3D Image Representation Using Separable Discrete Orthogonal Moments

Batioua, Imad; Benouini, Rachid; Zenkouar, Khalid; Arivazhagan, S.; Fadili, Hakim El; Qjidaa, Hassan

doi:10.1016/j.procs.2019.01.047

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…For Cartesian moments, such extension is quite straightforward. Based on a 1D orthogonality polynomials set, we can generate d-dimensional orthogonal basis functions by using the same polynomials set for the each directions/variables [145].…”

Section: Definition Extensionmentioning

confidence: 99%

“…High-dimensional extension of image moments appeared very early [148] and attracted significant attention in the last decade [145,158,[185][186][187][188][189]. A possible explanation for its popularity might be the rapid development of devices and technologies related to 3D images such as medical imaging [183] and computer graphics [184].…”

Section: Definition Extensionmentioning

confidence: 99%

“…A possible explanation for its popularity might be the rapid development of devices and technologies related to 3D images such as medical imaging [183] and computer graphics [184]. Recent work in this field mainly focuses on the new definitions [145,158], invariants [188,189], and accurate/fast calculations [185][186][187] of 3D image moments. Note that most of the main ideas for accurate/fast calculations of 2D image moments (mentioned in Sections IV-A and IV-B) can be natural generalized to the 3D case.…”

Section: Definition Extensionmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

Qi,

Zhang,

Wang

et al. 2021

Preprint

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Image representation is an important topic in computer vision and pattern recognition. It plays a fundamental role in a range of applications towards understanding visual contents. Moment-based image representation has been reported to be effective in satisfying the core conditions of semantic description due to its beneficial mathematical properties, especially geometric invariance and independence. This paper presents a comprehensive survey of the orthogonal moments for image representation, covering recent advances in fast/accurate calculation, robustness/invariance optimization, and definition extension. We also create a software package for a variety of widely-used orthogonal moments and evaluate such methods in a same base. The presented theory analysis, software implementation, and evaluation results can support the community, particularly in developing novel techniques and promoting realworld applications.

show abstract

Section: Definition Extensionmentioning

confidence: 99%

Section: Definition Extensionmentioning

confidence: 99%

Section: Definition Extensionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

Qi,

Zhang,

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Moments and moments functions have been widely used as features in image analysis and scene analysis [1][2][3][4], in stereo image matching [5], in image retrieval [6], image and object recognition [7][8][9][10] and image watermarking [11][12][13] applications. Other research fields where the moments can be used are classification [14], pattern recognition and 3D image recognition and understanding [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Real-time Computation of Krawtchouk Moments on Gray Images Using Block Representation

2021

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In the field of image analysis, the discrete orthogonal moments have better image representation capability than the continuous orthogonal moments and geometric moments. Krawtchouk moments are discrete orthogonal moments able to capture the local features of an image. The disadvantage of the Krawtchouk moments is the high computational cost which is increased as higher-order moments are involved in the computations. In this paper, we propose an effective approach for the computation of Krawtchouk moments. The gray image is decomposed in a set of binary images. The most significant binary images are represented using Image Block Representation and their moments are computed fast using block techniques. The least significant binary images are substituted by a constant ideal image called "half-intensity" image, which has known Krawtchouk moment values. The proposed method has low computational error, low computational complexity and under certain conditions, it is able to achieve real-time processing rates.

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White blood cell automatic classification using deep learning and optimized quaternion hybrid moments

Tahiri

Hlouli

Bencherqui

et al. 2023

Biomedical Signal Processing and Control

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3D Image Representation Using Separable Discrete Orthogonal Moments

Cited by 5 publications

References 19 publications

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

Real-time Computation of Krawtchouk Moments on Gray Images Using Block Representation

White blood cell automatic classification using deep learning and optimized quaternion hybrid moments

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