z-logo
open-access-imgOpen Access
Relative motion and pose from invariants
Author(s) -
Andrew Zisserman,
C. Marinos,
David Forsyth,
Joseph L. Mundy,
C. A. Rothwcll
Publication year - 1990
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5244/c.4.4
Subject(s) - artificial intelligence , computer vision , invariant (physics) , pose , articulated body pose estimation , planar , computer science , representation (politics) , object (grammar) , 3d pose estimation , position (finance) , reference frame , frame (networking) , mathematics , pattern recognition (psychology) , computer graphics (images) , telecommunications , finance , politics , political science , law , economics , mathematical physics
Projectively invariant shape descriptors efficiently identify instances of object models in images without reference to object pose. These descriptions rely on frame independent representations of planar curves, using plane conies. We show that object pose can be determined from coplanar curves, given such a frame independent representation. This result is demonstrated for real image data. The shape of objects in images changes as the camera is moved around. This extremely simple observation represents the dominant problem in model based vision. Nielsen [4, 5] first suggested using projectively invariant labels as landmarks for navigation. Recent papers [1, 2] have shown that it is possible to compute shape descriptors of arbitrary plane objects that are unaffected by camera position. These descriptors are known as transformational invariants. At no stage in this process, however, is the pose of the model determined. In this paper, we show that the available information does in fact determine the pose of the model. In particular, for complex planar objects, pose determination can be reduced to the simpler problem of pose determination for a pair of known planar conies.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom