Ling Ma

Work place: Fast Corporation 2791-5 Shimoturuma Yamoto, Kanagawa Japan



Research Interests: Image Processing, Computer Graphics and Visualization, Pattern Recognition


Ling Ma received his M.S. degree in computed mathematics from Hebei Normal University, P.R. China and Ph.D degrees in Department of Manufacturing Engineering from Beijing university of Aeronautics &Astronautics,. P.R. China, in 1987 and 1997, respectively. In 1998, he joined Tokyo Research Institute of Tani Electronic Company, Tokyo, Japan, as a researcher. Presently He is working as a Research Fellow in Fast Corporation in Japan. His research interests include image processing, pattern recognition, 3D machine vision and computer graphics. 

Author Articles
An Improved Method of Geometric Hashing Pattern Recognition

By Ling Ma Yumin Liu Huiqin Jiang Zhongyong Wang Haofei Zhou

DOI:, 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.

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A Novel Method to Improve the Visual Quality of X-ray CR Images

By Huiqin Jiang Zhongyong Wang Ling Ma Yumin Liu Ping Li

DOI:, 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.

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