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
Machine recognition of optically captured machine printed arabic text
Pattern Recognition
Pattern recognition with moment invariants: a comparative study and new results
Pattern Recognition
Object recognition based on moment (or algebraic) invariants
Geometric invariance in computer vision
Attitude estimation using moment invariants
Pattern Recognition Letters
Affine moment invariants: a new tool for character recognition
Pattern Recognition Letters
Noise and intensity invariant moments
Pattern Recognition Letters
Identification and inspection of 2-D objects using new moment-based shape descriptors
Pattern Recognition Letters
Generation of moment invariants and their uses for character recognition
Pattern Recognition Letters
An iterative solution for object pose parameters using image moments
Pattern Recognition Letters
Complete Sets of Complex Zernike Moment Invariants and the Role of the Pseudoinvariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Aircraft identification by moment invariants
IEEE Transactions on Computers
Template Matching in Rotated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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Moment invariants are properties of connected regions in binary images that are invariant to translation, rotation and scale. They are useful because they define a simply calculated set of region properties that can be used for shape classification and part recognition. Orthogonal moment invariants allow for accurate reconstruction of the described shape. Generic Fourier Descriptors yield spectral features and have better retrieval performance due to multi-resolution analysis in both radial and circular directions of the shape. In this paper we first compare various moment-based shape description techniques then we propose a method that, after a previous image partition into classes by morphological features, associates the appropriate technique with each class, i.e. the technique that better recognizes the images of that class. The results clearly demonstrate the effectiveness of this new method regard to described techniques.