By Topic

Using perceptual organization to extract 3D structures

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
$33 $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

2 Author(s)
R. Mohan ; Dept. of Comput. Sci. & Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA ; R. Nevatia

The authors describe an approach to perceptual grouping for detecting and describing 3-D objects in complex images and apply it to the task of detecting and describing complex buildings in aerial images. They argue that representations of structural relationships in the arrangements of primitive image features, as detected by the perceptual organization process, are essential for analyzing complex imagery. They term these representations collated features. The choice of collated features is determined by the generic shape of the desired objects in the scene. The detection process for collated features is more robust than the local operations for region segmentation and contour tracing. The important structural information encoded in collated features aids various visual tasks such as object segmentation, correspondence processes, and shape description. The proposed method initially detects all reasonable feature groupings. A constraint satisfaction network is then used to model the complex interactions between the collations and select the promising ones. Stereo matching is performed on the collations to obtain height information. This aids in further reasoning on the collated features and results in the 3-D description of the desired objects

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:11 ,  Issue: 11 )