Blind 3D Model Watermarking based on Multi-Resolution Representation and Fuzzy Logic

Hiding a text message, audio data, 2D image or 3D model into some another image or model is called Wa- termarking. This paper presents a new idea to watermark a 3D surface model into another 3D surface model based on wavelets, Multi-Resolution Representation (MRR) and ANFIS: Adaptive Neuro Fuzzy Inference System. ANFIS model is trained by using a hybrid method which will then produce a weight factor as ANFIS output. Depending upon the ANFIS output, system selects different locations in horizontal sub-band of the 3D model coefficient to insert the watermark into the model. The watermark is extracted from the watermarked model by using non-blind method. The system is trained and tested against various 3D models. Results show that the system is robust for similarity transformations, noise attacks, smoothing attacks, and cropping attacks.

In this paper, effective multiresolution image representations using a combination of 2-D filter bank (FB) and directional wavelet transform (WT) are presented. The proposed methods yield simple implementation and low computation costs compared to previous 1-D and 2-D FB combinations or adaptive directional WT methods. Furthermore, they are nonredundant transforms and realize quad-tree like multiresolution representations. In applications on nonlinear approximation, image coding, and denoising, the proposed filter banks show visual quality improvements and have higher PSNR than the conventional separable WT or the contourlet.

Copyright is a serious problem in the digital world, the process of sending and distributing digital media is so easy nowadays, copyright in the digital world is very detrimental to those who feel that their digital rights are copied and pasted or taken without the consent of the creator. Therefore we need a way where when a digital file can be identified as original as a product, one of the right ways is to use a watermark technique. But often this watermark process can be lost or cannot be extracted because the digital file has gone through a compression process, duplicate, or something else. So in this study, it will be tried to increase the durability of a watermark in digital audio as a solution for identifying copyrighted digital works. Where the watermark process will use the RSA and MSB algorithms to enter information into a digital audio file, later this information can be extracted to view copyright ownership information from the digital audio. And it is hoped that this watermarking is resistant to various digital audio processes such as compression, duplication, and editing carried out on the file. the information that is inserted into is maintained without compromising the quality of the digital audio.

Microbial fuel cells (MFCs) is a promising technology that is able to simultaneously produce bioenergy and treat wastewater. Their potential large-scale application is still limited by the need of optimising their power density. The aim of this study is to simulate the absolute power output by ceramic-based MFCs fed with human urine by using a fuzzy inference system in order to maximise the energy harvesting. For this purpose, membrane thickness, anode area and external resistance, were varied by running a 27-parameter combination in triplicate with a total number of 81 assays performed. Performance indices such as R2 and variance account for (VAF) were employed in order to compare the accuracy of the fuzzy inference system designed with that obtained by using nonlinear multivariable regression. R2 and VAF were calculated as 94.85% and 94.41% for the fuzzy inference system and 79.72% and 65.19% for the nonlinear multivariable regression model, respectively. As a result, these indices revealed that the prediction of the absolute power output by ceramic-based MFCs of the fuzzy-based systems is more reliable than the nonlinear multivariable regression approach. The analysis of the response surface obtained by the fuzzy inference system determines that the maximum absolute power output by the air-breathing set-up studied is 450 μW when the anode area ranged from 160 to 200 cm2, the external loading is approximately 900 Ω and a membrane thickness of 1.6 mm, taking into account that the results also confirm that the latter parameter does not show a significant effect on the power output in the range of values studied.

This paper is concerned with scheduling in flexible manufacturing systems (FMSs) using a fuzzy logic (FL) approach. Four fuzzy input variables: machine allocated processing time, machine priority, due date priority and setup time priority are defined. The job priority is the output fuzzy variable, showing the priority status of a job to be selected for next operation on a machine. The model will first select the machines and then assign operations based on a multi-criteria scheduling scheme. The performance of the approach is compared against established methods reported in the literature. The performance measures considered average machine utilisation, meeting due dates, setup times, work in process and mean flow times. The test results demonstrate the superiority of the fuzzy logic approach in most performance measures.

<p>Interactions between genes and the proteins they synthesize shape genetic regulatory networks (GRN). Several models have been proposed to describe these interactions, been the most commonly used those based on ordinary differential equations (ODEs). Some approximations using piecewise linear differential equations (PLDEs), have been proposed to simplify the model non linearities. However they not allways give good results. In this context, it has been developed a model capable of representing small GRN, combining characteristics from the ODE’s models and fuzzy inference systems (FIS). The FIS is trained through an artificial neural network, which forms an Adaptive Nertwork-based Fuzzy Inference System (ANFIS). This network allows to adapt the membership and output functions from the FIS according to the training data, thus, reducing the previous knowledge needed to model the specific phenomenon.<br /> In addition, Fuzzy Logic allows to express their rules through linguistic labels, which also allows to incorporate expert knowledge in a friendly way. The proposed model has been used to describe the Lac Operon in E. Coli and it has been compared with the models already mentioned. The outcome errors due to the training process of the ANFIS network are comparable with those of the models based on ODEs. Additionally, the fuzzy logic approach provides modeling flexibility and knowledge acquisition advantages.</p>

There are many invisible image watermarking techniques that can be used to provide authenticity and intellectual property rights. This paper presents a comprehensive frame work of invisible image watermarking techniques FFT (Fast Fourier Transform), DCT (Discrete Cosine Transform) and DWT (Discrete Wavelet Transform) based on Arnold Transform (AT) scrambling. DWT based on Arnold Transform gives better results in terms of authenticity and robustness as compared to FFT and DCT technique. The performance of each invisible image watermarking technique is theoretically and mathematically analyzed based on parameter PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error). Also, the performance of each technique is analyzed on different noise and transformation attack. An experiment based on gray scale image that shows there is consistency between the theoretical analysis and the experimental result analysis. In extraction process, invisible image watermarking technique is applied on both host image and watermarked image.

This paper presents a new view synthesis techniques using morphing and 2-d discrete wavelet transformation. We completely base on pairwise images that are known without calibrating camera and the depth information of images. First, we estimate the Fundamental Matrix related with any pair of images. Second, using fundamental matrix, any pair of image planes can be rectified to be parallel and their corresponding points are lying on the same scanline. This gives an opportunity to generate new views with linear interpolating technique. Third, the pre-warped images are then decomposed into hierarchical structure with wavelet transformation. Corresponding coefficients between two decomposed images are therefore linear interpolated to form the multiresolution representation of an intermediate view. Any quantization techniques can be embedded here to compress the coefficients in depth. The compressed format is very suitable for storage and communication. Fourthly, when displaying, compressed images are decoded and an inverse wavelet transform is achieved. Finally, we use a post-warping procedure to transform the interpolated views to its desired position. A nice future of using wavelet transformation is its multiresolution representation mode, which makes generating views can be refined progressively and hence suitable for communication.

The transformation model plays a vital role in medical image processing. This paper proposed new two Mixed Transforms models that are the hybrid combination of linear and nonlinear Transformations techniques. The first mixed transform is computed in three steps: calculate 2D discrete cosine transform (DCT) of the image, and applying Arnold Transform (AT) on the DCT coefficients, and applying the discrete Wavelet Transform (DWT) on the result to get which was abbreviated as (CAW). The second mixed transform consists of firstly computing the discrete Fourier transform (DFT), net applying the Arnold Transform (AT), and finally, the computation of discrete Wavelet Transform (DWT) which was abbreviated as (FAW). These transforms have superior directional representations as compared to other multiresolution representations such as DWT or DCT and work as non-adaptive mixed transformations for multi-scale object analysis. Due to their relationship to the wavelet idea, they are finding increasing use in areas like image processing and scientific computing. These transforms are tested in medical image classification task and their performances are compared with that of the traditional transforms. CAW and FAW transforms are used in the feature extraction stage of a classification VGG16 deep learning (DNN) task of Tumor MRI medical image. The numerical findings favor CAW and FAW over the wavelet transform for estimating and classifying pictures. From the results obtained it was shown that the CAW and FAW transform gave e much higher classification rate than that achieved with the traditional transforms, namely DCT, DFT and DWT. Furthermore, this combination leads to a family of directional and multi-transformation bases for image processing.

This paper addresses an Arnold Transform based gray image embedding in video using Discrete Wavelet Transform (DWT). In the proposed scheme, the video is authenticated with different parts of watermark by using histogram-based scene change technique. Each frame is estranged into three planes. DWT is applied to selective plane in each frame to putrefy into sub-bands. The chosen secrete image is separated into 8-bit planes. Bit plane image is further scrambled using an Arnold transform to get high protection of watermark. In this scheme, embedding is done in mid and high-frequency coefficients of DWT without mortifying the perceptual quality of video. Hidden image is extracted from the marked video by following the inverse processing steps. Robustness is tested by subjecting the marked video to various video processing and image processing attacks. Simulation results show that the proposed scheme is highly resistant to frame averaging, frame dropping, and noise attacks.

This paper presents a method for detecting damage in irregular 2D and 3D continuum structures based on combination of wavelet transform (WT) with fuzzy inference system (FIS) and particle swarm optimization (PSO). Many damage detection methods study regular structures. This method studies irregular structures and doesn\'t need response of healthy structures. First the damaged structure is analyzed with finite element methods, and damage response is obtained at the finite element points that have irregular distance, secondly the FIS, which is optimized by PSO is used to obtain responses at points, having equal distance by response at those points that previously obtained by the finite element methods. Then a 2D (for 2D continuum structures) or a 3D (for 3D continuum structures) matrix is performed by equal distance point response. Thirdly, by applying 2D or 3D wavelet transform on 2D or 3D matrix that previously obtained by FIS detail matrix coefficient of WT is obtained. It is shown that detail matrix coefficient can determine the damage zone of the structure by perturbation in the damaged area. In order to illustrate the capability of proposed method some examples are considered.

Design of robust and imperceptible digital watermarks has great importance now days. Watermarked images are affected by various attacks such as cropping, salt & pepper noise and rotation. These attacks destroy the inserted watermark, so that the copyright problem may arise. The effect of these attacks can be reduced by properly inserting the watermark with effective algorithm. In this paper, a blind robust watermarking algorithm using Dynamic Fuzzy Inference System is developed in wavelet Transform domain. The proposed algorithm is robust to cropping, salt & pepper noise and rotation attacks compared to other algorithms.

A multiresolution representation of a 3D model is one of important means for its fast rendering. In this paper we present an approach to build up an adaptive multiresolution representation for a point-sampled 3D model directly from its set of sample points. The binary space partition (BSP) is used to recursively split a point-sampled model according to the covariance analysis of its sample points. Thus a model is subdivided along the direction of the greatest variation, and the partition is adaptive to the spatial distribution of sample points. A multiresolution hierarchy based on bounding spheres is constructed for a model from its subdivision. Since a point-sampled model has not any topological information, the k-nearest clustering method is adopted to find a local neighborhood for each sample point, and a surface normal at each sample point is estimated by fitting a tangent plane in its neighborhood. We construct adaptive multiresolution hierarchies for several point-sampled models and display them based on the point-based rendering. Experimental results show this approach is efficient for rendering complex point-based 3D models.

A Wavelet Transform (WT) is a mathematical technique that transforms a signal into different frequency components, each analyzed with a resolution that matches its scale. Wavelets are small waves with limited duration and they possess both time and frequency localization, which means they can capture both high-frequency and low-frequency information simultaneously. There are several types of wavelet transforms, and, depending on the application, one may be preferred to the others. For a continuous input signal, the time and scale parameters can be continuous leading to the Continuous Wavelet Transform (CWT) and Discrete wavelets. Image processing is a critical area in modern computing with applications ranging from medical imaging to remote sensing and computer vision. Traditional Fourier analysis has limitations in capturing localized features in images due to its global nature. Wavelet Transform (WT), with its multi-resolution and localized time-frequency representation, provides a powerful alternative for analysing and processing visual information. This paper presents a comprehensive overview of the theory of wavelet transforms, their mathematical foundations, and applications in image processing and signal processing, including compression of images, denoising and feature extraction from images.

This paper introduces a blind watermarking scheme based on remainder of distance vector between two wavelet blocks. In the proposed scheme, the wavelet coefficients from frequency sub-band LH3 and HL3 have been grouped into two blocks for invisible watermark embedding and extraction. In the next stage, the distance vectors between two blocks are computed and then remainder after division calculated. For embedding 0 and 1 bit, the remainder of distance vectors fixed up to specific values. By increasing divisor, it is possible to embed watermark bits in the host image with more energy; however, this will lead to PSNR reduction. The proposed method, takes advantage of considerable robustness against prevalent attacks and another profit have wide range trade off between watermark embedding energy and the quality of watermarked image with select suitable value of divisor. The experimental results show that the proposed algorithm is robust against image processing attacks and also the comparison analysis indicates that has better performance than the other blind watermarking methods.