Recognition of partially occluded objects through fuzzy invariant indexing

We present an approach to the recognition of partially occluded objects employing fuzzy invariant values and fuzzy if-then-rules, called fuzzy invariant indexing (FII). Compared with traditional invariant indexing, the fuzzy method proposed here offers the following advantages: firstly, as shown in the experimental results of the paper the recognition quality may be considerably increased in the case of similar objects; secondly, the ability is provided to control the recognition process during the hypothesis evaluation stage, and thirdly, a FII-based recognition system can be simply extended in a closed form, i.e. new attributes may be added to the fuzzy classification rules resulting in only minor changes to the original structure of the system. We demonstrate the recognition performance of the new FII-technique for partially occluded (quasi-)planar objects in real image scenes taken from different camera viewpoints and conclude the paper with a discussion of the potential of the method and directions of possible future research.