Gaussian Low-pass Filter Research Articles

WITHDRAWN: Image Digest using Color Vector Angle and Dominant Walsh-Hadamard Transform Coefficients

Classical image digests often investigate the luminance plane (Y) in the YCbCr color standard to produce robust hash values. However, in most cases, the images are in RGB format. So digests employing luminance planes suffered from poor discrimination. The proposed work develops a robust image digest for RGB images, which exploits all R, G, and B planes of images to obtain intense discrimination of the digest. The proposed digest reprocesses images in the database to a fixed dimension. Later, it extracts dominant approximation components by using a Gaussian Low pass filter from each color component R, G and B. Thirdly, Color Vector Angle (CVA) is applied on merged three planes of the image. Fourthly Walsh-Hadamard transform (WHT) is used on 8 × 8 blocks. The dominant dc coefficients of WHT are taken for the hash generation. Finally, it takes the Hamming distances as a similarity measurement between the original and extracted hashes. The simulation results indicate that our proposed algorithm is superior to the classification efficiency among perceptual robustness and discriminating capability.

Application of Digital Image Based on Machine Learning in Media Art Design.

In digital media art, expressive force is an important art form of media. This paper studies digital images that have the same effect when applied to media art. The research object is media art images, and the application effect of the proposed algorithm is related to the media art images. The development of digital image technology has brought revolutionary changes to traditional media art expression techniques. In this paper, a partial-pixel interpolation technique based on convolutional neural network is proposed. Supervised training of convolutional neural networks requires predetermining the input and target output of the network, namely, integer image and fractional image in this paper. To solve the problem that the subpixel sample cannot be obtained, this paper first analyzes the imaging principle of digital image and proposes a subpixel sample generation algorithm based on Gaussian low-pass filter and polyphase sampling. From the perspective of rate distortion optimization, the purpose of pixel motion compensation is to improve the accuracy of interframe prediction. Therefore, this paper defines pixel motion compensation as an interframe regression problem, that is, the mapping process of the reference image integral pixel sample to the current image sample to be encoded. In this paper, a generalized partial-pixel interpolation model is proposed for bidirectional prediction. The partial-pixel interpolation of bidirectional prediction is regarded as a binary regression model; that is, the integral pixel reference block in two directions is mapped to the current block to be coded. It further studies how to apply the trained digital images to media art design more flexibly and efficiently.

Patch-Based Weld Defect Segmentation and Classification Using Anisotropic Diffusion Image Enhancement Combined with Support-Vector Machine

Several factors such as low image quality, limited human eye resolution, and biased interpretation make the manual radiography testing defect detection erroneous. The aim of this paper is to design a semi-automatic but accurate feature extraction and classification framework, using radiography images of welded joints represented in the GDXray image database. We have trained our support-vector machine classifier with crack, porosity and lack of penetration as the three more frequent classes of radiography defects. The images are segmented after binarization followed by a two-stage image enhancement technique. The body of the two-stage method is made up of an anisotropic diffusion Gaussian filtering, morphological edge detection, and low-pass Gaussian filtering. A three-class support-vector machine based on One-vs.-All implementation of the binary support-vector machine is created, trained, and tested. The method also involves manual adjustments which makes it semi-automatic, and compared to other studies, it is proved to work best with GDXray, a freely available comprehensive radiography image database. The effect of incorporating different optimization algorithms for solving the inherent optimization problem in support-vector machine theory, and utilizing various image processing techniques on defect detection is studied. We show that the combination of image processing and support-vector machine would result in a better performance than the previous studies using the same database.

Measuring clock jitter and data signals for bit error detection in optical transceiver systems

AbstractThis work demonstrates how to measure jitter using the eye diagram Analyzer histogram feature. Bit sequence generator is employed with the NRZ Pulse generator with the electrical jitter in order to measure the signal amplitude level. We have modulated the light signal from CW Laser with the electrical signal by Mach-Zehnder Modulator through 5 km fiber cables. The signal can be amplified with a gain of 7 dB with the presence of transimpedence amplifier. The combination of electrical signal and signal from noise source can be filtered by using the low pass Gaussian filter.

The influence of electrical filters with sequence generators on optical ISL performance evolution with suitable data rates

Abstract The work offered the analysis comparison study of optical wireless intersatellite link (ISL) for both different bit sequence generators and filters. The study clarifies the impact of varying sorts of both bit sequence generator (data source) in the transmitter and electrical filters in receiver on system performance. The two bit sequence generators used are pseudo random bit sequence generator (PRBSG) and user-defined bit sequence generator (UDBSG). The used filters in this study are low pass (LP) Bessel filter, LP Gaussian filter, LPCROF (cosine roll-off filter), and LPSCROF (squared CROF). Performance of the filter depends on the order of filter and filter bandwidth. The performance parameters in our study are quality factor, Bit Error Rate (BER), and optical received power. In this study, variation of optical received power depends on the type of sequence generator because it is measured after the channel directly.

The Impact of Quality of Digital Elevation Models on the Result of Landslide Susceptibility Modeling Using the Method of Weights of Evidence

The paper discusses the impact that the quality of the digital elevation model (DEM) has on the final result of landslide susceptibility modeling (LSM). The landslide map was developed on the basis of the analysis of archival geological maps and the Light Detection and Ranging (LiDAR) digital elevation model. In addition, complementary field studies were conducted. In total, 92 landslides were inventoried and their degree of activity was assessed. An inventory of the landslides was prepared using a 1-m-LiDAR DEM and field research. Two digital photogrammetric elevation models with an elevation pixel resolution of 20 m were used for landslide susceptibility modeling. The first digital elevation model was obtained from a LiDAR point cloud (DEM–airborne laser scanning (ALS)), while the second model was developed based on archival digital stereo-pair aerial images (DEM–Land Parcel Identification System (LPIS)). Both models were subjected to filtration using a Gaussian low-pass filter to reduce errors in their elevation relief. Then, using ArcGIS software, a differential model was generated to illustrate the differences in morphology between the models. The maximum differences in topographic elevations between the DEM–ALS and DEM–LPIS models were calculated. The Weights-of-Evidence model is a geostatistical method used for the landslide susceptibility modeling. Six passive factors were employed in the process of susceptibility generation: elevation, slope gradient, exposure, topographic roughness index (TRI), distance from tectonic lines, and distance from streams. As a result, two landslide susceptibility maps (LSM) were obtained. The accuracy of the landslide susceptibility models was assessed based on the Receiver Operating Characteristic (ROC) curve index. The area under curve (AUC) values obtained from the ROC curve indicate that the accuracy of classification for the LSM–DEM–ALS model was 78%, and for the LSM–LPIS–DEM model was 73%.

Enhancing underwater image via color correction and Bi-interval contrast enhancement

Underwater captured images often suffer from color cast and low visibility due to light is scattered and absorbed while it traveling in water. In this paper, we proposed a novel method of color correction and Bi-interval contrast enhancement to improve the quality of underwater images. Firstly, a simple and effective color correction method based on sub-interval linear transformation is employed to address color distortion. Then, a Gaussian low-pass filter is applied to the L channel to decompose the low- and high-frequency components. Finally, the low- and high-frequency components are enhanced by Bi-interval histogram based on optimal equalization threshold strategy and S-shaped function to enhancement image contrast and highlight image details. Inspired by the multi-scale fusion, we employed a simple linear fusion to integrate the enhanced high- and low-frequency components. Comparison with state-of-the-art methods show that the proposed method outputs high-quality underwater images with qualitative and quantitative evaluation well.

Multi-Visual Feature Saliency Detection for Sea-Surface Targets through Improved Sea-Sky-Line Detection

To visually detect sea-surface targets, the objects of interest must be effectively and rapidly isolated from the background of sea-surface images. In contrast to traditional image detection methods, which employ a single visual feature, this paper proposes a significance detection algorithm based on the fusion of multi-visual features after detecting the sea-sky-lines. The gradient edges of the sea-surface images are enhanced using a Gaussian low-pass filter to eliminate the effect of the image gradients pertaining to the clouds, wave points, and illumination. The potential region and points of the sea-sky-line are identified. The sea-sky-line is fitted through polynomial iterations to obtain a sea-surface image containing the target object. The saliency subgraphs of the high and low frequency, gradient texture, luminance, and color antagonism features are fused to obtain an integrated saliency map of the sea-surface image. The saliency target area of the sea surface is segmented. The effectiveness of the proposed method was verified. The average detection rate and time for the sea-sky-line detection were 96.3% and 1.05 fps, respectively. The proposed method outperformed the existing saliency models on the marine obstacle detection dataset and Singapore maritime dataset, with mean absolute errors of 0.075 and 0.051, respectively.

Robust Filtering Options for Higher-Order Strain Fields Generated by Digital Image Correlation

Digital image correlation (DIC) systems have been used in many engineering fields to obtain surface full-field strain distribution. However, noise affects the accuracy and precision of the measurements due to many factors. The aim of this study was to find out how different filtering options; namely, simple mean filtering, Gaussian mean filtering and Gaussian low-pass filtering (LPF), reduce noise while maintaining the full-field information based on constant, linear and quadratic strain fields. Investigations are done in two steps. First, linear and quadratic strain fields with and without noise are simulated and projected to discrete measurement points which build up strain window sizes consisting of 6×5, 12×11, and 26×17 points. Optimal filter sizes are computed for each filter strategy, strain field type, and strain windows size, with minimal impairment of the signal information. Second, these filter sizes are used to filter full-field strain distributions of steel samples under tensile tests by using an ARAMIS DIC system to show their practical applicability. Results for the first part show that for a typical 12×11 strain window, simple mean filtering achieves an error reduction of 66–69%, Gaussian mean filtering of 72–75%, and Gaussian LPF of 66–69%. If optimized filters are used for DIC measurements on steel samples, the total strain error can be reduced from initial 240−300 μstrain to 100–150 μstrain. In conclusion, the noise-floor of DIC signals is considerable and the preferable filters were a simple mean with s*¯ = 2, a Gaussian mean with σ*¯ = 1.7, and a Gaussian LPF with D0*¯ = 2.5 in the examined cases.

Spectral Estimation of Multiple Light Sources based on Highlight Detection

The authors discuss the spectral estimation of multiple light sources from image data in a complex illumination environment. An approach is proposed to effectively estimate illuminant spectra and the corresponding light sources based on highlight areas that appear on dielectric object surfaces. First, the authors develop a highlight detection method using two types of convolution filters with Gaussian distributions, center-surround and low-pass filters. This method is available even for white surfaces, and it is independent of object color and of viewing and incidence angles. Second, they present an algorithm for estimating the illuminant spectra from extracted highlight areas. Each specular highlight area has a spectral composition corresponding to only one light source among multiple light sources. The spectral image data are projected onto a two-dimensional subspace, where a linear cluster in pixel distribution is detected for each highlight area. Third, the relative positional relationship between highlight areas among different object surfaces is used to identify the light sources on each surface. The authors develop an algorithm based on probabilistic relaxation labeling. The light source for each highlight and the corresponding spectral-power distribution are determined from the iterative labeling process. Finally, the feasibility of the proposed approach is examined in an experiment using a real complex environment, where dielectric objects are illuminated by multiple light sources of light-emitting diode, fluorescence, and incandescence.

Arbitrary oriented multilingual text detection and segmentation using level set and Gaussian mixture model

A pioneer conceptual combination of level set method and Gaussian Mixture Model (GMM) is presented in the described multilingual, arbitrary-oriented character segmentation. The method is a serial accomplishment processes of Gaussian low pass filter, single level 2 Dimensional Discrete Wavelet Transform (2D DWT) for better feature extraction and implemented level set method, k-means clustering algorithm with GMM to achieve a veracious character segmentation results by detecting great measure of true text region of an image. The proposed method segments a character chiefly by distinguishing the touching character constituents and also deals discontinuities presence in a character by using Laplacian of Gaussian filter and morphological bridge function in a intellectual way. The exhibited method was explored on Multi-script Robust Reading Competition dataset and on our privately collected graphical and handwritten multilingual, arbitrarily-oriented text images. The suggested method is compared with the well known multilingual and arbitrarily-oriented charter segmentation methods. The described method attains better segmentation outcomes when compared to the familiar functioning methods. Hence, the suggested method is highly suitable to consider as an improved, standard and procedural technique.

Detection of Computer Graphics Using Attention-Based Dual-Branch Convolutional Neural Network from Fused Color Components.

With the development of 3D rendering techniques, people can create photorealistic computer graphics (CG) easily with the advanced software, which is of great benefit to the video game and film industries. On the other hand, the abuse of CGs has threatened the integrity and authenticity of digital images. In the last decade, several detection methods of CGs have been proposed successfully. However, existing methods cannot provide reliable detection results for CGs with the small patch size and post-processing operations. To overcome the above-mentioned limitation, we proposed an attention-based dual-branch convolutional neural network (AD-CNN) to extract robust representations from fused color components. In pre-processing, raw RGB components and their blurred version with Gaussian low-pass filter are stacked together in channel-wise as the input for the AD-CNN, which aims to help the network learn more generalized patterns. The proposed AD-CNN starts with a dual-branch structure where two branches work in parallel and have the identical shallow CNN architecture, except that the first convolutional layer in each branch has various kernel sizes to exploit low-level forensics traces in multi-scale. The output features from each branch are jointly optimized by the attention-based fusion module which can assign the asymmetric weights to different branches automatically. Finally, the fused feature is fed into the following fully-connected layers to obtain final detection results. Comparative and self-analysis experiments have demonstrated the better detection capability and robustness of the proposed detection compared with other state-of-the-art methods under various experimental settings, especially for image patch with the small size and post-processing operations.

PENERAPAN METODE FILTER GAUSSIAN DALAM PERBAIKAN KUALITAS CITRA SATELIT MODIS

One technology that uses satellite capabilities that can capture the image of an area is the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor, the resulting image may experience noise (noise) this is caused when sending through the transmission line, the image is too dark or bright, the image less sharp or blurred. For that we need a method of noise reduction such as remote sensing through satellite imagery. The purpose of this study is to apply the Gaussian lowpass filter method in improving the quality of fashionable satellite imagery in order to have good image quality. The results of PSNR show the highest value on the MOD_04 image, with smoother image results, the resulting PSNR value is 19.7791 higher than other images, where the higher the PSNR value, the better the resulting image.

Comparison of Respiratory and Ischemic Heart Mortalities and their Relationship to the Thermal Environment

Chronic respiratory and ischemic heart diseases are globally important parts of total mortality. This study focuses on the occurrence of mortality due to these disease groups in Germany and possible effects of the thermal environment. A retrospective analysis on the mortality rates of chronic lower respiratory diseases (CLRD) and ischemic heart diseases (IHD) at the regional level in Germany for the period 2001–2015 was done in combination with meteorological observations from the network of the German Meteorological Service. In order to control the mortality data for long-term and seasonal trends, a 365-day Gaussian low-pass filter with a filter response function was applied. The thermal environment was analysed using 2 m air temperature (Ta) and the human biometeorological index Perceived Temperature (PT). The relationship of the Relative Risk (RR) of mortality to the thermal environment is displayed as an exposure–response curve, with threshold values at which RR increases significantly towards higher and lower temperature values. CLRD mortality increases above 17.6 °C, at approximately 4.4%/°C (CI: ± 0.3). The increase of IHD mortality above the threshold of 18.8 °C is less steep, at 3.5%/°C (CI: ± 0.2). During hot periods, CLRD mortality increases by 19.9%, which is twice as much as IHD mortality, with an increase of 9.8%. However, cold days and cold periods affect IHD slightly more than CLRD. The results highlight the concerns of CLRD patients during hot days as well as heat waves. This could lead to better precautions being taken for respiratory patients, which are already established for cardiac patients in Germany.

Quaternion Mahalanobis Non-local Means for Color Image Denoising

By improving the quaternion non-local means (QNLM) image denoising algorithm, the quaternion Mahalanobis non-local means (QMNLM) method is proposed. This method combines quaternion with non-local similarity priors and introduces quaternion Mahalanobis distance. Firstly, the Mahalanobis distance between the image patches is calculated in the eigenspace by singular value decomposition of quaternion since the Mahalanobis distance is not robust in the sample space. Secondly, the image data is analyzed with the principal component analysis method, thus the Mahalanobis distance equation is simplified. The image with noise affected by high noise was first processed with a Gaussian low pass filter (LPF). Experimental results show that the noise reduction effect of this method is better than the non-local means (NLM), generalized non-local Means (GNLM) and QNLM methods. The proposed algorithm can effectively eliminate image noise and efficiently improve the image visual effect.

Integration of airborne gravimetry data filtering into residual least-squares collocation: example from the 1\xa0cm geoid experiment

Low-pass filters are commonly used for the processing of airborne gravity observations. In this paper, for the first time, we include the resulting correlations consistently in the functional and stochastic model of residual least-squares collocation. We demonstrate the necessity of removing high-frequency noise from airborne gravity observations, and derive corresponding parameters for a Gaussian low-pass filter. Thereby, we intend an optimal combination of terrestrial and airborne gravity observations in the mountainous area of Colorado. We validate the combination in the frame of our participation in ‘the 1 cm geoid experiment’. This regional geoid modeling inter-comparison exercise allows the calculation of a reference solution, which is defined as the mean value of 13 independent height anomaly results in this area. Our result performs among the best and with 7.5 mm shows the lowest standard deviation to the reference. From internal validation we furthermore conclude that the input from airborne and terrestrial gravity observations is consistent in large parts of the target area, but not necessarily in the highly mountainous areas. Therefore, the relative weighting between these two data sets turns out to be a main driver for the final result, and is an important factor in explaining the remaining differences between various height anomaly results in this experiment.

Influence of gear surface roughness on the pitting and micropitting life

Pitting and micropitting are the two main gear rolling contact fatigue modes. It is widely accepted that micropitting will lead to pitting; however, the relationship between pitting and micropitting life needs further investigation. In this work, micropitting and pitting tests were performed on an FZG back-to-back test rig using standard FZG PT-C and GF-C gears. The gear tooth profile change due to micropitting and pitting damage was measured in situ in the gearbox using a profilometer after each test. The gear surface roughness parameters were calculated from the measured tooth profile. A Gaussian low pass filter with cut off length [Formula: see text] mm was applied to the measured tooth profile to obtain the waviness. The calculated roughness parameters and the obtained tooth profile with waviness for each test were imported into the KISSsoft software to calculate the contact stress and specific film thickness at the corresponding load stage. Experimental results show that smooth gear surface can reduce or even avoid micropitting damage, but could lead to a reduction in pitting life.

The Use of LSTM Neural Networks to Implement the NARX Model. A Case Study of EUR-USD Exchange Rates

The paper focuses on financial data forecasting in terms of one-step-ahead nonlinear model with exogenous inputs. The main aim is the development of a methodology to forecast the exchange rate between EURO and US Dollar. The prediction task is carried out by two recurrent neural networks, the standard NARX neural network and a LSTM-based approach. The exogenous inputs consist of historical trading data and three widely used technical indicators, namely a variant of moving average, the Upper Bollinger Frequency Band and the Lower Bollinger Frequency Band. In order to obtain accurate forecasting algorithms, the exogenous inputs are filtered using the well-known Gaussian low-pass filter. The quality of each method is evaluated in terms of both quantitative and qualitative metrics, namely the root mean squared error, the mean absolute percentage error, and the prediction of change in direction. Extensive experiments point out that the most suited forecasting method is based on the proposed LSTM neural network for NARX model.

Perceptual image hashing based on structural fractal features of image coding and ring partition

Perceptual image hashing finds increasing attention in several multimedia security applications. However, reaching the trade-off balance between the two most important properties of image hashing− robustness and discrimination, still remains the most restive challenge in hashing schemes. In this study, a robust image hashing technique is proposed by incorporating ring partition and fractal image coding. The scheme starts by normalizing the image to help in extracting its local features. Then the concept of ring partition is introduced in order to make our hash rotation invariant by dividing the image into 5 different rings to form a secondary image that possesses the invariant property. Further, image coding is introduced by extracting the structural fractal features to exploit dimensionality reduction and compression, hence, generating a robust hash. To ensure the system’s security, encryption is performed on the generated fractal elements before the final hash construction. We conduct series of experiments to evaluate the performance of our scheme. The achieved result shows that our scheme is robust against several content-preserving attacks such as image rotation, JPEG compression, gamma correction, gaussian low pass filtering, image scaling, cropping, brightness adjustment and contrast adjustment. In addition, the receiver operating characteristics is used to show the discriminative capability and robustness of our scheme as compared to other state-of-art schemes in the literature.

A Robust Image Watermarking Scheme Based on the Laplacian Pyramid Transform

This paper is concerned with the digital image watermarking techniques to protect intellectual property and to authenticate digital images. Different from the most conventional methods using the discrete cosine transforms (DCT) and discrete-wavelet transforms (DWTs), this paper exploits the improved Laplacian pyramid transform to develop a new image watermarking scheme in which the improved Laplacian pyramid transform is used to decompose and reconstruct the host image. Then, to select an appropriate watermarking solution, we investigate the various frequency sub-band regions with different the levels and strength factors to perform the watermark embedding. Finally, we conduct experiments to investigate the invisibility and robustness of the proposed algorithm in terms the peak signal-to-noise ratio (PSNR), normalized correlation (NC), and structural similarity index (SSIM). Experimental results showed that our proposed scheme offers good robustness and invisibility. As compared to the watermarking schemes using the curvelets, our watermarking scheme is more robust for the lossy JPEG compression and Gaussian low pass filtering attacks. In addition, our method is also efficient in terms of computational time.