Fabric Image Edge Detection Based on Octonion and Echo State Networks

Chen, Jian Cheng; Tu, Ang Yan

doi:10.4028/www.scientific.net/amm.519-520.712

Cited by 2 publications

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“…The octonion has one real part and seven imaginary parts which can represent the information of more colour image channels [9–12]. Octonions have many applications in the processing of colour images, as for example in colour image edge detection [9], in 2D and 3D signal processing [10], in artificial neural networks, where Octonionic neural networks are used as a computational model [11] and examples can continue.…”

Section: Introductionmentioning

confidence: 99%

Image splicing detection based on Markov features in discrete octonion cosine transform domain

et al. 2018

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To improve the poor robustness and low accuracy of the existing algorithms of image splicing detection, a novel passive image forgery detection method is proposed in this study, which is based on DOCT (discrete octonion cosine transform) and Markov. By introducing the octonion and DOCT, the colour information of six image channels (the RGB model and the HSI model) can be exhaustively extracted, which enhances the robustness of the algorithm. On the issue of improving the detection accuracy, the standard deviation is used to characterise the relationship of the colour information between the parts of DOCT coefficient matrix, and the K‐fold cross‐validation is introduced to improve the identification performance of the classifier. The steps of the algorithm are as follows: Firstly, the 8 × 8 block DOCT transform is used to the original image to obtain parts of block DOCT coefficient. Secondly, the standard deviation is used to process the corresponding parts of all blocks of the image. Finally, the Markov feature vector of the DOCT coefficient is extracted and feds to the LIBSVM (a library for support vector machines). When using LIBSVM for classification, K‐fold cross‐validation is executed to select the best parameter pairs. The experiment results demonstrate that the algorithm is superior to the other state‐of‐the‐art splicing detection methods.

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Section: Introductionmentioning

confidence: 99%

Image splicing detection based on Markov features in discrete octonion cosine transform domain

et al. 2018

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“…Octonions have many applications in processing of color images, as for example in color image edge detection, see (Chen, Tu, 2014), in remote sensing images, (Li, 2011), in 2D and 3D signal processing, (Snopek,15), in artificial neural networks, where Octonionic neural networks are used as a computational models, (Klco et all, 2017), in electrodynamics, see (Chanyal, 2011) and (Chen, Tu, 2014), in wireless data communication (Jouget, 2013) and examples can continue.…”

Section: Introductionmentioning

confidence: 99%

Some remarks regarding Quaternions and Octonions

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2017

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Fabric Image Edge Detection Based on Octonion and Echo State Networks

Cited by 2 publications

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Image splicing detection based on Markov features in discrete octonion cosine transform domain

Image splicing detection based on Markov features in discrete octonion cosine transform domain

Some remarks regarding Quaternions and Octonions

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