Approaches to visual object recognition can be divided into model-based and non modeLbased schemes. In this paper we establish some limitations on non model-based recognition schemes. We show that a consistent non model-based recognition scheme for general objects cannot discriminate between objects. The same result holds even if the recognition function is imperfect, and is allowed to mis-identify each object from a substantial fraction of the viewing directions. We then consider recognition schemes restricted to classes of objects. We define the notion of the discrimination power of a consistent recognition function for a class of objects. The function’s discrimination power determines the set of objects that can be discriminated by the recognition function. We show how the properties of a class of objects determine an upper bound on the discrimination power of any consistent recognition function for that class.
CITATION STYLE
Moses, Y., & Ullman, S. (1992). Limitations of non model-based recognition schemes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 588 LNCS, pp. 820–828). Springer Verlag. https://doi.org/10.1007/3-540-55426-2_94
Mendeley helps you to discover research relevant for your work.