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We present an object recognition system built on a combination of feature-and correspondence-based pattern recognizers. The featurebased part, called preselection network, is a single-layer feedforward network weighted with the amount of information contributed by each feature to the decision at hand. For processing arbitrary objects, we employ small, regular graphs whose nodes are attributed with Gabor amplitudes, termed parquet graphs. The preselection network can quickly rule out mostdoi:10.1162/neco.2009.12-07-675 pmid:19292649 fatcat:ldqzd7bqgjczvizxxxucmhilz4