Adaptive license plate image extraction
CompSysTech '04 Proceedings of the 5th international conference on Computer systems and technologies
Expert Systems with Applications: An International Journal
License Plate Detection and Character Recognition
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Car plate localization using pulse coupled neural network in complicated environment
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Vision-based vehicle speed measurement method
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Car plate localization using modified PCNN in complicated environment
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
Robust license plate segmentation method based on texture features and radon transform
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A novel license plate location method based on neural network and saturation information
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A vehicle license plate detection method using region and edge based methods
Computers and Electrical Engineering
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This paper presents a morphology-based method for detecting license plates from cluttered images. The proposed system consists of three major components. At the first, a morphology-based method is proposed to extract important contrast features as guides to search the desired license plates. The contrast feature is robust to lighting changes and invariant to several transformations like scaling, translation, and skewing. Then, a recovery algorithm is applied for reconstructing a license plate if the plate is fragmented into several parts. The last step is to do license plate verification. The morphology-based method can significantly reduce the number of candidates extracted from the cluttered images and thusspeeds up the subsequent plate recognition. Under the experimental database, 128 examples got from 130 images were successfully detected. The average accuracy of license plate detection is 98%. Experimental results show that the proposed method improves the state-of-the-art work in terms of effectiveness and robustness of license plate detection.