We present a design methodology for real-time vision applications aiming at significantly reducing the design-implement-validate cycle time on dedicated parallel platforms. This methodology is based upon the concept of algorithmic skeletons, i.e., higher order program constructs encapsulating recurring forms of parallel computations and hiding their low-level implementation details. Parallel programs are built by simply selecting and composing instances of skeletons chosen in a predefined basis. A complete parallel programming environment was built to support the presented methodology. It comprises a library of vision-specific skeletons and a chain of tools capable of turning an architecture-independent skeletal specification of an application into an optimized, deadlock-free distributive executive for a wide range of parallel platforms. This skeleton basis was defined after a careful analysis of a large corpus of existing parallel vision applications. The source program is a purely functional specification of the algorithm in which the structure of a parallel application is expressed only as combination of a limited number of skeletons. This specification is compiled down to a parametric process graph, which is subsequently mapped onto the actual physical topology using a third-party CAD software. It can also be executed on any sequential platform to check the correctness of the parallel algorithm. The applicability of the proposed methodology and associated tools has been demonstrated by parallelizing several realistic real-time vision applications both on a multi-processor platform and a network of workstations. It is here illustrated with a complete road-tracking algorithm based upon white-line detection. This experiment showed a dramatic reduction in development times (hence the term fast prototyping), while keeping performances on par with those obtained with the handcrafted parallel version.