Abstract
In the central visual pathway originating from the eye, a bridging is required between two hierarchical tasks, that of pixel based information recording by visual pathway at low level on one hand and that of object recognition at high level on the other. Such a bridge which may be designated as a mid-level block-grained integration has here been modeled by a multi-layer flexible cellular neural network (F-CNN). The proposed CNN architecture is validated by different intermediate level tasks involving rigid and deformable pattern recognition. Execution of such tasks by the proposed architecture, it has been shown, is capable of generating valid and significant inputs for the WHERE (dorsal) and WHAT (ventral) pathways in the brain. The model includes the proposal of a feedback (also by CNN architecture) to the lower mid-level from the higher mid-level dorsal and ventral pathways for flexible cell (physiological receptive field) size adjustment in the primary visual cortex towards successful ‘where’ and ‘what’ identifications for high-level vision.
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Das, A., Roy, A., Ghosh, K. (2011). Proposing a CNN Based Architecture of Mid-level Vision for Feeding the WHERE and WHAT Pathways in the Brain. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_66
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DOI: https://doi.org/10.1007/978-3-642-27172-4_66
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