Name
Abstract | ||
|---|---|---|
It is too hard to tell vision systems what things look like. It is easier to talk about purpose and what things are for. Consequently, we want vision systems to use functional descriptions to identify things, when necessary, and we want them to learn physical descriptions for themselves, when possible. This paper describes a theory that explains how to make such systems work. The theory is a synthesis of two sets of ideas: ideas about learning from precedents and exercises developed at MIT and ideas about physical description developed at Stanford. The strength of the synthesis is illustrated by way of representative experiments. All of these experiments have been performed with an implementation system. |
Year | Venue | Keywords |
|---|---|---|
1983 | AAAI | implementation system,functional description,systems work,physical description,vision system,functional definition,representative experiment,physical properties |
Field | DocType | Citations |
Computer science,Artificial intelligence,Machine learning | Conference | 60 |
PageRank | References | Authors |
89.40 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick H. Winston | 1 | 370 | 559.01 |
| Thomas O. Binford | 2 | 915 | 1430.05 |
| Boris Katz | 3 | 60 | 89.40 |
| Michael Lowry | 4 | 231 | 97.94 |