Migrating orthogonal rotation-invariant moments from continuous to discrete space

被引：0
|
作者：
Lin, HB [1 ]
Si, J [1 ]
Abousleman, GR [1 ]
机构：
[1] Arizona State Univ, Dept Elect Engn, Tempe, AZ 85287 USA
来源：
2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING | 2005年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Orthogonality and rotation invariance are important feature properties in digital signal processing. Orthogonality enables a target to be represented by a compact number of features, while rotation invariance results in unique features for a target with different orientations. The orthogonal, rotation-invariant moments (ORIMs), such as Zernike, Pseudo-Zernike, and Orthogonal Fourier-Melling moments, are defined in continuous space. These ORIMs have been digitized and have been demonstrated effectively for some digital imagery applications. However, digitization compromises the orthogonality of the moments, and hence, reduces their precision. Therefore, digital ORIMs are incapable of representing the fine details of images. In this paper, we propose a numerical optimization technique to improve the orthogonality of the digital ORIMs. Simulation results show that our optimized digital ORIMs can be used to reproduce subtle details of images.
引用
收藏
页码：245 / 248
页数：4
相关论文
共 50 条
  • [21] RIVQ-VAE: Discrete Rotation-Invariant 3D Representation Learning
    Mezghanni, Mariem
    Boulkenafed, Malika
    Ovsjanikov, Maks
    2024 INTERNATIONAL CONFERENCE IN 3D VISION, 3DV 2024, 2024, : 1382 - 1391
  • [22] RIMFRA: Rotation-invariant multi-spectral facial recognition approach by using orthogonal polynomials
    Taner Cevik
    Nazife Cevik
    Multimedia Tools and Applications, 2019, 78 : 26537 - 26567
  • [23] Comparative analysis of continuous and discrete orthogonal moments for face recognition
    Kaur, Parminder
    Pannu, Husanbir Singh
    2017 INTERNATIONAL CONFERENCE OF ELECTRONICS, COMMUNICATION AND AEROSPACE TECHNOLOGY (ICECA), VOL 1, 2017, : 449 - 453
  • [24] Discrete vs. continuous orthogonal moments for image analysis
    Mukundan, R
    Ong, SH
    Lee, PA
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON IMAGING SCIENCE, SYSTEMS AND TECHNOLOGY, VOLS I AND II, 2001, : 23 - 29
  • [25] RIMFRA: Rotation-invariant multi-spectral facial recognition approach by using orthogonal polynomials
    Cevik, Taner
    Cevik, Nazife
    MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (18) : 26537 - 26567
  • [26] Rule generation from a rotation-invariant neural pattern recognition system
    Dept. of Inf. Sci. and Intelligent Syst., Tokushima Univ., 2-1, Minami-Josanjirns, Tokushima
    770-8506, Japan
    不详
    790-0923, Japan
    ICONIP , Int. Conf. Neural Inf. Process. - Proc., 1600, (706-711):
  • [27] Learning how to extract rotation-invariant and scale-invariant features from texture images
    Montoya-Zegarra, Javier A.
    Paulo Papa, Joao
    Leite, Neucimar J.
    da Silva Torres, Ricardo
    Falcao, Alexandre X.
    EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2008, 2008 (1)
  • [28] Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images
    Javier A. Montoya-Zegarra
    João Paulo Papa
    Neucimar J. Leite
    Ricardo da Silva Torres
    Alexandre Falcão
    EURASIP Journal on Advances in Signal Processing, 2008
  • [29] Rotation invariants of vector fields from orthogonal moments
    Yang, Bo
    Kostkova, Jitka
    Flusser, Jan
    Suk, Tomas
    Bujack, Roxana
    PATTERN RECOGNITION, 2018, 74 : 110 - 121
  • [30] Single-image super-resolution using orthogonal rotation invariant moments
    Singh, Chandan
    Aggarwal, Ashutosh
    COMPUTERS & ELECTRICAL ENGINEERING, 2017, 62 : 266 - 280
12345