Work place: School of Information Engineering and Digital Medical Image Technique Research Center Zhengzhou University, Zhengzhou, China
Research Interests: Signal Processing, Wireless Communication, Information Systems
Zhongyong Wang received his B. S. and M. S., degrees in Automatic Control from Harbin Shipbuilding Engineering Institute, Harbin, China, in 1986, 1988, respectively, and received his Ph. D. degree in Automatic Control Theory and Application from Xi'an Jiaotong University, Xi'an, China, in 1998. Since 1988, Zhongyong Wang has been with Zhengzhou University, Zhengzhou, China, as a Lecturer in the Department of Electronics. From 1999 to 2002, he was an Associate Professor, and in 2002 he was promoted to Professor in the School of Information Engineering. He is currently the Dean of the School of Information Engineering and a member of Digital Imaging Technology Research Center, Zhengzhou University. Prof. Wang’s general fields of interest cover numerous aspects within embedded systems, signal processing and communication theory.
DOI: https://doi.org/10.5815/ijmecs.2011.03.01, Pub. Date: 8 Jun. 2011
Geometric hashing (GH) is a general model-based recognition scheme. GH is widely used in the industrial products assembly and inspection tasks. The aim of this study is to speed up the geometric hashing pattern recognition method for the purpose of real-time object detection applications. In our method, a pattern is decomposed into some sub-patterns to reduce the data number in hash table bins. In addition, the sub-patterns are recorded in a plurality of hash tables. Finally we improve the recognition performance by combining with image pyramid and edge direction information. To confirm the validity of our proposed method, we make a complexity analysis, and apply our method to some images. Both complexity analysis and experiment evaluations have demonstrated the efficiency of this technique.[...] Read more.
DOI: https://doi.org/10.5815/ijigsp.2011.04.04, Pub. Date: 8 Jun. 2011
The aim of this study is to improve the visual quality of x-ray CR images displayed at general displays. Firstly, we investigate a series of wavelet-based denoising methods for removing quantum noise remains in the original images. The denoised image is obtained by using the scheme of wavelet thresholding, where the best suitable wavelet and level are chosen based on theory analysis. Secondly, the image contrast is enhanced using Gamma correction. Thirdly, we improve unsharp masking method for enhancing some useful information and restraining other information selectively. Fourthly, we fuse the denoised image with the enhanced image. Fively, the used display is calibrated, so that it could offer full compliance with the Grayscale Standard Display Function (GSDF) defined in Digital Imaging and Communications in Medicine (DICOM) Part 14. Finally, we decide parameters of the image fusion, resulting in the diagnosis image. A number of experiments are performed over some x-ray CR images by using the proposed method. Experimental results show that this method can effectively reduce the quantum noise while enhancing the subtle details; the visual quality of X-ray CR images is highly improved.[...] Read more.
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