Stream Processing of Scientific Big Data on Heterogeneous Platforms -- Image Analytics on Big Data in Motion

High performance image analytics is an important challenge for big data processing as image and video data is a huge portion of big data e.g. generated by a tremendous amount of image sensors worldwide. This paper presents a case study for image analytics namely the parallel connected component labeling (CCL) which is one of the first steps of image analytics in general. It is shown that a high performance CCL implementation can be obtained on a heterogeneous platform if parts of the algorithm are processed on a fine grain parallel field programmable gate array (FPGA) and a multi-core processor simultaneously. The proposed highly efficient architecture and implementation is suitable for the processing of big image and video data in motion and reduces the amount of memory required by the hardware architecture significantly for typical image sizes.