Shape Description and Recognition Method Inspired by the Primary Visual Cortex

The shape or contour of an object is usually stable and persistent, so it is a good basis for invariant recognition. Before this can be accomplished, two problems must be handled. The first is obtaining clean edges, and the other is organizing those edges into a structured form so they can be manipulated easily. Simple cells in the primary visual cortex are specialized for orientation detection. This neural mechanism can be stimulated using an array model. This model can produce a fairly clean set of lines, all in the form of vectors instead of pixels. These multiple orientation layers are then disintegrated from the original array, and a hierarchical partition tree can be created to re-organize them. Based on the similarity of the trees, a rough classification of objects can be realized. To enable maximum recognition, a moment-based measurement method was designed to describe the layout of active simple cells in each layer in detail. Then, a decision tree was produced using samples re-described using Hu's moment invariants. This system takes into account more geometric information during the recognition process. The experimental results suggest that the representation efficiency enabled by simple cells, and their neural mechanism and the application of a multi-layered representation schema can simplify the algorithm. This further demonstrates the crucial role of simple cells in the visual processing path and shows that they can facilitate subsequent shape processing.