A uniform interface for the data exchange between image segmentation and high-level image analysis is presented, termed here an 'iconic-symbolic interface'. The interface is specified as a class in an object-oriented programming environment. The term 'iconic processing' is contrasted to 'iconic data structures.' Symbolic processing is separated from iconic processing by the use of explicitly represented knowledge about the task domain. Many segmentation algorithms may be performed independent of the task domain. It is shown that the same holds for the recovery of depth and surface information by shape from shading or stereo and for the detection of motion. Several data structures for the representation of the results of segmentation are compared. The new class 'segmentation object' (i.e., the data structure and the required operations on it) is defined as a superset of the other proposed data structures. It allows for a uniform representation for 2-D and 3-D image segmentation and for motion detection. The interface to symbolic processing is defined by a machine-independent external representation of the segmentation object. Compactness is obtained by binary storage. International standardization of low-level image preprocessing and of an image interchange format is in process. A future standard can cooperate with the external representation of segmentation objects.