A Line-Context Based Object Recognition Method

Abstract

The shape or contour of an object is usually stable and persistent, so it is a good basis for invariant recognition. For this purpose, two problems must be handled. The first is obtaining clean edges and the other is organizing those edges into a structured form so that they can be manipulated easily. We apply a bio-inspired orientation detection algorithm because it can output a fairly clean set of lines, and all lines are in the form of vectors instead of pixels. This line representation is efficient. We decompose them into several slope-depended layers and then create a hierarchical partition tree to record their geometric distribution. Based on the similarity of trees, a rough classification of objects can be realized. However, for an accuracy recognition, we design a momentbased measure to describe the detail layout of lines in a layer and then re-describe image by Hu's moment invariants. The experimental results suggest that the representation efficiency enabled by simple cell's neural mechanism and application of multi-layered representation schema can simplify the complexity of the algorithm. This proves that line-context representation greatly eases subsequent shape-oriented recognition.