Image recognition by combined affine and blur Tchebichef moment invariants

Li, Qian; Zhu, Hongqing; Liu, Qian

doi:10.1109/cisp.2011.6100441

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…For this reason, various OG moments have been implemented in moment invariants, where they replace traditional geometric moments (see [1], Chapter 7, for a survey of OG moments). In the context of blur invariants (but not to Gaussian blur), this approach was applied for instance in [35,36,37,38,39,40,41]. 6 We tested the use of Legendre moments in the proposed Gaussian blur invariants.…”

Section: Recognition Of Blurred Facesmentioning

confidence: 99%

Handling Gaussian blur without deconvolution

Kostková

Flusser

Lébl

et al. 2020

Pattern Recognition

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Section: Recognition Of Blurred Facesmentioning

confidence: 99%

Handling Gaussian blur without deconvolution

Kostková

Flusser

Lébl

et al. 2020

Pattern Recognition

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“…Legendre moments [52], [53], [54], [55], Zernike moments [56], [57], [58], and Chebyshev moments [59] were employed for this purpose. Zuo [60] even combined moment blur invariants and SIFT features [61] into a single vector with weighted components but without a convincing improvement.…”

Section: Current State Of the Artmentioning

confidence: 99%

Projection Operators and Moment Invariants to Image Blurring

Flusser

Suk

Boldyš

et al. 2015

IEEE Trans. Pattern Anal. Mach. Intell.

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In this paper we introduce a new theory of blur invariants. Blur invariants are image features which preserve their values if the image is convolved by a point-spread function (PSF) of a certain class. We present the invariants to convolution with an arbitrary N-fold symmetric PSF, both in Fourier and image domain. We introduce a notion of a primordial image as a canonical form of all blur-equivalent images. It is defined in spectral domain by means of projection operators. We prove that the moments of the primordial image are invariant to blur and we derive recursive formulae for their direct computation without actually constructing the primordial image. We further prove they form a complete set of invariants and show how to extent their invariance also to translation, rotation and scaling. We illustrate by simulated and real-data experiments their invariance and recognition power. Potential applications of this method are wherever one wants to recognize objects on blurred images.

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“…Other researchers developed blur invariants that are based on orthogonal moments instead of geometric moments. Legendre moments [18][19][20][21], Zernike moments [22][23][24], and Chebyshev moments [25] are some of the examples. Some other authors proposed blur invariants in the Fourier domain.…”

Section: Introductionmentioning

confidence: 99%

Augmented SRC for face recognition under quality distortions

Mounsef

Karam

2019

IET biom.

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In the last two decades, numerous methods have been developed to offer a formulation to the face recognition problem under scene-dependent conditions. However, these methods have not considered image quality degradations resulting from capture, processing, and transmission such as blur and occlusion due to packet loss, under the same scene variations. Although deep neural networks are achieving state-of-the-art results on face recognition, the existing networks are susceptible to quality distortions. In this work, the authors propose an augmented sparse representation classifier (SRC) framework to improve the performance of the conventional SRC in the presence of Gaussian blur, camera shake blur, and block occlusions, while preserving its robustness to scene-dependent variations. In their evaluation of the SRC framework, they present a feature sparsity concentration and classification index that is capable of assessing the quality of features in terms of recognition accuracy as well as class-based sparsity concentration. For this purpose, they consider three main types of features including image raw pixels, histogram of oriented gradients and deep learning visual geometry group (VGG) Face. The obtained performance results show that the proposed method outperforms state-of-the-art sparse-based and blur-invariant methods.

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Image recognition by combined affine and blur Tchebichef moment invariants

Cited by 10 publications

References 12 publications

Handling Gaussian blur without deconvolution

Handling Gaussian blur without deconvolution

Projection Operators and Moment Invariants to Image Blurring

Augmented SRC for face recognition under quality distortions

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