Robust fast model-based recognition of partially occluded objects using normalized interval vertex descriptors
Ramón Parra-Loera, Hector Erives, Wiley E. Thompson · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
This paper deals with the problem of robust fast recognition of partially occluded or incomplete views of `flat' objects. Robustness is accomplished through hypothesis confirmation using complementary or supporting information available for the current hypothesis and by model-based hypothesis verification. Classification speed is obtained by pruning the hypothesis hierarchy using simple pruning procedures based on structural properties derived from current object representation. In addition, classification speed is also improved through the use of simple model-based decision making procedures instead of computationally expensive transformations. Normalized Interval Vertex Descriptors (NIVD) are used to represent objects. NIVDs are representations derived from the physical characteristics of an object (vertices and sides) that are easy to obtain, especially for polygon like shapes. They provide not only a compact representation, but they also allow the definition of features that can be used to speed up the classification process. Experimental results of this process are also included.