An evaluation on circularly orthogonal moments for image representation

3D Image Representation Using Separable Discrete Orthogonal Moments

Procedia Computer Science ◽

10.1016/j.procs.2019.01.047 ◽

2019 ◽

Vol 148 ◽

pp. 389-398 ◽

Cited By ~ 1

Author(s):

Imad Batioua ◽

Rachid Benouini ◽

Khalid Zenkouar ◽

Said Najah ◽

Hakim El Fadili ◽

...

Keyword(s):

Image Representation ◽

3D Image ◽

Orthogonal Moments ◽

Discrete Orthogonal

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Image representation using separable two-dimensional continuous and discrete orthogonal moments

Pattern Recognition ◽

10.1016/j.patcog.2011.10.002 ◽

2012 ◽

Vol 45 (4) ◽

pp. 1540-1558 ◽

Cited By ~ 36

Author(s):

Hongqing Zhu

Keyword(s):

Image Representation ◽

Two Dimensional ◽

Orthogonal Moments ◽

Discrete Orthogonal

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Image representation based on fractional order Legendre and Laguerre orthogonal moments

International Journal of ADVANCED AND APPLIED SCIENCES ◽

10.21833/ijaas.2021.02.007 ◽

2021 ◽

Vol 8 (2) ◽

pp. 54-59

Author(s):

R. M. Farouk ◽

◽

Qamar A. A. Awad ◽

Keyword(s):

Fractional Order ◽

Image Representation ◽

Computational Time ◽

Gray Level ◽

Global Features ◽

Orthogonal Functions ◽

Orthogonal Moments ◽

Fractional Moments ◽

Gray Level Image ◽

Detection Filter

In this paper, we have introduced new sets of fractional order orthogonal basis moments based on Fractional order Legendre orthogonal Functions (FLeFs) and Fractional order Laguerre orthogonal Functions (FLaFs) for image representation. We have generated a novel set of Fractional order Legendre orthogonal Moments (FLeMs) from fractional order Legendre orthogonal functions and a new set of Fractional order Laguerre orthogonal Moments (FLaMs) from the fractional order Laguerre orthogonal functions. The new presented sets of (FLeMs) and (FLaMs) are tested with the recently introduced Fractional order Chebyshev orthogonal Moments (FCMs). This edge detection filter can be used successfully in the gray level image and color images. The new sets of fractional moments are used to reconstruct the gray level image. The numerical results show FLeMs and FLaMs are promised techniques for image representation. The computational time of the proposed techniques is compared with the computational time of Chebyshev orthogonal Moments techniques and gives better results. Also, the fractional parameters give the flexibility of studying global features of the image at different positions of moments.

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

ACM Computing Surveys ◽

10.1145/3479428 ◽

2023 ◽

Vol 55 (1) ◽

pp. 1-35

Author(s):

Shuren Qi ◽

Yushu Zhang ◽

Chao Wang ◽

Jiantao Zhou ◽

Xiaochun Cao

Keyword(s):

Image Representation ◽

Semantic Description ◽

Analysis Software ◽

Theory Analysis ◽

Orthogonal Moments ◽

Geometric Invariance ◽

Mathematical Properties ◽

Real World Applications ◽

Comprehensive Survey ◽

Core Conditions

Image representation is an important topic in computer vision and pattern recognition. It plays a fundamental role in a range of applications toward 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 article presents a comprehensive survey of the orthogonal moments for image representation, covering recent advances in fast/accurate calculation, robustness/invariance optimization, definition extension, and application. 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 real-world applications.

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