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This paper presents an FPGA-based machine vision implementation for flow detection on Lab-on-Chip (LoC) experiments. The proposed machine vision system is designed to provide real-time information to the LoC user about the state of the flows (flow coordinates and points of interest) as well as input to the LoC controller. It is uniquely designed to compensate noise in the input video originating from non ideal lighting conditions or LoC movement. This machine vision implementation achieves real time response for input videos of 1Mpixel resolution and frame-rates exceeding 60fps for microfluidic flows with a maximum speed of 20mm/sec.