“…The Zernike polynomials are orthogonal. Therefore it can draw out the Zernike moments from a ROI irrespective of the shape of the target [4]. The formulation of Zernike moment appears to be very favored, outperforming the options (in phrase of noise resilience, information redundancy and reconstruction capability).…”
Section: A Orthogonal Rotation Invariant Moment (Orim)mentioning
confidence: 99%
“…Sergio Dominguez [3] proposed a technique for the recognition of 3-D object and pose analysis. Chandan et al [4] proposed a technique for the error computation, Sheng et al proposed OFMMs [5], [8] for pattern recognition. The amounts of OMs are invariant to the variation of the signal [6].…”
Orthogonal moments (OMs) are amongst the superlative region centered shape descriptors. These OMs retain lowest facts redundancy. Zernike Moments (ZM) and pseudo Zernike Moments (PZM) are tested with respect to rotation invariance, and scale invariance for skin lesion images. Image reconstruction is executed for various orders of two different orthogonal moments; ZM and PZM. Reconstruction errors are also computed. This paper examines the impact of these errors on the features of OMs and executes a relative study of these errors on the precise calculation of the two major OMs: ZMs and PZMs.
“…The Zernike polynomials are orthogonal. Therefore it can draw out the Zernike moments from a ROI irrespective of the shape of the target [4]. The formulation of Zernike moment appears to be very favored, outperforming the options (in phrase of noise resilience, information redundancy and reconstruction capability).…”
Section: A Orthogonal Rotation Invariant Moment (Orim)mentioning
confidence: 99%
“…Sergio Dominguez [3] proposed a technique for the recognition of 3-D object and pose analysis. Chandan et al [4] proposed a technique for the error computation, Sheng et al proposed OFMMs [5], [8] for pattern recognition. The amounts of OMs are invariant to the variation of the signal [6].…”
Orthogonal moments (OMs) are amongst the superlative region centered shape descriptors. These OMs retain lowest facts redundancy. Zernike Moments (ZM) and pseudo Zernike Moments (PZM) are tested with respect to rotation invariance, and scale invariance for skin lesion images. Image reconstruction is executed for various orders of two different orthogonal moments; ZM and PZM. Reconstruction errors are also computed. This paper examines the impact of these errors on the features of OMs and executes a relative study of these errors on the precise calculation of the two major OMs: ZMs and PZMs.
“…For further details one can refer Refs. [20,24]. Despite these errors, the magnitude of RIMTs provides satisfactory results for many practical applications.…”
Section: Similarity Weight Computation Using Rimt-unlmmentioning
confidence: 99%
“…The difference in CPU elapse time is more apparent for large images. Although in our experiments we adopt the fast computation of ZMs [22][23][24][25], ART has speed advantage over ZMs due to its low computation complexity, thus providing minimum CPU elapse time for all images. The proposed ZM-UNLM-based approach with order of moment p max = 4 uses only nine moments which are sufficient to represent the useful characteristics of an image, whereas the existing DCT-UNLM approach uses 15 DCT coefficients extracted in a zig-zag manner.…”
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