Watermarking Digital Image Using Fuzzy Matrix Compositions and Rough Set

Bhattacharya, Sharbani

doi:10.14569/ijacsa.2014.050621

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…The paper proposes two fuzzy matrix composition Fuzzy Product-Mod-Minus composition and Fuzzy Compliment-Product-Minus composition. Embedding will be done creating Fuzzy Rough set from these two new compositions, published Fuzzy Max-Mod-Minus composition and Fuzzy Compliment-Sum-Minus composition [29] and the Fuzzy Max-Min composition.…”

Section: Fuzzy Matrix Compositionsmentioning

confidence: 99%

“…The Fuzzy Compliment-Sum-Minus composition [29] is consisting of following method. Let A, B and C are fuzzy set with A(x1, x2), B (y1, y2) and C(z1,z2).Let us say,…”

Section: Fuzzy Compliment-sum-minus Compositionmentioning

confidence: 99%

“…compositions of fuzzy matrices as said. The most appropriate composition is chosen for Presently we have used hit and trial method to embed.Any image can have invisible watermark with fuzzy matrix compositions[29]. Next section explains the proposed method of creating Fuzzy Rough set to embed the watermark.VII.…”

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confidence: 99%

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Watermarking Digital Image Using Fuzzy Matrix Compositions and Rough Set

Bhattacharya¹

2014

ijacsa

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Abstract-Watermarking is done in digital images for authentication and to restrict its unauthorized usages. Watermarking is sometimes invisible and can be extracted only by authenticated party. Encrypt a text or information by publicprivate key from two fuzzy matrix and embed it in image as watermark. In this paper we proposed two fuzzy compositions Product-Mod-Minus, and Compliment-Product-Minus. Embedded watermark using Fuzzy Rough set created from fuzzy matrix compositions.

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Section: Fuzzy Matrix Compositionsmentioning

confidence: 99%

“…The Fuzzy Compliment-Sum-Minus composition [29] is consisting of following method. Let A, B and C are fuzzy set with A(x1, x2), B (y1, y2) and C(z1,z2).Let us say,…”

Section: Fuzzy Compliment-sum-minus Compositionmentioning

confidence: 99%

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Watermarking Digital Image Using Fuzzy Matrix Compositions and Rough Set

Bhattacharya¹

2014

ijacsa

Self Cite

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“…Moreover, the increasing number of cases of diabetic retinopathy globally requires extending efforts in developing visual tools to assist in the analytic of the series of retinal disease. These decision support systems for retinal ADD, as (Bhattacharya, 2014) for non-proliferative diabetic retinopathy have been improved from recent machine learning success on the high dimensional images processing by featuring details on the blood vessel. (Lin et al, 2000) demonstrated an automated technique for the segmentation of the blood vessels by tracking the center of the vessels on Kalman Filter.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases

Yang,

Huang,

Liu

et al. 2018

Preprint

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Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. We propose a novel visualassisted diagnosis hybrid model based on the support vector machine (SVM) and deep neural networks (DNNs). The model incorporates complementary strengths of DNNs and SVM. Furthermore, we present a new clinical retina label collection for ophthalmology incorporating 32 retina diseases classes. Using EyeNet, our model achieves 89.73% diagnosis accuracy and the model performance is comparable to the professional ophthalmologists.

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“…Moreover, the increasing number of cases of diabetic retinopathy globally requires extending efforts in developing visual tools to assist in the analytic of the series of retinal disease. These decision support systems for retinal ADD, as [6] for non-proliferative diabetic retinopathy have been improved from recent machine learning success on the high dimensional images processing by featuring details on the blood vessel. [7] demonstrated an automated technique for the segmentation of the blood vessels by tracking the center of the vessels on Kalman Filter.…”

Section: Introductionmentioning

confidence: 99%

Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model

Yang¹,

Liu²,

Huang³

et al. 2018

Preprint

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Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. Based on the fact that fundus structure and vascular disorders are the main characteristics of retinal diseases, we propose a novel visual-assisted diagnosis hybrid model mixing the support vector machine (SVM) and deep neural networks (DNNs). Furthermore, we present a new clinical retina labels collection sorted by the professional ophthalmologist from the educational project Retina Image Bank, called EyeNet, for ophthalmology incorporating 52 retina diseases classes. Using EyeNet, our model achieves 90.40% diagnosis accuracy, and the model performance is comparable to the professional ophthalmologists.

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Watermarking Digital Image Using Fuzzy Matrix Compositions and Rough Set

Cited by 4 publications

References 19 publications

Watermarking Digital Image Using Fuzzy Matrix Compositions and Rough Set

Watermarking Digital Image Using Fuzzy Matrix Compositions and Rough Set

A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases

Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model

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