Share Email Print
cover

Proceedings Paper

Three-dimensional line interpretation via local processing
Author(s): Alexander P. Pentland; Jeff Kuo
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The interpretation of line drawings is known to be very difficult, and has a long history in vision research. However for certain restricted but important types of drawings we have been able to produce good 3-D interpretations quite efficiently using only local image-plane computations. The types of drawings we can handle are line drawings of 3-D space curves, for instance, a drawing of the 3-D path followed by a butterfly or a line drawing of a potato chip. Such line drawings are, of course, intrinsically ambiguous - there is simply not enough information in the 2-D image to arrive at a unique 3-D interpretation. Despite this difficulty, there remains the fact that for any given image all people see pretty much exactly the same 3-D interpretation (or sometimes a small number of interpretations). People, therefore, must be bringing additional knowledge or assumptions to the problem. In this paper we show that by picking the smoothest 3-D space curve that is consistent with the image data we can obtain a 3-D interpretation which is very similar to the people's interpretation. The teleological motivation for selecting the smoothest 3-D space curve is that it is the most stable 3-D interpretation, and thus in one sense the most likely 3-D interpretation. The process of computing the smoothest 3-D space curve is carried out by simple, local processing that can be implemented by a neural network.

Paper Details

Date Published: 1 October 1990
PDF: 7 pages
Proc. SPIE 1249, Human Vision and Electronic Imaging: Models, Methods, and Applications, (1 October 1990); doi: 10.1117/12.19685
Show Author Affiliations
Alexander P. Pentland, Massachusetts Institute of Technology (United States)
Jeff Kuo, Massachusetts Institute of Technology (United States)


Published in SPIE Proceedings Vol. 1249:
Human Vision and Electronic Imaging: Models, Methods, and Applications
Bernice E. Rogowitz; Jan P. Allebach, Editor(s)

© SPIE. Terms of Use
Back to Top