Abstract
The detection and recognition of objects from image data is a difficult problem that is closely related to problems of segmentation and stable and reliable feature detection. Feature detection is dependent on a number of factors such as the resolution at which data is sensed. In normal vision systems, the sensor is static with no ability to pan or zoom. However, with the advent of active robot vision heads such as GETAFIX, there is the ability to pan and zoom onto areas of interest. In this article, the use of GETAFIX for object recognition by automatic active panning, zooming and focusing is considered. This is demonstrated by conducting experiments for the case of detecting cylindrical 3D objects in table-top scenes.