Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work  present an iterative multi-scale algorithm for motion estimation that follows mechanisms of motion processing in the human brain. Keeping the properties of a previously presented neural model of cortical motion integration  created a computationally fast algorithmic implementation of the model. The novel contribution is the extension of the algorithm to operate on multiple scales without the disadvantages of typical coarse-to-fine approaches. Compared to the implementation with one scale our multi-scale approach generates faster dense flow fields and reduces wrong motion estimations. In contrast to other approaches, motion estimation on the fine scale is biased by the coarser scales without being corrupted if erroneous motion cues are generated on coarser scales, e.g., when small objects are overlooked. This multi-scale approach is also consistent with biological observations: The function of fast feedforward projections to higher cortical areas with large receptive fields and feedback connections to earlier areas as suggested by our approach might contribute to human motion estimation.
['Cornelia Beck', 'Pierre Bayerl', 'Heiko Neumann']
I am writing a research paper on a idea Optic Flow Integration at Multiple Spatial Frequencies : Neural Mechanism and Algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Optic Flow Integration at Multiple Spatial Frequencies : Neural Mechanism and Algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work  present an iterative multi-scale algorithm for motion estimation that follows mechanisms of motion processing in the human brain. Keeping the properties of a previously presented neural model of cortical motion integration  created a computationally fast algorithmic implementation of the model. The novel contribution is the extension of the algorithm to operate on multiple scales without the disadvantages of typical coarse-to-fine approaches. Compared to the implementation with one scale our multi-scale approach generates faster dense flow fields and reduces wrong motion estimations. In contrast to other approaches, motion estimation on the fine scale is biased by the coarser scales without being corrupted if erroneous motion cues are generated on coarser scales, e.g., when small objects are overlooked. This multi-scale approach is also consistent with biological observations: The function of fast feedforward projections to higher cortical areas with large receptive fields and feedback connections to earlier areas as suggested by our approach might contribute to human motion estimation."}]