Color Fourier–Mellin descriptors for image recognition

Mennesson, José; Saint-Jean, Christophe; Mascarilla, Laurent

doi:10.1016/j.patrec.2013.12.014

Cited by 38 publications

(15 citation statements)

References 31 publications

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“…This may result in degradation in compact representation of color images. In the recent years, quaternion algebra is utilized efficiently for color images [35][36][37][38]. Quaternion combines a color of three tuple (RGB or HSV) into a single hypercomplex number.…”

Section: Resultsmentioning

confidence: 99%

Combined Rotation- and Scale-Invariant Texture Analysis Using Radon-Based Polar Complex Exponential Transform

Singh

Urooj

2015

Arab J Sci Eng

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Polar complex exponential transform (PCET) is superior to pseudo Zernike moment-based method in terms of kernel generation, numerical stability and easier implementation. Their performance degrades under additive noise such as white Gaussian noise. Moreover, these methods show poor performance against directional information of texture. In this paper, a new rotation-and scale-invariant method for texture analysis using Radon transform and PCET for textured image is proposed. Scale and translation invariance is achieved by normalization process in Radon space, and rotation invariance is obtained by combining Radon transform with PCET. A k-nearest neighbor classifier is employed to classify the texture. To test and evaluate the proposed method, several sets of textures were experimented with different scaling, translation and rotation in different noisy conditions. The correct classification percentage is calculated under the varying standard deviation. Experimental results show preeminence of the proposed method as compared to the existing invariant texture analysis methods.

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Section: Resultsmentioning

confidence: 99%

Combined Rotation- and Scale-Invariant Texture Analysis Using Radon-Based Polar Complex Exponential Transform

Singh

Urooj

2015

Arab J Sci Eng

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“…is invariant to rotation, which is the conventional magnitude-based method adopted in [12], [13] and [30] for achieving the rotation invariance. However, such a process loses the phase information which may be useful in some applications [48,49].…”

Section: Rotation Invariantsmentioning

confidence: 99%

“…0.9610i+0.1080j+0.2545k) and µ 100 are two relatively optimal choices for QFMMIs; µ 8 In object and scenes recognition, phase information plays a role more important than magnitude information [47,48]. As a consequence, to achieve rotation invariant, we decided to use the method based on Theorem 1 instead of the conventional magnitude-based method by directly considering the modulus of QTMs [12,13,30]. In order to evaluate our method and compare with a more conventional strategy, we have constructed new set of QTMIs based on the magnitude-based method.…”

Section: Color Face Recognitionmentioning

confidence: 99%

Color Image Analysis by Quaternion-Type Moments

et al. 2014

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In this paper, by using the quaternion algebra, the conventional complex-type moments (CTMs) for gray-scale images are generalized to color images as quaternion-type moments (QTMs) in a holistic manner. We first provide a general formula of QTMs from which we derive a set of quaternion-valued QTM invariants (QTMIs) to image rotation, scale and translation transformations by eliminating the influence of transformation parameters. An efficient computation algorithm is also proposed so as to reduce computational complexity. The performance of the proposed QTMs and QTMIs are evaluated considering several application frameworks ranging from color image reconstruction, face recognition to image registration.We show they achieve better performance than CTMs and CTM invariants (CTMIs). We also discuss the choice of the unit pure quaternion influence with the help of experiments. ( ) / 3 ! ! i j k appears to be an optimal choice.

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“…There have been many attempts to apply the Mellin transform for the extraction of invariant features (see, for example, [2,4,6,11]). However, up to now, this transform has only been used to extract invariants of a similarity transform.…”

Section: Introductionmentioning

confidence: 99%

The Mellin Central Projection Transform

Yang

Zhang

2017

ANZIAM J.

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The central projection transform can be employed to extract invariant features by combining contour-based and region-based methods. However, the central projection transform only considers the accumulation of the pixels along the radial direction. Consequently, information along the radial direction is inevitably lost. In this paper, we propose the Mellin central projection transform to extract affine invariant features. The radial factor introduced by the Mellin transform, makes up for the loss of information along the radial direction by the central projection transform. The Mellin central projection transform can convert any object into a closed curve as a central projection transform, so the central projection transform is only a special case of the Mellin central projection transform. We prove that closed curves extracted from the original image and the affine transformed image by the Mellin central projection transform satisfy the same affine transform relationship. A method is provided for the extraction of affine invariants by employing the area of closed curves derived by the Mellin central projection transform. Experiments have been conducted on some printed Chinese characters and the results establish the invariance and robustness of the extracted features.

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Color Fourier–Mellin descriptors for image recognition

Cited by 38 publications

References 31 publications

Combined Rotation- and Scale-Invariant Texture Analysis Using Radon-Based Polar Complex Exponential Transform

Combined Rotation- and Scale-Invariant Texture Analysis Using Radon-Based Polar Complex Exponential Transform

Color Image Analysis by Quaternion-Type Moments

The Mellin Central Projection Transform

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