Translation and scale invariants of Legendre moments

2004 ◽  
Vol 37 (1) ◽  
pp. 119-129 ◽  
Author(s):  
Chee-Way Chong ◽  
P. Raveendran ◽  
R. Mukundan
Keyword(s):  
Legendre Moments ◽  
Scale Invariants

Accurate 2D and 3D images classification using translation and scale invariants of Meixner moments

2021 ◽  
Author(s):  
M. Yamni ◽  
A. Daoui ◽  
O. El ogri ◽  
H. Karmouni ◽  
M. Sayyouri ◽  
...  
Keyword(s):  
3D Images ◽  
Meixner Moments ◽  
2D And 3D ◽  
Scale Invariants

scholarly journals 2D Legendre Moments-Based Visual Control

2012 ◽  
Vol 162 ◽  
pp. 487-496 ◽  
Author(s):  
Aurelien Yeremou Tamtsia ◽  
Youcef Mezouar ◽  
Philippe Martinet ◽  
Haman Djalo ◽  
Emmanuel Tonye
Keyword(s):  
Visual Servoing ◽  
Dynamic Range ◽  
High Sensitivity ◽  
High Dynamic Range ◽  
Interaction Matrix ◽  
Geometric Moments ◽  
Control Scheme ◽  
Control Schemes ◽  
Legendre Moments ◽  
Set Of Points

Among region-based descriptors, geometric moments have been widely exploited to design visual servoing schemes. However, they present several disadvantages such as high sensitivity to noise measurement, high dynamic range and information redundancy (since they are not computed onto orthogonal basis). In this paper, we propose to use a class of orthogonal moments (namely Legendre moments) instead of geometric moments to improve the behavior of moment-based control schemes. The descriptive form of the interaction matrix related to the Legendre moments computed from a set of points is rst derived. Six visual features are then selected to design a partially-decoupled control scheme. Finally simulated and experimental results are presented to illustrate the validity of our proposal.

scholarly journals Liver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments

2018 ◽  
Vol 7 (2.6) ◽  
pp. 306
Author(s):  
Aravinda H.L ◽  
M.V Sudhamani
Keyword(s):  
Texture Feature ◽  
Region Growing ◽  
Ct Images ◽  
Higher Order ◽  
Liver Carcinoma ◽  
Tumor Segmentation ◽  
Abdominal Ct ◽  
Simple Linear Iterative Clustering ◽  
Classification Of Tumors ◽  
Legendre Moments

The major reasons for liver carcinoma are cirrhosis and hepatitis.  In order to  identify carcinoma in the liver abdominal CT images are used. From abdominal CT images, segmentation of liver portion using adaptive region growing, tumor segmentation from extracted liver using Simple Linear Iterative Clustering is already implemented. In this paper, classification of tumors as benign or malignant is accomplished using Rough-set classifier based on texture feature extracted using Average Correction Higher Order Local Autocorrelation Coefficients and Legendre moments. Classification accuracy achieved in proposed scheme is 90%. The results obtained are promising and have been compared with existing methods.

Scale Invariants from Gaussian-Geometric Moments

2021 ◽  
Author(s):  
Tao Sun ◽  
Shulin Yang ◽  
Bin Wu
Keyword(s):  
Geometric Moments ◽  
Scale Invariants
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