High level vision is concerned mainly with the interpretation of scene in term of the objects in it, and is usually based on knowledge of specific objects and relationships. The analysis usually involves symbolic descriptions, although it might make reference to results from the low and middle levels to verify hypotheses. Search among different hypotheses commonly occurs. Typical results of high level analysis are a naming of objects present in the scene, estimates of their position, identification of objects that can satisfy a particular function, descriptions of what sorts of motions are occurring, or summaries of what sort of scene it is (e.g. an office scene). In the coffee mug example, the results might say that we are looking at a coffee mug, sitting in a desk at a given position, the mug is half-full, there is nothing else nearby that could be used to hold coffee, and the desk is cluttered.

Matching Models to Scene Descriptions

Matching and Locating 3D Models in 2D Images

Representing Three Dimensional Solid Objects

Viewer Centred Representations

Pose Estimation from Corresponding Feature Data

A working model of an interpretation tree search, programmed in Java

Comments to: Sarah Price at ICBL.
(Last update: 4th July, 1996)