Research into computer vision techniques has far outpaced the design of interfaces to support the techniques' accessibility, especially to developers not expert in vision. We present a new interface, specifically for segmentation methods, designed to be application-developer-friendly while retaining sufficient power and flexibility to solve a wide variety of problems. The interface presents segmentation at a higher level (above algorithms) and uses a task-based description derived from definitions of low-level segmentation. We show that through interpretation, the description can be used to invoke an appropriate method to provide the developer's requested result. Our proof-of-concept implementation interprets the model description and invokes one of six segmentation methods with automatically derived parameters, which we demonstrate on a range of segmentation tasks.

Presented in Ottawa, May 2014 at the Research Notes session of the GRAND Annual Conference.