다중 해상도 웨이블릿 변환과 영상 융합을 이용한 워터마킹

The aims of Brain-Computer interfaces (BCI) research is helping paralyzed people communicating with others by using their electroencephalogram (EEG) signals. In this study, EEG signals from 5 mental tasks were recorded from 7 subjects and combinations of 2 different mental tasks were studied for each subject for one trial. The motivation for this work is using Linear predictive Coding (LPC) method to compress channels of EEG one channel. Eight features are employed for each signal of EEG using LPC 1st order followed by 3 level Discrete Wavelet Transform (DWT). New mother wavelet is used to be near the waveform of EEG signals. Statistical calculations are conducted for the 4 coefficients of DWT. Classification is conducted using support vector machine SVM. The classifier using SVM provided a high recognition rate reaching up to 100%, in some cases, and an average rate of about 85 %. The average specificity percent is 83.33 %. The average sensitivity percent is 86.66%.

Most Discrete Wavelet Transform(DWT)/Singular Value Decomposition(SVD) watermark methods are robust to common attacks, such as loss compression, noise and filtering attacks, but they cannot resist geometrical attacks. As Scale Invariant Feature Transform(SIFT) feature points are invariant to rotation, scaling and translation, a blind DWT-SVD watermark method against geometrical attacks based on SIFT is proposed. The square of the inscribed circle of the host image matrix is chosen as the watermark embedding area. Firstly, the watermark embedding area is decomposed with 1-level DWT and the low-frequency sub-band is divided into non-overlapping blocks; then the SVD is applied to every block. Secondly, the watermark is embedded by quantifying the maximum singular value of each block. Finally, SIFT feature points of the watermarked image are saved to detect and correct possible geometrical attacks. The experimental results show that the proposed method not only has the robustness to any angle of rotation attacks, wide range of scaling attacks and translation attacks, but also can achieve blind extraction.

Because of the transition from analog to digital technologies, content owners are seeking technologies for the protection of copyrighted multimedia content. Encryption and watermarking are two major tools that can be used to prevent unauthorized consumption and duplication. In this paper, we generalize an idea in a recent paper that embeds a binary pattern in the form of a binary image in the LL and HH bands at the second level of Discrete Wavelet Transform (DWT) decomposition. Our generalization includes all four bands (LL, HL, LH, and HH), and a comparison of embedding a watermark at first and second level decompositions. We tested the proposed algorithm against fifteen attacks. Embedding the watermark in lower frequencies is robust to a group of attacks, and embedding the watermark in higher frequencies is robust to another set of attacks. Only for rewatermarking and collusion attacks, the watermarks extracted from all four bands are identical. Our experiments indicate that first level decomposition appear advantageous for two reasons: The area for watermark embedding is maximized, and the extracted watermarks are more textured with better visual quality.

Digital image-processing technique is used in image super-resolution for a variety of applications. In this paper, we propose hybrid domain feature-based image super-resolution using a fusion of Average Pixel Values Technique (APVT) and Discrete Wavelet Transform (DWT). The low-resolution (LR) images are considered and converted into high-resolution (HR) images using a novel technique of APTV by inserting an average of rows between rows and average of columns between columns to get HR images. The DWT is applied on HR images to obtain four bands. The HR images are downsampled, and to enhance image quality, histogram equalization (HE) is utilized. The LL band and HE matrix are added to obtain new LL band. The inverse DWT is applied on four bands to derive SR image. It is observed that the performance of the proposed method is better than existing methods.

The worldwide increasing applications of nonlinear loads, mostly consisting of power electronics devices, have made the power quality problems an important concern than ever before for utilities and consumers. Therefore, detection and classification of Power Quality disturbances is highly desirable. Most power quality disturbances are non-stationary and transitory and their detection and classification have proved to be much needed. Hence there is a requirement of advanced tools and techniques for the analysis of Power Quality disturbances. In this work detection and classification of various types of transients and interruptions, caused due to severe fault and reclosing of circuit breaker, is done by using Discrete Wavelet Transform and two-layer feed forward neural network. This work presents new approach aimed at automating the analysis of power quality disturbances including transients, interruption and normal waveform. The disturbance current waveform can be obtained from the disturbance generation model. The Discrete Wavelet Transform is chosen for feature extraction. Feature extraction outputs are the coefficients (detailed and approximate) of Discrete Wavelet Transform represents the power quality disturbance signal at the different levels in time and frequency domain.

Integration of wavelet transform, Local Binary Patterns and moments for content-based image retrieval

In this paper a novel and simple approach for detection and classification of wide variety range of power quality(PQ) events based on discrete wavelet transform (DWT) and correlation coefficient is presented. For this purpose, two new indices is proposed and by comparing the values of the correlation coefficient between the value of these indices for pre-stored PQ events and for a recorded indistinct signal, type of PQ events will be detected. This algorithm has advantages of DWT and correlation coefficient which, it does not have disadvantage of neural network or neural network-fuzzy based algorithms such as; training, and high dimension input matrices nor it does not have disadvantage of Fourier transform based approach such as unsuitability for non-stationary signal as it does not track signal dynamics properly due to limitation of fixed window width. The effectiveness of this method has been tested using numerous PQ disturbance and simulation results confirm the competency and the ability of the proposed method in PQ disturbances detection and automatic diagnosis. Compared with the other methods, the simulation under different noises conditions, verify the effectiveness of noise immunity, and relatively better accuracy of the proposed method.

Purpose: Alzheimer's Disease (AD) is in the dementia group and is one of the most prevalent neurodegenerative disorders. Approximately 50 million people were affected in 2018, and that number is expected to triple by 2050. Several demographic properties, neuroimaging such as MRI, functional MRI (MRI), neuropsychiatric symptoms, and cognitive abilities are used to predict AD. Between existing characteristics, White Matter (WM) is a known marker for AD tracking, and WM segmentation in MRI based on clustering can be used to decrease the volume of data. Many algorithms have been developed to predict AD, but most concentrate on the distinction of AD from Cognitive Normal (CN), and fewer on the distinction of AD from Mild Cognitive Impairment (MCI), which has an important position in AD progression. In addition, there are not efficient algorithms with low computational costs and sufficient features in clinical use.
 In this study, we provided a new, simple, and efficient methodology for classifying patients into AD and MCI patients and evaluated the effect of the view dimension of Fuzzy C means (FCM) in prediction with ensemble classifiers. This work was based on the segmentation of WM and extracting two groups of features.
 Materials and Methods: We proposed our methodology in three steps; first, segmentation of WM from T1 MRI with FCM according to two specific viewpoints (3D and 2D). In the second step, two groups of features are extracted: approximate coefficients of Discrete Wavelet Transform (DWT) with three-level decomposition and statistical (mean, variance, skewness) features. In the final step, an ensemble classifier that is constructed with three simple classifiers, K-Nearest Neighbor (KNN), Decision Tree (DT), and Linear Discriminant Analysis (LDA), was used to distinguish MCI from AD.
 Results: The proposed method has been evaluated by using 1,280 slices (samples) from 64 patients with MCI (32) and AD (32) of the ADNI dataset. The best performance is for the 3D viewpoint, and the accuracy, precision, and f1-score achieved from the methodology are 94.22%, 94.45%, and 94.21%, respectively, by using a ten-fold Cross-Validation (CV) strategy.
 Conclusion: The experimental evaluation shows that WM segmentation increases the performance of the ensemble classifier; moreover, the 3D view FCM is better than the 2D view. According to the results, the proposed methodology has comparable performance for the detection of MCI from AD. The low computational cost algorithm and the three classifiers for generalization can be used in practical application by physicians in pre-clinical.

일반적인 워터마킹 기술들은 영상의 특성을 고려하지 않은 채 신체 책인 영상에 워터마크를 삽입하는 경우가 많다 본 논문에서는 워터마킹 알고리즘을 웨이브렛을 기반으로 구현을 하였으며, 견고한 워터마크 삽입을 위해 Multi-Threshold Watermark Coding(MTWC)의 원리에 기초하여 Region of Interest(ROI)라고 불리우는 영상의 영역에 큰 계수를 찾아 워터마크를 삽입하게 된다. 이때 Human Visual System(HVS)을 이용하여 견고성과 비가시성의 향상을 도모하였다. 워터마크 삽입대역은 중주파 대역에 삽입을 하게 되는데 중주파 대역에 삽입한 워터마크는 어떠한 영상 처리과정 후에도 높은 비율의 추출을 보였으며, 워터마크가 삽입된 영상의 왜곡정도도 다른 대역보다 상대적으로 적게 나타나므로 중주파 대역에 워터마크를 삽입하였다. 워터마크는 의사 랜덤 시퀀스(Pseudo Random Sequence)로 구성되어 있고 워터마크의 검출은 Cox의 유사도 측정식을 이용하여 워터마크의 삽입여부를 판단한다 General watermarking techniques tend not to consider intrinsic characteristics of image, so that watermarks are embeded to entire images. In this paper, we present a watermarking algorithm based on wavelet domain, and the watermark is embedded into large coefficients in region of interest(ROI) being based on principle of multi-threshold watermark coding(MTWC) for robust watermark insertion. We try to accomplish both image duality and robustness using human visual system(HVS). The watermarks are embedded in middle frequency bands because the distortion degree of watermarked images appears to be less than lower frequency bands, and the embedded watermarks in the middle bands showed high extraction ratios after some distortion. The watermarks are consisted of pseudo random sequences and detected using Cox's similarity mesurement.

Content based image retrieval is grievous need of present scenario in digital imaging world. This work presents a new multi-scale content based image retrieval system which leverages the multi-resolution property of discrete wavelet transform (DWT) and the local information attribute of local extrema patterns (LEPs). Two level DWT is applied on images and wavelet coefficients are obtained for images, further the LEPs are collected from wavelet coefficients to extract the feature vector. The proposed method abbreviated as DWT+LEP and tested on two benchmark databases for validation and compared with local extrema patterns (LEPs), discrete wavelet transform (DWT), center-symmetric local binary pattern (CS LBP), local edge pattern for image retrieval (LEPINV), local edge pattern for segmentation (LEPSEG), local binary pattern (LBP) and block based local binary pattern (BLK LBP).

The digital world demands the transmission and storage of high quality video for streaming and broadcasting applications, the constraints are the network bandwidth and the memory of devices for the various multimedia and scientific applications, the video consists of spatial and temporal redundancies. The objective of any video compression algorithm is to eliminate the redundant information from the video signal during compression for effective transmission and storage. The correlation between the successive frames has not been exploited enough by the current compression algorithms. In this paper, a novel method for video compression is presented. The proposed model, applies the transformation on set of group of pictures (GOP). The high spatial correlation is achieved from the spatial and temporal redundancy of GOP by accordion representation and this helps to bypass the computationally demanding motion compensation step. The core idea of the proposed technique is to apply Tucker Decomposition (TD) on the Discrete Wavelet Transform (DWT) coefficients of the Accordion model of the GOP. We use DWT to separate the video in to different sub-images and TD to efficiently compact the energy of sub-images. The blocking artifacts will be considerably eliminated as the block size is huge. The proposed method attempts to reduce the spatial and temporal redundancies of the video signal to improve the compression ratio, computation time, and PSNR. The experimental results prove that the proposed method is efficient especially in high bit rate and with slow motion videos.

Face recognition is a significant biometric credential in the field of security authentication. It additionally assumes a noteworthy job in image processing and it is applicable in various systems like verifying the identity of the person and in security purpose. Recognizing the face with varying background, poses and illumination are the complexity involved in this face recognition. Many algorithms exist for face recognition, of which, Discrete Wavelet Transform (DWT) with Principal Component Analysis (PCA) works better for recognition of faces. An algorithm using 3 Level-DWT and modified PCA is proposed for feature extraction. The PCA and reconstruction of images using Inverse PCA, help not only for dimensionality reduction, but also to find the least principal components (PC) of an image from which the significant features of a face image can be extracted. The significant features thus extracted are used for classifying genetic and non-genetic faces. Using extracted features from 3 level DWT and PCA, Support vector machine (SVM) is utilized to classify the faces genetically. The proposed extracted features does not intend to certain features like ears, nose and eyes of the face, but corresponds to identify the faces which are genetically similar. Based on the statistical measure analysis, the proposed algorithm 3 Level dwt with modified PCA works well in extracting the features for identifying the faces which are genetically closer. This face recognition application system can be effectively used to treat a patient in other location with complete security. There is no chance for data stealing, since the concerned doctors and patient only will take part in the system. The identification of genetic faces will turn out to be an achievement in the field of health care monitoring systems.

This paper deals with the application of Spatial and Temporal DWT (Discrete Wavelet transform) on the videos. Here we will discuss about three mechanisms and their performance on videos at increased DWT level. In any video processing algorithm, memory is the major criteria. In these three mechanisms dynamic (automatic) DWT level selection and manual level selection is implemented. Here we will also discuss about implementation of different DWT level in spatial and temporal domain. In this paper Haar wavelet is taken as the reference as it has its inherent properties and ease of implementation.

Face recognition in biometrics is a difficult process because of factors including individual differences in facial emotions, illumination, resolution, facial rotation, and facial structure similarities. "A Combination of Cross Domain Features for Face Recognition" is what we suggest here. The benchmarked face images are considered, downsized to consistent 128x128 dimensions, and converted to grayscale. Face photos are compressed, and their quality is improved by using the Discrete Wavelet Transform (DWT). Initial characteristics that are unaffected by changes in an image's lighting are extracted using the Histogram of Oriented Gradients (HOG) on a compressed LL band of DWT. By connecting the LL band's output to the HOG's input, the DWT and HOG's LL bands are connected in a cascade. HOG's input and output are combined to create new, strong final features. The system's results when comparing test and database features are calculated using the Euclidean Distance (ED). It has been noted that the suggested system outperforms the current ones in terms of results.

Assuring medical images protection and robustness is a compulsory necessity nowadays. In this paper, a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform (DWT) with the energy compaction of the Discrete Wavelet Transform (DCT). The multi-level Encryption-based Hybrid Fusion Technique (EbhFT) aims to achieve great advances in terms of imperceptibility and security of medical images. A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT transform. Afterwards, a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the watermark. Lastly, a PN-sequence key is formed along with a supplementary key in the third layer of the EbHFT. Thus, the watermarked image is generated by enclosing both keys into DWT and DCT coefficients. The fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT methods. In order to validate the proposed technique, a standard dataset of medical images is used. Simulation results show higher performance of the visual quality (i.e., 57.65) for the watermarked forms of all types of medical images. In addition, EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation (NC). Finally, extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.