By Topic

View-based recognition using an eigenspace approximation to the Hausdorff measure

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Huttenlocher, D.P. ; Dept. of Comput. Sci., Cornell Univ., Ithaca, NY, USA ; Lilien, R.H. ; Olson, C.F.

View-based recognition methods, such as those using eigenspace techniques, have been successful for a number of recognition tasks. Such approaches, however, are somewhat limited in their ability to recognize objects that are partly hidden from view or occur against cluttered backgrounds. In order to address these limitations, we have developed a view matching technique based on an eigenspace approximation to the generalized Hausdorff measure. This method achieves compact storage and fast indexing that are the main advantages of eigenspace view matching techniques, while also being tolerant of partial occlusion and background clutter. The method applies to binary feature maps, such as intensity edges, rather than directly to intensity images

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:21 ,  Issue: 9 )