Kejia Wang

Beijing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Kejia Wang is a researcher whose work lies at the intersection of computer vision and pattern recognition, with a particular focus on invariant image descriptors and their practical applications. Wang’s most notable contribution is the development and application of Radial Harmonic Fourier Moments (RHFMs), a powerful set of image descriptors that are robust to translation, rotation, scaling, and intensity variations. These moments offer excellent image description ability, strong noise resistance, and low computational complexity, making them highly effective for challenging recognition tasks. In their 2011 paper on Chinese Chess character recognition, Wang demonstrated the practical utility of RHFMs by successfully identifying rotated characters, a problem that confounds many traditional methods. While this foundational work has accrued 9 citations, it represents a significant step in advancing rotation-invariant pattern recognition. Wang’s research continues to influence fields where robust feature extraction is critical, including document analysis and automated inspection systems.