This paper proposes a faster mechanism for license plate detection. Usually license plate has more vertical edges compared to horizontal edge. By focusing on this concept an algorithm called VEDA is used here. First convert the color image into gray image. Then for binarizing the image a technique called adaptive thresholding (AT) is used. The noise in the image is removed by using ULEA algorithm. Then the proposed VEDA is used to extract the vertical edges. Then HDD algorithm is used for highlighting the license plate area. Compared to other edge detection mechanisms such as SOBEL and CANNY, VEDA has lesser complexity. Also the computation time is also less for VEDA when compared against SOBEL. Index Terms-License plate detection (LPD), adaptive thresholding (AT), unwanted lines elimination algorithm (ULEA), vertical edge detection algorithm (VEDA), highlighting desired details (HDD), candidate region extraction (CRE), plate region selection (PRS).

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Medical imaging is the process of creating images of the human body for the purpose of diagnosis and treatment. In medical imaging various modalities provide different features of body because they use different principles of imaging. Since no single image modality provides a complete information in all cases, fusion will be very useful. The objective of image fusion is to improve the image content, by combining information from multiple images. In this paper three transform based fusion rules, The Wavelet transform(WT), Contourlet transform(CT) and Nonsubsampled contourlet transform(NSCT) are compared. Experimental results show that the image fusion method based on NSCT performs better than the other two methods.Index Terms-Image fusion, medical imaging, magnetic resonance imaging (MRI), computed tomography(CT).

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