Haiying

Papers

Total Citations

H-Index

About

Haiying’s research centers on image processing, pattern recognition, and invariant feature extraction, with a particular focus on applying advanced mathematical moments to real-world recognition tasks. Their most notable contribution is the development and application of Radial Harmonic Fourier Moments (RHFMs), a powerful technique that offers invariance to translation, rotation, scaling, and intensity changes while maintaining strong noise resistance and low computational complexity. In their highly cited 2011 paper, Haiying demonstrated the practical utility of RHFMs by successfully applying them to rotated Chinese Chess character recognition—a challenging problem due to the variety of orientations and visual distortions. This work, which has garnered 9 citations, showcases Haiying’s ability to bridge theoretical innovation with applied computer vision. By advancing the capabilities of moment-based descriptors, Haiying has contributed to more robust and efficient recognition systems, making their research valuable for fields ranging from document analysis to automated game playing. Their work continues to inspire researchers seeking computationally efficient yet highly discriminative feature extraction methods.