Narrow Results

Biometric information is widely used in user identification system. Because of the unique and invariant properties of the iris through a lifetime, iris recognition is one of the most stable and reliable means in biometric identification. Extracting distinguishable iris features for iris recognition is very important. In this paper, for capturing effective texture features that represent the complex directional structures of an iris image, a new iris recognition method using the nonsubsampled contourlet transform (NSCT) features is proposed. With the shift-invariance, multiscale, and multidirection properties, significant NSCT coefficient features along the radial and angular directions in an iris image can be represented efficiently. Iris segmentation and normalization are considered at first as pre-processing. The modified normalized iris image is obtained from the normalized iris regions for extracting the robust iris features, and then is filtered with the NSCT to obtain the distinct coefficient features in each directional subband. Next, using the NSCT coefficients in each subband, an iris code vector is constructed for iris matching. Comparison of experimental results of the proposed and existing methods with three databases show the effectiveness of the proposed NSCT feature-based iris recognition algorithm, in terms of the three performance measures.

A contrast enhancement method based on the adaptive noise threshold estimation and logarithmic function in nonsubsampled contourlet transform (NSCT) domain is proposed, which can improve the defects segmentation accuracy of the elevator compensation chain. After extracting region of interest (ROI) according to the spatial location of defects, it is transformed into NSCT domain, where the high-frequency subband coefficients corresponding to noise is suppressed by the adaptive noise threshold estimation. Then, the logarithmic function transformation is used to enhance the image edges to a variable extent. Finally, the enhanced image is segmented by watershed algorithm based on laws texture analysis. Experimental results demonstrate that the performance of the proposed method is superior to the existing methods in terms of both the quality of contrast enhancement and the segmentation accuracy.

In the field of medical diagnostics, interested parties have resorted increasingly to medical imaging. It is well established that the accuracy and completeness of diagnosis are initially connected with the image quality, but the quality of the image is itself dependent on a number of factors including primarily the processing that an image must undergo to enhance its quality. The quality evaluation of compressed image is necessary to judge the performance of a compression method. This paper introduces an algorithm for medical image compression based on hybrid nonsubsampled contourlet (NSCT) and quincunx wavelet transforms (QWT) coupled with set partitioning in hierarchical trees (SPIHT) coding algorithm, of which we present the objective measurements (PSNR, EDGE, WPSNR, MSSIM, VIF, and WSNR) in order to evaluate the quality of the image.