On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Zernike moment-based image analysis and its application
Pattern Recognition Letters
Using moment invariants and HMM in facial expression recognition
Pattern Recognition Letters
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Moment computation for objects with spline curve boundary
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation, scale, and translation resilient watermarking for images
IEEE Transactions on Image Processing
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
Some computational aspects of discrete orthonormal moments
IEEE Transactions on Image Processing
A unified methodology for the efficient computation of discrete orthogonal image moments
Information Sciences: an International Journal
Using tchebichef moment for fast and efficient image compression
Pattern Recognition and Image Analysis
Rotation and translation invariants of Gaussian-Hermite moments
Pattern Recognition Letters
Image analysis by Gaussian-Hermite moments
Signal Processing
Image recognition by affine Tchebichef moment invariants
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Radial Tchebichef moment invariants for image recognition
Journal of Visual Communication and Image Representation
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Discrete orthogonal moments such as Tchebichef moments have been successfully used in the field of image analysis. However, the invariance property of these moments has not been studied mainly due to the complexity of the problem. Conventionally, the translation and scale invariant functions of Tchebichef moments can be obtained either by normalizing the image or by expressing them as a linear combination of the corresponding invariants of geometric moments. In this paper, we present a new approach that is directly based on Tchebichef polynomials to derive the translation and scale invariants of Tchebichef moments. Both derived invariants are unchanged under image translation and scale transformation. The performance of the proposed descriptors is evaluated using a set of binary characters. Examples of using the Tchebichef moments invariants as pattern features for pattern classification are also provided.