Motion Mapping Cognition: A Nondecomposable Primary Process in Human Vision
This work tackles the foundational problem of understanding human visual cognition for advancing machine vision, but it appears incremental as an extension of existing topological perception theory.
The paper introduces motion mapping cognition (MMC) as a nondecomposable primary process in human vision, arguing it explains most visual functions but cannot be modeled by traditional methods like object recognition, and proposes a quantized topological matching principle based on optimal transport theory to address it.
Human intelligence seems so mysterious that we have not successfully understood its foundation until now. Here, I want to present a basic cognitive process, motion mapping cognition (MMC), which should be a nondecomposable primary function in human vision. Wherein, I point out that, MMC process can be used to explain most of human visual functions in fundamental, but can not be effectively modelled by traditional visual processing ways including image segmentation, object recognition, object tracking etc. Furthermore, I state that MMC may be looked as an extension of Chen's theory of topological perception on human vision, and seems to be unsolvable using existing intelligent algorithm skills. Finally, along with the requirements of MMC problem, an interesting computational model, quantized topological matching principle can be derived by developing the idea of optimal transport theory. Above results may give us huge inspiration to develop more robust and interpretable machine vision models.