The aim of this paper is to describe an adaptive and predictive FPGA embedded architecture for vision systems dedicated to image analysis. A large panel of image analysis algorithms with some common characteristics must be mapped onto this architecture. Major characteristics of such algorithms are extracted to define the architecture. This architecture must easily adapt its structure to algorithm modifications. According to required modifications, few parts must be either changed or adapted. An NoC approach is used to break the hardware resources down as stand-alone blocks and to improve predictability and reuse aspects. Moreover, this architecture is designed using a globally asynchronous locally synchronous approach so that each local part can be optimized separately to run at its best frequency. Timing and resource prediction models are presented. With these models, the designer defines and evaluates the appropriate structure before the implementation process. The implementation of a particle image velocimetry algorithm illustrates this adaptation. Experimental results and predicted results are close enough to validate our prediction models for PIV algorithms.