The goal of this project is to create a hardware/software framework for quick and easy implementation and deployment of applications from the field of image processing / computer vision for a massive parallel and reconfigurable processing platform, which is also developed within this project. For this purpose, a toolchain will be developed, which allows to easily implement, test and evaluate various image processing applications. The complexity of these applications ranges from simple image filters to complex object recognition using state of the art artificial neural networks including their training. Various processing chains shall be able to be constructed from simpler modules according to a modular principle. These modules should then - transparent for the creator of the application - be mapped via a conversion tool to a highly optimized embedded processor platform. The platform implements a scalable, massive parallel architecture with numerous cores, which will be optimized for the Dream Chip Technologies (DCT) DCT10A System-on-Module platform. This architecture is designed as a programmable array, which can be connected as a co-processor to an arbitrating host processor. By implementing appropriate models profiling information regarding computing power and required number of computation cores should be presented to the application programmer for evaluation before the actual mapping to the hardware is conducted. Thus, the tool chain to be developed implements an evaluation and modeling methodology to quickly and easily develop and translate computer vision applications to the reconfigurable FPGA-based Dream Chip Technologies DCT10A platform ("Computer Vision to DCT10A" framework - CV2DCT10A) -framework).