Abstract

We relate the problem of finding a correspondence between sensed and model features to that of finding a match between a random set of letters and words in a dictionary. The process is equivalent to hashing and the linguistic perspective is added to illuminate items such as design tradeoffs, computational complexity, and hashing function definition. A method for two-dimensional pose estimation based on this concept has been implemented. The method is local feature based and is robust to image warping, occlusion, illumination anomalies, and sensed feature generation errors. The method will work with certain modifications for three-dimensional data. Many pose estimation problems do not require scale and skew invariance and our method is restricted to translation and rotation invariant applications. This non-affine constraint can reduce computational and storage complexity vis- -vis a fully affine transformation invariant technique.

Citation

NIST Interagency/Internal Report (NISTIR) - 6790

Report Number

6790

NIST Pub Series

NIST Interagency/Internal Report (NISTIR)

Pub Type

NIST Pubs

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Keywords

computer vision, feature correspondence, feature matching, hash tables, hashing, linguistics, pose clustering, pose estimation

Citation

Horst, J. (2002), A Lexical Analogy to Feature Matching and Pose Estimation, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=824453 (Accessed April 28, 2024)