  Image processing applications are common in every field of our daily life.
However, most of them are very complex and contain several tasks with different
complexities which result in varying requirements for computing architectures.
Nevertheless, a general processing scheme in every image processing application
has a similar structure, called image processing pipeline: (1) capturing an
image, (2) pre-processing using local operators, (3) processing with global
operators and (4) post-processing using complex operations. Therefore,
application-specialized hardware solutions based on heterogeneous architectures
are used for image processing. Unfortunately the development of applications
for heterogeneous hardware architectures is challenging due to the distribution
of computational tasks among processors and programmable logic units. Nowadays,
image processing systems are started from scratch which is time-consuming,
error-prone and inflexible. A new methodology for modeling and implementing is
needed in order to reduce the development time of heterogenous image processing
systems. This paper introduces a new holistic top down approach for image
processing systems. Two challenges have to be investigated. First, designers
ought to be able to model their complete image processing pipeline on an
abstract layer using UML. Second, we want to close the gap between the abstract
system and the system architecture.
