An Effective License-Plate Detection Method for Overexposure and Complex Vehicle Images

Tseng, Hsiao-Yun; Lai, Cheng-Chou; Yu, Shyr-Shen

doi:10.1109/ichit.2008.231

Cited by 4 publications

(1 citation statement)

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“…Because of the finite sensitivity of the vision sensor, it becomes saturated easily; thus, over-exposure of images is inevitable. In [ 30 ], a license-plate detection method has been proposed to facilitate the detection of license plates from over-exposed images. This method involves the following steps: converting a color image into a grayscale image, equalizing the image, detecting the edges, checking the black pixel ratio, verifying the license plate, and outputting the license plate.…”

Section: Introductionmentioning

confidence: 99%

Correction Method for Line Extraction in Vision Measurement

Shao

Wei

et al. 2015

PLoS ONE

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Over-exposure and perspective distortion are two of the main factors underlying inaccurate feature extraction. First, based on Steger’s method, we propose a method for correcting curvilinear structures (lines) extracted from over-exposed images. A new line model based on the Gaussian line profile is developed, and its description in the scale space is provided. The line position is analytically determined by the zero crossing of its first-order derivative, and the bias due to convolution with the normal Gaussian kernel function is eliminated on the basis of the related description. The model considers over-exposure features and is capable of detecting the line position in an over-exposed image. Simulations and experiments show that the proposed method is not significantly affected by the exposure level and is suitable for correcting lines extracted from an over-exposed image. In our experiments, the corrected result is found to be more precise than the uncorrected result by around 45.5%. Second, we analyze perspective distortion, which is inevitable during line extraction owing to the projective camera model. The perspective distortion can be rectified on the basis of the bias introduced as a function of related parameters. The properties of the proposed model and its application to vision measurement are discussed. In practice, the proposed model can be adopted to correct line extraction according to specific requirements by employing suitable parameters.

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Section: Introductionmentioning

confidence: 99%