Embedded vision system with hardware acceleration

This paper presents an efficient architecture of low power embedded vision system based on dynamically pro-grammable logic acceleration. This approach is based on param-eterised architecture designed for a machine supervised learning classifier. We test and verified the model functionality by experiments on benchmark datasets including case study on object recognition. Evaluation shows that the proposed architecture gained speedups and high performance.