Car Plate Localization Using Modified PCNN in Complicated Environment
Car plate Localization, which remains a difficult problem under complicated environment, is the key problem in many traffic related applications. In this paper we describe a new method based on modified Pulse Coupled Neural Network (PCNN) with adaptive threshold, which can capture relatively complete objects in human perception. After inverse filtering, PCNN processing is applied to produce a firing time sequence image. Then car plates’ position and rotated angle can be extracted from the firing image. Experiment results show that the correct car plate locating rate reaches 98%, which is higher than other Localization methods on the same image database.
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