Image Analysis by Tchebichef Moments

By R. Mukundan, S. Ong, P. Lee. IEEE Transactions on Image Processing, 2001

936 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a67dee08d90e7733060d25ed111402ea8c8e725b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chenglin Liu
2. Luc Van Gool
3. Dacheng Tao
4. Ming Yang
5. Andrew Zisserman
6. Xiaogang Wang
7. Bernt Schiele
8. Yu Qiao

Citations in the corpus

• Character Recognition Systems: A Guide for Students and Practitioners (2007, background): “introduced the Tchebichef moments where the basis function is the discrete orthogonal Tchebichef polynomial [84].”
• Frequency response functions of shape features from full-field vibration measurements using digital image correlation (2012, methodology): “Numerical errors due to discretisation of the Legendre/Zernike polynomials are inevitable during the digital evaluation of image moments, which may be overcome by adopting discrete Tchebichef, Krawtchouk or Hahn polynomials [36,55,56].”
• A Survey of Shape Feature Extraction Techniques (2008, background): “Zernike moments (ZM) have the following advantages [47]: ˆ rotation invariance: the magnitudes of Zernike moments are invariant to rotation.”