Classification of Islamic Geometric Pattern Images Using Zernike Moments

2011 ◽  
Author(s):  
Maryam Ahadian ◽  
Azam Bastanfard
Keyword(s):  
Zernike Moments ◽  
Geometric Pattern

Investigation of the applicability of Zernike moments to the classification of SDS 2D-PAGE maps

2011 ◽  
Vol 400 (5) ◽  
pp. 1419-1431 ◽  
Author(s):  
Emilio Marengo ◽  
Marina Cocchi ◽  
Marco Demartini ◽  
Elisa Robotti ◽  
Marco Bobba ◽  
...  
Keyword(s):  
Zernike Moments ◽  
2D Page

scholarly journals Orthogonal Moment Extraction and Classification of Melanoma Images

2018 ◽  
Author(s):  
Sudhakar Singh ◽  
Shabana Urooj
Keyword(s):  
Support Vector Machine ◽  
Zernike Moments ◽  
Support Vector ◽  
Moment Invariants ◽  
Swarm Optimization ◽  
Orthogonal Moments ◽  
Equal Interval ◽  
The Moment ◽  
Phase Angles

This paper provides orthogonal moments (OM) such as, Zernike Moments(ZM), Psuedo Zernike Moments(PZM) and Orthogonal Fourier Mellin Moments(OFMM) for the analysis of melanoma images. The moment invariants may vary with respect to geometric variations. For the analysis of orthogonal moments hundred random melanoma images and hundred non-melanoma images have been taken into consideration from the database of 570 melanoma images and 250 non-melanoma images respectively. Orthoganal moments have been computed by varying the phase angles from 10° to 40° with an equal interval of 10° degree for the orders 2, 4,8,16,32,64,128,256 respectively. For the optimal OMs Particle Swarm Optimization (PSO) technique have been used. These set of extracted optimal OMs have been further applied to classify melanoma images. Support Vector Machine (SVM) has been used for the classification of [1]sensitivity=88.78%.

An Approach to Recognize Handwritten Hindi Characters Using Substantial Zernike Moments With Genetic Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 66-81
Author(s):  
Ajay Indian ◽  
Karamjit Bhatia
Keyword(s):  
Genetic Algorithm ◽  
Recognition Accuracy ◽  
Learning Algorithm ◽  
Zernike Moments ◽  
Classification Model ◽  
Computational Time ◽  
The Moment ◽  
Moment Features ◽  
Two Parameters

A technique to recognize off-line handwritten Hindi character is suggested by employing Zernike complex moments like a tool to describe the characteristics of a character. Further, an algorithm for selecting the features is employed to identify the substantial image moments from the extracted moments, as the extracted moments may have some insignificant ones. Insignificant moments can increase the computational time and can also degrade the classification accuracy. Thus, the objectives of the study are twofold: (1) to find the important Zernike moments by employing the Genetic algorithm (GA) and (2) the classification of each character is performed using neural network. This way, the performance of the proposed technique is evaluated on two parameters (i.e., speed and recognition accuracy). Further, the efficacy of GA for selecting the moment features is assessed, and the efficacy of selected Zernike complex moments using GA is analyzed for handwritten Hindi characters. Here, the authors used a resilient backpropagation learning algorithm (RPROP) as a classification model.

scholarly journals On the classification of simple and complex biological images using Krawtchouk moments and Generalized pseudo-Zernike moments: a case study with fly wing images and breast cancer mammograms

2021 ◽  
Vol 7 ◽  
pp. e698
Author(s):  
Jia Yin Goh ◽  
Tsung Fei Khang
Keyword(s):  
Image Analysis ◽  
Classification Performance ◽  
Image Features ◽  
Zernike Moments ◽  
Taxonomic Classification ◽  
Learning Context ◽  
Orthogonal Moments ◽  
Krawtchouk Moments

In image analysis, orthogonal moments are useful mathematical transformations for creating new features from digital images. Moreover, orthogonal moment invariants produce image features that are resistant to translation, rotation, and scaling operations. Here, we show the result of a case study in biological image analysis to help researchers judge the potential efficacy of image features derived from orthogonal moments in a machine learning context. In taxonomic classification of forensically important flies from the Sarcophagidae and the Calliphoridae family (n = 74), we found the GUIDE random forests model was able to completely classify samples from 15 different species correctly based on Krawtchouk moment invariant features generated from fly wing images, with zero out-of-bag error probability. For the more challenging problem of classifying breast masses based solely on digital mammograms from the CBIS-DDSM database (n = 1,151), we found that image features generated from the Generalized pseudo-Zernike moments and the Krawtchouk moments only enabled the GUIDE kernel model to achieve modest classification performance. However, using the predicted probability of malignancy from GUIDE as a feature together with five expert features resulted in a reasonably good model that has mean sensitivity of 85%, mean specificity of 61%, and mean accuracy of 70%. We conclude that orthogonal moments have high potential as informative image features in taxonomic classification problems where the patterns of biological variations are not overly complex. For more complicated and heterogeneous patterns of biological variations such as those present in medical images, relying on orthogonal moments alone to reach strong classification performance is unrealistic, but integrating prediction result using them with carefully selected expert features may still produce reasonably good prediction models.

Classification of benign and malignant masses based on Zernike moments

2011 ◽  
Vol 41 (8) ◽  
pp. 726-735 ◽  
Author(s):  
Amir Tahmasbi ◽  
Fatemeh Saki ◽  
Shahriar B. Shokouhi
Keyword(s):  
Zernike Moments

Classification of damages in composite images using Zernike moments and support vector machines

2019 ◽  
Vol 168 ◽  
pp. 77-86 ◽  
Author(s):  
A.R. Jac Fredo ◽  
R.S. Abilash ◽  
R. Femi ◽  
A. Mythili ◽  
C. Suresh Kumar
Keyword(s):  
Support Vector Machines ◽  
Zernike Moments ◽  
Support Vector ◽  
Vector Machines
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