Quaternion Zernike moments and their invariants for color image analysis and object recognition

Chen, B. J.; Hu, Shu; Zhang, Hengliang; Chen, G.; Toumoulin, Christine; Dillenseger, Jean-Louis; Luo, Limin

doi:10.1016/j.sigpro.2011.07.018

Cited by 177 publications

(55 citation statements)

References 39 publications

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“…Quaternions are mainly used in computer vision problems due to their ability to represent 3D rotations easily. They are also used for the representation of color images by encoding the RGB channels into their imaginary parts [13][14]. The main advantage of quaternion-based representation is that any information stored in array structures can be totally treated as a vector.…”

Section: A Quaternionsmentioning

confidence: 99%

A new method for breast cancer identification using multi-modal features in quaternionic form

Apostolopoulos

Koutras

Christoyianni

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Mammography is still the most effective procedure for early diagnosis of the breast cancer. Computer-aided Diagnosis (CAD) systems can be very helpful in this direction for radiologists to recognize abnormal and normal regions of interest in digital mammograms faster than traditional screening program. In this work, we propose a new method for breast cancer identification of all types of lesions in digital mammograms using multimodal features in a quaternionic representation. The proposed method consists of two steps: First, a novel feature extraction module utilizes two dimensional discrete transforms based on ART, Shapelets, Zernike moments and Gabor filters to decompose Regions of Suspicion (ROS) into a set of localized basis functions with different shapes. The extracted features are then fused and presented in quaternionic representation to the classification module in the second step. For the classification task, we propose a new type of classifier (Q-classifier) that successfully, accurately, with low computational cost and higher speed of diagnosis, recognizes normal and abnormal ROS from mammograms. The proposed method is evaluated on the Mini-MIAS database. The methods' performance is evaluated using Receiver Operating Characteristics (ROC) curve. The achieved result AUC = 0.934 shows that the proposed method can be quite effective and can be used as a tool for efficiently diagnosing breast cancer compared to similar techniques presented in the literature that use SVM classifiers and unimodal features.

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Section: A Quaternionsmentioning

confidence: 99%

A new method for breast cancer identification using multi-modal features in quaternionic form

Apostolopoulos

Koutras

Christoyianni

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…Orthogonality here means that there is no redudancy or overlapping of information between the moments. Thus moments are uniquely quantified based on their orders ( [6,7]). The distinguishing feature of ZM is the invariance of its magnitude with respect to rotation ( [8,9,10,11]).…”

Section: Zernike Momentmentioning

confidence: 99%

Zernike Moments and Genetic Algorithm: Tutorial and Application

Babatunde¹,

Leisa²,

Liu³

et al. 2014

BJMCS

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Aims/ objectives: To demontrate effectiveness of Zernike Moments for Image Classification. Zernike moment(ZM) is an excellent region-based moment which has attracted the attentions of many image processing researchers since its first application to image analysis. Many papers have been published on several works done on ZM but no single paper ever give a detailed information of how the computation of ZM is done from the time the image is captured to the computation of ZM. This work showed how to effectively apply ZM on RGB images. We have demonstrated the effectiveness of Zernike moment in image classification system. A neuro-genetic intelligent system has been built with PNN classifier. The feature extracted viz ZM and Geometric features were further subjected to GA to bring the best combinatorial features for optimal accuracy. The algebraic structure of our novel fitness function enabled the GA to select the best results. The 10-fold CV used enabled the whole system to be unbiased giving a classification accuracy of 90.05%. A demonstration of affine properties of ZM are comprehensively stated and explained. In summary, the ZM enabled the classifier to have improved accuracy of 91% as compared with Geometric features with 89 % accuracy.

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“…Despite image moments' close mathematical nature to DFT, the rst quaternion moment families have been recently introduced, based on Fourier-Mellin [7] and Zernike [3] polynomials. However, the specic moments families are calculated based on continuous orthogonal kernels which are dened in the polar coordinate system.…”

Section: Quaternion Momentsmentioning

confidence: 99%

Should We Consider Adaptivity in Moment-based Image Watermarking ?

Papakostas¹

2014

Moments and Moment Invariants - Theory and Applications

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The term adaptivity is absent from the state-of-the-art moment-based image watermarking methods. A question to be answered is whether adaptive watermark insertion will guide to the enhancement of image's security (concerning a number of requirements such as robustness, imperceptibility, complexity and capacity). Initially, the term adaptivity is being unfold from dierent perspectives; the selection of the most qualied coecients (considering their order and magnitude) for carrying the watermark information; the selection of the most qualied image region for hosting the Efstratios D. Tsougenis

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Quaternion Zernike moments and their invariants for color image analysis and object recognition

Cited by 177 publications

References 39 publications

A new method for breast cancer identification using multi-modal features in quaternionic form

A new method for breast cancer identification using multi-modal features in quaternionic form

Zernike Moments and Genetic Algorithm: Tutorial and Application

Should We Consider Adaptivity in Moment-based Image Watermarking ?

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