The theory of moments and transforms is well established and widely applied to a number of
computer vision, pattern recognition and image processing applications. A sub-class of …

Orthogonal moments are the projections of image functions onto particular kernel functions.
They play vital role in digital image feature extraction being rotation, scaling, translation …

The geometrically invariant image descriptors are very important for recognizing objects of
arbitrary shapes and orientations. We propose a framework for the fusion of the …

Zernike moments (ZMs) are very effective orthogonal rotation invariant moments.
Conventionally, the magnitudes of ZMs are used as feature descriptors and the Euclidean …

Orthogonal moments (OM) are extensively used for image enhancement, representation and
comparison. They are basically derived through mutually perpendicular polynomials which …

Moments can be viewed as powerful image descriptors that capture global characteristics of
an image. The magnitude of the moment coefficients is said to be invariant under …

Rotationally invariant transforms, namely, angular radial transform and polar harmonic
transforms such as polar cosine transform, polar sine transform and polar complex …