Abstract
Previous research in model-based 2 – D object recognition has compared the scene representation either with each model in a database of models or with each of a collection of features belonging to a database of models until the scene was completely analyzed. In other words, these techniques were model-driven and lacked a data-driven indexing mechanism for model retrieval. In this paper, we develop such an indexing mechanism. This mechanism is part of an overall scheme called SMITH (Shape Matching Utilizing Indexed Hypothesis Generation and Testing) for 2-D object recognition which is based on a dynamic programming implementation of attributed string matching. It is computationally efficient and works effectively for both non-occluded and occluded shapes. Another advantage of our technique is that models may be inserted or deleted with relatively little cost.
This research has been partially supported by the Institute for Manufacturing Research at Wayne State University.
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Mehrotra, R., Grosky, W.I. (1988). Smith: An Efficient Model-Based two Dimensional Shape Matching Technique. In: Ferraté, G., Pavlidis, T., Sanfeliu, A., Bunke, H. (eds) Syntactic and Structural Pattern Recognition. NATO ASI Series, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83462-2_15
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DOI: https://doi.org/10.1007/978-3-642-83462-2_15
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