The paper is devoted to the recognition of objects and patterns deformed by imaging geometry as well as by unknown blurring. We introduce a new class of features invariant simultaneously to blurring with a centrosymmetric PSF and to aane transformation. As we prove in the paper, they can be constructed by combining aane moment invariants and blur invariants… (More)
A new method of normalization is used for the construction of the affine moment invariants. The affine transform is decomposed into translation, scaling, stretching, two rotations and mirror reflection. The object is successively normalized to these elementary transforms by means of low order moments. After normalization, other moments of normalized object… (More)
The paper is devoted to the moment invariants with respect to projective transform. It has been a common belief that such invariants do not exist. We show that projective moment invariants exist in a form of infinite series containing moments with positive as well as negative indices.
The article is devoted to the feature-based recognition of blurred images acquired by a linear shift-invariant imaging system against an image database. The proposed approach consists of describing images by features that are invariant with respect to blur and recognizing images in the feature space. The PSF identification and image restoration are not… (More)
A general method of systematic derivation of affine moment invariants of any weights and orders is introduced. Each invariant is expressed by its generating graph. Techniques for elimination of reducible invariants and dependent invariants are discussed. This approach is illustrated on the set of all affine moment invariants up to the weight ten.
A new type of affine moment invariants for color images is proposed in this paper. The traditional affine moment invariants can be computed on each color channel separately, yet when the channels are transformed together, by the same affine transform, additional invariants can be computed. They have low order and therefore high robustness to noise. The new… (More)
The paper is devoted to the feature-based recognition of degraded signals acquired by a linear time-invariant system. The proposed approach consists of describing signals by features which are invariant with respect to the degradation and recognizing signals in the feature space. Neither impulse response identification nor signal restoration are required.… (More)