Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
 
Articles by Guo He
Total Records ( 2 ) for Guo He
  Guo He , Wang Yu-Xin , Feng Zhen , Yu Yu-Long , Jia Qi , Wang Yuan-Yuan , Liu Yao , Zhang Li-Jie and Hou Yi-Ting
  Image segmentation is an important issue in the field of computer vision, it serves as a bridge linking the basic image processing methods to the high-level semantic recognition methods. With the increasing applications of the image segmentation methods in the modern industries, such as the defect detection in the production lines, the real-time requirements are greatly raised. Recently, with the advent of the General-purpose Graphics Processing Unit (GPGPU) platform, the parallelized implementations on this new platform open a new way to accelerate the image segmentation methods to meet the real-time requirements. In this work, various methods are analyzed and parallelized on the GPGPU platform, the horizontal comparisons are made to evaluate the potentials of parallelization for different segmentation methods. The parallelization strategies are performed on two levels: on the algorithm development level and on the program development level. It is expected that this investigation may provide guidance to the future parallelization tasks for the more advanced image segmentation methods and other computer vision applications.
  Feng Zhen , Guo He , Wang Yu-Xin , Xu Wen-Long , Jiang Ming-Feng and Liu Feng
  The choice of sparsity bases plays a crucial role to reconstruct high-quality MR images from heavily under-sampled k-space signals. Traditionally, the Wavelet transform and the Total Variation (TV) are used as the sparsity bases. In this study, a novel sparsity basis, based on a two-dimensional Walsh transform, is proposed to sparsify the MR image. The basic theory of the Walsh transform-based CS-MRI is explained and the proposed technique is validated with experiments. Three different types of MR images are used to test the proposed method performance in terms of reconstruction accuracy. The results show that the proposed Walsh transform-based sparsity basis is capable of reconstructing MRI images with a higher fidelity than the traditional Wavelet transform-based sparsity basis using a similar running time.
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility