Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new adaptive k-means clustering algorithm for real-time video imaging is presented. In the proposed solution, a ighted contribution of both pixel intensity and distance beten the pixels is used for cluster identification. The ight adaptation of each parameter reduces the computation complexity and makes it possible to implement the algorithm in hardware. The algorithm is designed for real-time video imaging in a VLSI implementation. It was implemented with 15 kgates and maximum clock rate of 80 MHz. Simulation results prove that a QCIF image could be handled in 15 f/s.
['Boris Maliatski', 'Orly Yadid-Pecht']
I am writing a research paper on a idea Hardware-driven adaptive k-means clustering for real-time video imaging
[{"from": "human", "value": "I am writing a research paper on a idea Hardware-driven adaptive k-means clustering for real-time video imaging"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new adaptive k-means clustering algorithm for real-time video imaging is presented. In the proposed solution, a ighted contribution of both pixel intensity and distance beten the pixels is used for cluster identification. The ight adaptation of each parameter reduces the computation complexity and makes it possible to implement the algorithm in hardware. The algorithm is designed for real-time video imaging in a VLSI implementation. It was implemented with 15 kgates and maximum clock rate of 80 MHz. Simulation results prove that a QCIF image could be handled in 15 f/s."}]