Automatic object recognition methodologies are being used increasingly in the automation of many industrial processes. This paper presents a processing architecture that promotes the re-utilization of common system modules. The approach described covers the whole processing chain by connecting low-level pre-processing to the final classification stage, specially emphasizing the segmentation and feature extraction levels. The resulting core is a search engine based on the data structure of a graph. This structure is initially fed with raw edge information extracted from the original raster image representation. The edge segments used at this preliminary version of the graph are the result of a multistage process of edge following and edge linking. The use of image properties at high levels of abstraction allows the dimensions of the search space to be significantly reduced. Modularity and re-utilization allows the refinement of an initial low-level representation of information into successive higher levels of description, until a final set of features that may directly feed a classifier is achieved.
Feature extractor and search engine for automatic object recognition
1998
8 Seiten, 17 Quellen
Aufsatz (Konferenz)
Englisch
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