This paper presents an effective location algorithm employing color features and black-white texture analysis of the
image to extract a vehicle license plate from a complicated background image. According to the background color of the
license plate in RGB spaces, we transform the RGB image into a grayscale image as strengthening the color of the
license plate, and partition the intensity image to obtain a binary image which can outstand the license plate part. We
leach away the color which is similar to the license plate by analyzing the texture characteristic. The test shows that this
location method can hardly be influenced by all of the factors including illumination, license plate position, license plate
size, license plate angle, car position, image background and so on. Meanwhile, it can gain a high speed, better effects
and a wide application area.
A novel SAR image denoising scheme based on hidden Markov tree (HMT) in the quad-tree complex wavelet packet
transform (QCWPT) domain was presented to achieve the tradeoff between details retainment and noise removal. A
neighborhood coefficient differential window was used to compute intra-scale correlations of complex wavelet
coefficients in high frequency detail subimage, and intra-scale correlational state was identified according to the
smallest error rate Bayesian decision-making rules. A HMT was fitted to describe the correlations between the
complex wavelet coefficients across decomposition scales and mark inter-scale correlational state. The product
results of corresponding positional intra-scale and inter-scale correlational state were looked as a new hidden state
transition probability. A set of iterative equations was developed using the expectation-maximization(EM) algorithm
to estimate the model parameters and produce denoising images. Experimental results show that the proposed
denoising algorithm is superior to the traditional filtering methods and possible to achieve an excellent balance
between suppress speckle noise effectively and preserve as many image details and edges as possible.
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