Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a high-speed real-time plane fitting implementation on a field-programmable gate array (FPGA) platform. A novel hardware-based least squares algorithm fits planes to patches of points within a depth image captured using a Microsoft Kinect v2 sensor. The validity of a plane fit and the plane parameters are reported for each patch of 11 by 11 depth pixels. The high level of parallelism of operations in the algorithm has allod for a fast, low-latency hardware implementation on an FPGA that is capable of processing depth data at a rate of 480 frames per second. A hybrid hardware–software end-to-end system integrates the hardware solution with the Kinect v2 sensor via a computer and PCI express communication link to a Terasic TR4 FPGA development board.  have also implemented two proof-of-concept object detection applications as future candidates for bionic vision systems.  show that our complete end-to-end system is capable of running at 60 frames per second. An analysis and characterisation of the Kinect v2 sensor errors has been performed in order to specify logic precision requirements, statistical testing of the validity of a plane fit, and achievable plane fitting angle resolution.
['Horace Josh', 'Lindsay Kleeman']
I am writing a research paper on a idea A novel hardware plane fitting implementation and applications for bionic vision
[{"from": "human", "value": "I am writing a research paper on a idea A novel hardware plane fitting implementation and applications for bionic vision"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a high-speed real-time plane fitting implementation on a field-programmable gate array (FPGA) platform. A novel hardware-based least squares algorithm fits planes to patches of points within a depth image captured using a Microsoft Kinect v2 sensor. The validity of a plane fit and the plane parameters are reported for each patch of 11 by 11 depth pixels. The high level of parallelism of operations in the algorithm has allod for a fast, low-latency hardware implementation on an FPGA that is capable of processing depth data at a rate of 480 frames per second. A hybrid hardware\u2013software end-to-end system integrates the hardware solution with the Kinect v2 sensor via a computer and PCI express communication link to a Terasic TR4 FPGA development board.  have also implemented two proof-of-concept object detection applications as future candidates for bionic vision systems.  show that our complete end-to-end system is capable of running at 60 frames per second. An analysis and characterisation of the Kinect v2 sensor errors has been performed in order to specify logic precision requirements, statistical testing of the validity of a plane fit, and achievable plane fitting angle resolution."}]