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Feature extraction for license plate location based on L0-norm smoothing

Feature extraction for license plate location based on L0-norm smoothing AbstractWe propose a simple feature extraction algorithm for license plate location, which can reduce the occurrence of pseudo-licenses significantly. Our scheme arises from a novel L0-norm image smoothing, in which the multiple local textures in the complex backgrounds can be suppressed remarkably without changing the structures and edges of the license objects. Due to this “edgeaware” property, we then combine a feature filtering with an efficient binarized image, a simple multi-scale image analysis algorithm, to remove the potential false license plates. Finally, we extract license plates with a projection method. Experimental results show the proposed method provides a flexible and powerful way to the license plate location in complex backgrounds. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Open Computer Science de Gruyter

Feature extraction for license plate location based on L0-norm smoothing

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Publisher
de Gruyter
Copyright
© 2019 Junqing Huang et al., published by De Gruyter Open
eISSN
2299-1093
DOI
10.1515/comp-2019-0007
Publisher site
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Abstract

AbstractWe propose a simple feature extraction algorithm for license plate location, which can reduce the occurrence of pseudo-licenses significantly. Our scheme arises from a novel L0-norm image smoothing, in which the multiple local textures in the complex backgrounds can be suppressed remarkably without changing the structures and edges of the license objects. Due to this “edgeaware” property, we then combine a feature filtering with an efficient binarized image, a simple multi-scale image analysis algorithm, to remove the potential false license plates. Finally, we extract license plates with a projection method. Experimental results show the proposed method provides a flexible and powerful way to the license plate location in complex backgrounds.

Journal

Open Computer Science – de Gruyter

Published: Jan 1, 2019

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