Showing papers on "Discrete cosine transform published in 2017"
TL;DR: D discrete orthogonal stockwell transform using discrete cosine transform is presented for efficient representation of the ECG signal in time–frequency space and particle swarm optimization technique is employed for gradually tuning the learning parameters of the SVM classifier.
Abstract: Signal processing techniques are an obvious choice for real-time analysis of electrocardiography (ECG) signals. However, classical signal processing techniques are unable to deal with the nonstationary nature of the ECG signal. In this context, this paper presents a new approach, i.e., discrete orthogonal stockwell transform using discrete cosine transform for efficient representation of the ECG signal in time–frequency space. These time–frequency features are further reduced in lower dimensional space using principal component analysis, representing the morphological characteristics of the ECG signal. In addition, the dynamic features (i.e., RR-interval information) are computed and concatenated to the morphological features to constitute the final feature set, which is utilized to classify the ECG signals using support vector machine (SVM). In order to improve the classification performance, particle swarm optimization technique is employed for gradually tuning the learning parameters of the SVM classifier. In this paper, ECG data exhibiting 16 classes of the most frequently occurring arrhythmic events are taken from the benchmark MIT-BIH arrhythmia database for the validation of the proposed methodology. The experimental results yielded an improved overall accuracy, sensitivity (Sp), and positive predictivity (Pp) of 98.82% in comparison with the existing approaches available in the literature.
188 citations
TL;DR: Results show that the raised method has better performance, compared with the state-of-the-art automated heartbeat classification systems.
175 citations
TL;DR: Comparison results viz-a - viz payload and robustness show that the proposed techniques perform better than some existing state of art techniques and could be useful for e-healthcare systems.
Abstract: Electronic transmission of the medical images is one of the primary requirements in a typical Electronic-Healthcare (E-Healthcare) system. However this transmission could be liable to hackers who may modify the whole medical image or only a part of it during transit. To guarantee the integrity of a medical image, digital watermarking is being used. This paper presents two different watermarking algorithms for medical images in transform domain. In first technique, a digital watermark and Electronic Patients Record (EPR) have been embedded in both regions; Region of Interest (ROI) and Region of Non-Interest (RONI). In second technique, Region of Interest (ROI) is kept untouched for tele-diagnosis purpose and Region of Non-Interest (RONI) is used to hide the digital watermark and EPR. In either algorithm 8ź×ź8 block based Discrete Cosine Transform (DCT) has been used. In each 8ź×ź8 block two DCT coefficients are selected and their magnitudes are compared for embedding the watermark/EPR. The selected coefficients are modified by using a threshold for embedding bit a `0' or bit `1' of the watermark/EPR. The proposed techniques have been found robust not only to singular attacks but also to hybrid attacks. Comparison results viz-a - viz payload and robustness show that the proposed techniques perform better than some existing state of art techniques. As such the proposed algorithms could be useful for e-healthcare systems.
155 citations
TL;DR: The method has been extensively tested and analyzed against known attacks and is found to be giving superior performance for robustness, capacity and reduced storage and bandwidth requirements compared to reported techniques suggested by other authors.
Abstract: This paper presents a new robust hybrid multiple watermarking technique using fusion of discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and singular value decomposition (SVD) instead of applying DWT, DCT and SVD individually or combination of DWT-SVD / DCT-SVD. For identity authentication purposes, multiple watermarks are embedded into the same medical image / multimedia objects simultaneously, which provides extra level of security with acceptable performance in terms of robustness and imperceptibility. In the embedding process, the cover image is decomposed into first level discrete wavelet transforms where the A (approximation/lower frequency sub-band) is transformed by DCT and SVD. The watermark image is also transformed by DWT, DCT and SVD. The S vector of watermark information is embedded in the S component of the cover image. The watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DCT and inverse DWT. The watermark is extracted using an extraction algorithm. Furthermore, the text watermark is embedding at the second level of the D (diagonal sub-band) of the cover image. The security of the text watermark considered as EPR (Electronic Patient Record) data is enhanced by using encryption method before embedding into the cover. The results are obtained by varying the gain factor, size of the text watermark, and cover medical images. The method has been extensively tested and analyzed against known attacks and is found to be giving superior performance for robustness, capacity and reduced storage and bandwidth requirements compared to reported techniques suggested by other authors.
142 citations
TL;DR: Experimental results show that the proposed scheme can cluster the inter-block embedding changes and perform better than the state-of-the-art steganographic method.
133 citations
TL;DR: A novel classification technique for large data set of mammograms using a deep learning method that targets a three-class classification study (normal, malignant, and benign cases).
Abstract: In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.
133 citations
TL;DR: A color multiple watermarking method based on DCT (Discrete Cosine Transform) and repetition code is proposed and simulated to protect the copyright ownership and validate the authenticity of multiple owners.
Abstract: Multiple watermarking based techniques are receiving more attention in recent times for its wide variety of applications in different fields. To protect the copyright ownership and validate the authenticity of multiple owners, in this paper a color multiple watermarking method based on DCT (Discrete Cosine Transform) and repetition code is proposed and simulated. Initially, green and blue components of color host image are selected for inserting multiple watermarks. Then, each green and blue component of the image is decomposed into non overlapping blocks and subsequently DCT is employed on each block. In this technique, a binary bit of watermark is embedded into green/blue component’s transformed block by modifying some middle significant AC coefficients using repetition code. During multiple watermarks embedding in green and blue components of the proposed method, DC and some higher AC coefficients are kept intact after zigzag scanning of each DCT block to ensure the imperceptibility of the watermarked host image. The proposed scheme is experimented to establish the validity by extracting adequate multiple watermark data from the restructured cover image after applying common geometric transformation attacks (like rotation, cropping, scaling and deletion of lines/columns etc.), common enhancement technique attacks (like lowpass filtering, histogram equalization, sharpening, gamma correction, noise addition etc.) and JPEG compression attacks.
124 citations
TL;DR: The experimental results demonstrate that the proposed DWT-SVD and DCT with Arnold Cat Map encryption based robust and blind watermarking scheme is robust, imperceptible and secure to several attacks and common signal processing operations.
Abstract: In this article, a new DWT-SVD and DCT with Arnold Cat Map encryption based robust and blind watermarking scheme is proposed for copyright protection. The proposed scheme solves the most frequently occurring watermarking security problems in Singular Value Decomposition (SVD) based schemes which are unauthorized reading and false-positive detection. This scheme also optimizes fidelity and robustness characteristics. The grey image watermark splits into two parts using four bits MSBs and four bits LSBs of each pixel. Discrete Cosine Transform (DCT) coefficients of these MSBs and LSBs values are embedded into the middle singular value of each block having size 4 × 4 of the host image's one level Discrete Wavelet Transform (DWT) sub-bands. The reason for incorporating Arnold Cat Map in the proposed scheme is to encode the watermark image before embedding it in the host image. The proposed scheme is a blind scheme and does not require the choice of scaling factor. Thus, the proposed scheme is secure as well as free from the false positive detection problem. The proposed watermarking scheme is tested for various malicious and non-malicious attacks. The experimental results demonstrate that the scheme is robust, imperceptible and secure to several attacks and common signal processing operations.
121 citations
TL;DR: The authors' develop a digital watermarking algorithm based on a fractal encoding method and the discrete cosine transform (DCT) method that has higher performance characteristics such as robustness and peak signal to noise ratio than classical methods.
Abstract: With the rapid development of computer science, problems with digital products piracy and copyright dispute become more serious; therefore, it is an urgent task to find solutions for these problems. In this study, the authors' develop a digital watermarking algorithm based on a fractal encoding method and the discrete cosine transform (DCT). The proposed method combines fractal encoding method and DCT method for double encryptions to improve traditional DCT method. The image is encoded by fractal encoding as the first encryption, and then encoded parameters are used in DCT method as the second encryption. First, the fractal encoding method is adopted to encode a private image with private scales. Encoding parameters are applied as digital watermarking. Then, digital watermarking is added to the original image to reversibly using DCT, which means the authors can extract the private image from the carrier image with private encoding scales. Finally, attacking experiments are carried out on the carrier image by using several attacking methods. Experimental results show that the presented method has higher performance characteristics such as robustness and peak signal to noise ratio than classical methods.
114 citations
TL;DR: Li et al. as discussed by the authors proposed a graph-signal smoothness prior (LERaG) based on the left eigenvectors of the random walk graph Laplacian matrix, which has desirable image filtering properties with low computation overhead.
Abstract: Given the prevalence of joint photographic experts group (JPEG) compressed images, optimizing image reconstruction from the compressed format remains an important problem. Instead of simply reconstructing a pixel block from the centers of indexed discrete cosine transform (DCT) coefficient quantization bins (hard decoding), soft decoding reconstructs a block by selecting appropriate coefficient values within the indexed bins with the help of signal priors. The challenge thus lies in how to define suitable priors and apply them effectively. In this paper, we combine three image priors—Laplacian prior for DCT coefficients, sparsity prior, and graph-signal smoothness prior for image patches—to construct an efficient JPEG soft decoding algorithm. Specifically, we first use the Laplacian prior to compute a minimum mean square error initial solution for each code block. Next, we show that while the sparsity prior can reduce block artifacts, limiting the size of the overcomplete dictionary (to lower computation) would lead to poor recovery of high DCT frequencies. To alleviate this problem, we design a new graph-signal smoothness prior (desired signal has mainly low graph frequencies) based on the left eigenvectors of the random walk graph Laplacian matrix (LERaG). Compared with the previous graph-signal smoothness priors, LERaG has desirable image filtering properties with low computation overhead. We demonstrate how LERaG can facilitate recovery of high DCT frequencies of a piecewise smooth signal via an interpretation of low graph frequency components as relaxed solutions to normalized cut in spectral clustering. Finally, we construct a soft decoding algorithm using the three signal priors with appropriate prior weights. Experimental results show that our proposal outperforms the state-of-the-art soft decoding algorithms in both objective and subjective evaluations noticeably.
114 citations
TL;DR: Experiments demonstrate that the proposed image-deblocking algorithm combining SSR and QC outperforms the current state-of-the-art methods in both peak signal-to-noise ratio and visual perception.
Abstract: The block discrete cosine transform (BDCT) has been widely used in current image and video coding standards, owing to its good energy compaction and decorrelation properties. However, because of independent quantization of DCT coefficients in each block, BDCT usually gives rise to visually annoying blocking compression artifacts, especially at low bit rates. In this paper, to reduce blocking artifacts and obtain high-quality images, image deblocking is cast as an optimization problem within maximum a posteriori framework, and a novel algorithm for image deblocking by using structural sparse representation (SSR) prior and quantization constraint (QC) prior is proposed. The SSR prior is utilized to simultaneously enforce the intrinsic local sparsity and the nonlocal self-similarity of natural images, while QC is explicitly incorporated to ensure a more reliable and robust estimation. A new split Bregman iteration-based method with an adaptively adjusted regularization parameter is developed to solve the proposed optimization problem, which makes the entire algorithm more practical. Experiments demonstrate that the proposed image-deblocking algorithm combining SSR and QC outperforms the current state-of-the-art methods in both peak signal-to-noise ratio and visual perception.
TL;DR: A novel passive image forgery detection method is proposed based on local binary pattern (LBP) and discrete cosine transform (DCT) to detect copy–move and splicing forgeries.
Abstract: With the development of easy-to-use and sophisticated image editing software, the alteration of the contents of digital images has become very easy to do and hard to detect. A digital image is a very rich source of information and can capture any event perfectly, but because of this reason, its authenticity is questionable. In this paper, a novel passive image forgery detection method is proposed based on local binary pattern (LBP) and discrete cosine transform (DCT) to detect copy–move and splicing forgeries. First, from the chrominance component of the input image, discriminative localized features are extracted by applying 2D DCT in LBP space. Then, support vector machine is used for detection. Experiments carried out on three image forgery benchmark datasets demonstrate the superiority of the method over recent methods in terms of detection accuracy.
TL;DR: The experimental results demonstrate that the proposed DCT based quantization and Discrete Cosine Transform based self-embedding fragile watermarking scheme not only outperforms high quality restoration effectively, but also removes the blocking artifacts and improves the accuracy of tamper localization due to use of very small size blocks.
Abstract: Due to rapid development of Internet and computer technology, image authentication and restoration are very essential, especially when it is utilized in forensic science, medical imaging and evidence of court. A quantization and Discrete Cosine Transform(DCT) based self-embedding fragile watermarking scheme with effective image authentication and restoration quality is proposed in this paper. In this scheme, the cover image is divided in size of 2×2 non-overlapping blocks. For each block twelve bits watermark are generated from the five most significant bits (MSBs) of each pixel and are embedded into the three least significant bits (LSBs) of the pixels corresponding to the mapped block. The proposed scheme uses two levels encoding for content restoration bits generation. The restoration is achievable with high PSNR and NCC up to 50 % tampering rate. The experimental results demonstrate that the proposed scheme not only outperforms high quality restoration effectively, but also removes the blocking artifacts and improves the accuracy of tamper localization due to use of very small size blocks.
TL;DR: The robustness of the scheme is better than an existing scheme for a similar set of medical images in terms of Normalized Correlation (NC), and experimental results show that scheme is robust to geometric attacks, signal processing attacks and JPEG compression attacks.
TL;DR: A robust and secure video steganographic algorithm in discrete wavelet transform (DWT) and discrete cosine Transform (DCT) domains based on the multiple object tracking (MOT) algorithm and error correcting codes is proposed.
Abstract: Over the past few decades, the art of secretly embedding and communicating digital data has gained enormous attention because of the technological development in both digital contents and communication. The imperceptibility, hiding capacity, and robustness against attacks are three main requirements that any video steganography method should take into consideration. In this paper, a robust and secure video steganographic algorithm in discrete wavelet transform (DWT) and discrete cosine transform (DCT) domains based on the multiple object tracking (MOT) algorithm and error correcting codes is proposed. The secret message is preprocessed by applying both Hamming and Bose, Chaudhuri, and Hocquenghem codes for encoding the secret data. First, motion-based MOT algorithm is implemented on host videos to distinguish the regions of interest in the moving objects. Then, the data hiding process is performed by concealing the secret message into the DWT and DCT coefficients of all motion regions in the video depending on foreground masks. Our experimental results illustrate that the suggested algorithm not only improves the embedding capacity and imperceptibility but also enhances its security and robustness by encoding the secret message and withstanding against various attacks.
TL;DR: Experimental results with various maritime images demonstrate that the proposed ship detection algorithm outperforms the traditional techniques in terms of both detection accuracy and real-time performance, especially for complex sea-surface background with large waves.
TL;DR: The enhancement of the unprecedented lesser quality of electrocardiogram signals through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering was able to render ensemble heartbeats of significantly higher quality.
Abstract: Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method's performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.
TL;DR: The goal of this paper is to propose an accurate method for estimating quantization steps from an image that has been previously JPEG-compressed and stored in lossless format based on the combination of the quantization effect and the statistics of discrete cosine transform (DCT) coefficient.
Abstract: The goal of this paper is to propose an accurate method for estimating quantization steps from an image that has been previously JPEG-compressed and stored in lossless format. The method is based on the combination of the quantization effect and the statistics of discrete cosine transform (DCT) coefficient characterized by the statistical model that has been proposed in our previous works. The analysis of quantization effect is performed within a mathematical framework, which justifies the relation of local maxima of the number of integer quantized forward coefficients with the true quantization step. From the candidate set of the true quantization step given by the previous analysis, the statistical model of DCT coefficients is used to provide the optimal quantization step candidate. The proposed method can also be exploited to estimate the secondary quantization table in a double-JPEG compressed image stored in lossless format and detect the presence of JPEG compression. Numerical experiments on large image databases with different image sizes and quality factors highlight the high accuracy of the proposed method.
TL;DR: In this paper, a CAD tool is built and integrated into a standard digital flow to offer a wide range of cost-accuracy tradeoffs for any conventional design, including area, power, and delay savings.
Abstract: Energy-efficiency is a critical concern for many systems, ranging from Internet of things objects and mobile devices to high-performance computers. Moreover, after 40 years of prosperity, Moore’s law is starting to show its economic and technical limits. Noticing that many circuits are over-engineered and that many applications are error-resilient or require less precision than offered by the existing hardware, approximate computing has emerged as a potential solution to pursue improvements of digital circuits. In this regard, a technique to systematically tradeoff accuracy in exchange for area, power, and delay savings in digital circuits is proposed: gate-level pruning (GLP). A CAD tool is build and integrated into a standard digital flow to offer a wide range of cost-accuracy tradeoffs for any conventional design. The methodology is first demonstrated on adders, achieving up to 78% energy-delay-area reduction for 10% mean relative error. It is then detailed how this methodology can be applied on a more complex system composed of a multitude of arithmetic blocks and memory: the discrete cosine transform (DCT), which is a key building block for image and video processing applications. Even though arithmetic circuits represent less than 4% of the entire DCT area, it is shown that the GLP technique can lead to 21% energy-delay-area savings over the entire system for a reasonable image quality loss of 24 dB. This significant saving is achieved thanks to the pruned arithmetic circuits, which sets some nodes at constant values, enabling the synthesis tool to further simplify the circuit and memory.
TL;DR: This computational imaging technique can retrieve an image from sub-Nyquist measurements, and the background noise is easily cancelled to give excellent image quality.
Abstract: We propose and demonstrate a computational imaging technique that uses structured illumination based on a two-dimensional discrete cosine transform to perform imaging with a single-pixel detector. A scene is illuminated by a projector with two sets of orthogonal patterns, then by applying an inverse cosine transform to the spectra obtained from the single-pixel detector a full-colour image is retrieved. This technique can retrieve an image from sub-Nyquist measurements, and the background noise is easily cancelled to give excellent image quality. Moreover, the experimental set-up is very simple.
TL;DR: A forgery detection method that first reduces the features via discrete wavelet transform (DWT) to give an approximate image from the lowest energy sub-band and a mask-based tampering method is developed as part of the experiments in order to test the detection method.
TL;DR: The hybrid method is suitable for avoidance of the patient identity theft/alteration/modification and secure medical document dissemination over the open channel for medical applications and is robust for hidden watermark at acceptable quality of the watermarked image.
Abstract: This paper presents a robust and secure region of interest and non-region of interest based watermarking method for medical images. The proposed method applies the combination of discrete wavelet transform and discrete cosine transforms on the cover medical image for the embedding of image and electronic patient records (EPR) watermark simultaneously. The embedding of multiple watermarks at the same time provides extra level of security and important for the patient identity verification purpose. Further, security of the image and EPR watermarks is enhancing by using message-digest (MD5) hash algorithm and Rivest---Shamir---Adleman respectively before embedding into the medical cover image. In addition, Hamming error correction code is applying on the encrypted EPR watermark to enhance the robustness and reduce the possibility bit error rates which may result into wrong diagnosis in medical environments. The robustness of the method is also extensively examined for known attacks such as salt & pepper, Gaussian, speckle, JPEG compression, filtering, histogram equalization. The method is found to be robust for hidden watermark at acceptable quality of the watermarked image. Therefore, the hybrid method is suitable for avoidance of the patient identity theft/alteration/modification and secure medical document dissemination over the open channel for medical applications.
TL;DR: An algorithm based on Markov in quaternion discrete cosine transform (QDCT) domain is proposed for image splicing detection which not only make use of color information of images, but also can yield considerably better detection performance compared with the state-of-the-artSplicing detection methods tested on the same dataset.
TL;DR: A digital image watermarking algorithm in YCoCg-R color space is proposed and the results show the superior robustness of the proposed method over the other algorithms with same capacity.
TL;DR: Experimental results show that robustness is achieved by recovering satisfactory watermark data from the reconstructed cover image after applying common geometric transformation attacks, common enhancement technique attacks (like lowpass filtering, histogram equalization, sharpening, gamma correction, noise addition etc.) and JPEG compression attacks.
Abstract: To compromise between imperceptibility and robustness property of robust image watermarking technique, a RDWT-DCT based blind image watermarking scheme using Arnold scrambling is presented in this paper. Firstly, RDWT (Redundant Discrete Wavelet Transform) is applied to each gray scale cover image block after the image is decomposed into fixed size non overlapping blocks. Secondly, the binary watermark logo is encrypted by Arnold chaotic map and reshaped to a sequence to improve the security of the logo. In the subsequent step, DCT (Discrete Cosine Transform) is employed on each LH subband of the non-overlapping host image block. Finally, after zigzag scanning of each DCT block a binary bit of watermark is embedded into each block by adjusting some middle significant AC coefficients using repetition code. Experimental results show that robustness is achieved by recovering satisfactory watermark data from the reconstructed cover image after applying common geometric transformation attacks (like rotation, cropping, scaling, shearing and deletion of lines or column operation etc.), common enhancement technique attacks (like lowpass filtering, histogram equalization, sharpening, gamma correction, noise addition etc.) and JPEG compression attacks. The proposed scheme is also tested to verify the robustness performance against standard benchmark software "Checkmark" and satisfactory results are achieved against the Checkmark attacks such as Hard and Soft Thresholding, Template Removal, Warping, Dithering, Remodulation and Downsampling/Upsampling etc.
14 Nov 2017
TL;DR: The proposed method is compared with other three algorithms to select the subset of features used eight UCI datasets and showed that the proposed method provided better results than other methods in terms of performance measures and statistical test.
Abstract: The feature selection is an important step to improve the performance of classifier through reducing the dimension of the dataset, so the time complexity and space complexity are reduced. There are several feature selection methods are used the swarm techniques to determine the suitable subset of features. The sine cosine algorithm (SCA) is one of the recent swarm techniques that used as global optimization method to solve the feature selection, however, it can be getting stuck in local optima. In order to solve this problem, the differential evolution operators are used as local search method which helps the SCA to skip the local point. The proposed method is compared with other three algorithms to select the subset of features used eight UCI datasets. The experiments results showed that the proposed method provided better results than other methods in terms of performance measures and statistical test.
TL;DR: This work proposes a new discrete cosine transform (DCT) approach for color image steganography and implements a global-adaptive-region (GAR) embedding scheme that allows for extremely high embedding capacities while maintaining enhanced perceptibility.
Abstract: An increasing number of spatial and frequency domain data hiding techniques have been proposed to address the relatively low embedding capacities of image-based steganography. These techniques have brought promise of higher embedding capacities, albeit at the expense of lower perceptibility. This work proposes a new discrete cosine transform (DCT) approach for color image steganography and implements a global-adaptive-region (GAR) embedding scheme that allows for extremely high embedding capacities while maintaining enhanced perceptibility. The idea is to adapt the variable region size, used to hide the data, in each DCT block of the cover image to the amount of correlation of the image values in the corresponding block. We will demonstrate how this new technique achieves enhanced hiding capacities and perceptibility compared to other spatial, Fourier, and adaptive-region DCT based steganography schemes.
TL;DR: The method achieves the best accuracy in the Jacknife test, from 79.20% to 86.20%, and the performance of independent test shows that the method has a certain ability to be effectively used for DNA-binding protein prediction.
Abstract: Since the importance of DNA-binding proteins in multiple biomolecular functions has been recognized, an increasing number of researchers are attempting to identify DNA-binding proteins In recent years, the machine learning methods have become more and more compelling in the case of protein sequence data soaring, because of their favorable speed and accuracy In this paper, we extract three features from the protein sequence, namely NMBAC (Normalized Moreau-Broto Autocorrelation), PSSM-DWT (Position-specific scoring matrix—Discrete Wavelet Transform), and PSSM-DCT (Position-specific scoring matrix—Discrete Cosine Transform) We also employ feature selection algorithm on these feature vectors Then, these features are fed into the training SVM (support vector machine) model as classifier to predict DNA-binding proteins Our method applys three datasets, namely PDB1075, PDB594 and PDB186, to evaluate the performance of our approach The PDB1075 and PDB594 datasets are employed for Jackknife test and the PDB186 dataset is used for the independent test Our method achieves the best accuracy in the Jacknife test, from 7920% to 8623% and 805% to 8620% on PDB1075 and PDB594 datasets, respectively In the independent test, the accuracy of our method comes to 763% The performance of independent test also shows that our method has a certain ability to be effectively used for DNA-binding protein prediction The data and source code are at https://doiorg/106084/m9figshare5104084
TL;DR: In this article, a combination of horizontal and vertical subbands of stationary wavelet transform is used as these subbands contain muscle movement information for majority of the facial expressions for recognition.
Abstract: Humans use facial expressions to convey personal feelings. Facial expressions need to be automatically recognized to design control and interactive applications. Feature extraction in an accurate manner is one of the key steps in automatic facial expression recognition system. Current frequency domain facial expression recognition systems have not fully utilized the facial elements and muscle movements for recognition. In this paper, stationary wavelet transform is used to extract features for facial expression recognition due to its good localization characteristics, in both spectral and spatial domains. More specifically a combination of horizontal and vertical subbands of stationary wavelet transform is used as these subbands contain muscle movement information for majority of the facial expressions. Feature dimensionality is further reduced by applying discrete cosine transform on these subbands. The selected features are then passed into feed forward neural network that is trained through back propagation algorithm. An average recognition rate of 98.83% and 96.61% is achieved for JAFFE and CK+ dataset, respectively. An accuracy of 94.28% is achieved for MS-Kinect dataset that is locally recorded. It has been observed that the proposed technique is very promising for facial expression recognition when compared to other state-of-the-art techniques.
TL;DR: A low-complexity 8-point orthogonal approximate discrete cosine transform (DCT) is introduced that could outperform the well-known signed DCT, as well as state-of-the-art algorithms.
Abstract: A low-complexity 8-point orthogonal approximate DCT is introduced. The proposed transform requires no multiplications or bit-shift operations. The derived fast algorithm requires only 14 additions, less than any existing DCT approximation. Moreover, in several image compression scenarios, the proposed transform could outperform the well-known signed DCT, as well as state-of-the-art algorithms.