Statistics – Applications
Scientific paper
Oct 1997
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997spie.3164..198l&link_type=abstract
Proc. SPIE Vol. 3164, p. 198-209, Applications of Digital Image Processing XX, Andrew G. Tescher; Ed.
Statistics
Applications
Scientific paper
The recognition of moving 3D objects is one of the most important tasks of future vision systems. Several techniques have been proposed using a sequence of images. Image difference thresholding, optical flow, token tracker...are a few examples. The main inconvenience is the amount of calculation that cannot meet the real time constraints, even on parallel computers. The snakes approach, also called 'active edges' seems more suitable for 3D-object recognition and tracking. We used this concept as a basic tool in our model-based recognition method. The proposed approach uses an elementary geometric property: if the 2D projections of 3D objects are similar, then 3D objects can be identical. A conveniently defined series of 2D projections can allow us to find correct associations of the representative projections of known objects memorized in image database. The snakes are used as an efficient means for location/tracking of a given object in a sequence of images. The good results obtained with this technique on a SUN Sparc workstation allows us to expect similar satisfaction on dedicated computer under strong real-time conditions.
Bonnin Patrick J.
Le Cong Trinh
Lemasson Eric
Pissaloux Edwige
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