Adaptive Window Research Articles

Tropical algebra based adaptive filter for noise removal in digital image

The concept of the tropical algebra was first introduced to solve problems in mathematical economy such as optimization and approximation problems. In this paper, the concept of tropical algebra is used to build an image filtering algorithm. By using this concept, the lowest and highest pixel values are considered in determining the new pixel value. In addition, adaptive window will also be implemented to help the filtering process become more effective at high density noise. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to evaluate the output image quality produced by the filtering method. In this experiment, the performance of proposed method and existing methods such as switching median filter (SMF), adaptive fuzzy noise switching median filter (NAFSM), modified decision based on unsymmetric trimmed median filter (MDBUTMF), adaptive type-2 fuzzy filter (AT2FF) ), based on pixel density filters (BPDF), different applied median filters (DAMF), and tropical SVD filters (TSVD) will be compared. PSNR and SSIM results show that the proposed method outperforms most existing methods: SMF (27.13/0.7954), NAFSM (29.11/0.8459), MDBTUMF (29.18/0.8462), AT2FF (28.10/0.8159), BPDF (25.65/0.7545), DAMF (31.20/0.8833), TSVD (28.39/0.7906), and proposed (31.26/0.8827).

Three-observation-window time-gated algorithm for fluorescence lifetime detection.

A novel three-observation-window time-gated algorithm that combines overlapped windows and discrete windows together is developed for accurate fluorescence lifetime extraction. The new algorithm adopting a rapid lifetime determination strategy can offer an excellent ability to precisely detect long fluorescence lifetime for fluorescence lifetime imaging microscopy. Monte Carlo simulation indicates that an extremely small relative standard deviation below 0.4% is obtained over a wide fluorescence lifetime range from 5 ns to 30 ns. The detection error of the short fluorescence lifetime less than 5 ns is further reduced by means of an adaptive window width method. In contrast to other algorithms, such as time-correlated single-photon counting and traditional gated-window methods, not only the detection range but also the measurement accuracy is dramatically enhanced.

A new quality evaluation parameter for Rayleigh backscattering spectrum and its adaptive subset window algorithm in distributed fiber strain measurement

• A quality factor for the Rayleigh scattering spectrum is defined. • An algorithm of the adaptive subset window length is proposed. Rayleigh backscattering spectrum (RBS) correlation based distributed fiber strain measurement can be achieved by calculating the RBS offset using cross-correlation function from an unstrained and strained optical fiber under test. Thus the subset window length and the quality of the RBS in the subset have an important impact on the accuracy of the cross-correlation analysis and the strain measurement. How to evaluate the quality of different RBS signals plays an important role in optimizing the subset window length and improving the use of the technique. In this paper, a parameter called mean of the square of intensity gradient (MSIG) is proposed for quality assessment of the RBS signals. Further, an adaptive subset window algorithm base on the MSIG of each subset is developed for the subset window selection. The experiments verify the effectiveness and accuracy of the proposed parameter. The results show that the RBS with larger MSIG has smaller standard deviation error and higher signal quality. The adaptive subset window algorithm based on MSIG proposed in this paper can effectively improve the strain calculation accuracy of the distributed fiber strain measurement.

A novel adaptive window based technique for T wave detection and delineation in the ECG

Abstract The electrocardiogram (ECG) morphology determines the overall activity of the heart and is the most widely used tool in the diagnostic processes. T wave is a crucial wave component that reveals very useful information regarding various cardiac disorders. In this paper we have proposed a novel T wave detection technique based on adaptive window and simple decision rule. The proposed technique uses two-stage median filters followed by the Savitzky-Golay filter at the pre-processing stage to remove the noises in the ECG signal. The QRS complex is detected for locating the T wave as a reference in one ECG cycle. An R-R interval based window is considered for detecting the T wave, and decision logic depends on the iso-electric line value. The proposed technique is tested on the QT database and self-recorded dataset for its performance evaluation. In the present work, the results achieved for T wave detection sensitivity (Se), positive predictivity (+P), detection error rate (DER), and accuracy (Acc) on the QT database are Se = 97.57%, +P = 99.63%, DER = 2.78%, and Acc = 97.22% with an average time error of (3.468 ± 5.732) ms. The proposed technique shows Se = 99.94%, +P = 99.94%, DER = 0.01%, and Acc = 99.89% on the self-recorded dataset. The proposed technique is also capable of detecting both the upward and downward T wave efficiently in the ECG signal.

Improved Cost Computation and Adaptive Shape Guided Filter for Local Stereo Matching of Low Texture Stereo Images

Dense stereo matching has been widely used in photogrammetry and computer vision applications. Even though it has a long research history, dense stereo matching is still challenging for occluded, textureless and discontinuous regions. This paper proposed an efficient and effective matching cost measurement and an adaptive shape guided filter-based matching cost aggregation method to improve the stereo matching performance for large textureless regions. At first, an efficient matching cost function combining enhanced image gradient-based matching cost and improved census transform-based matching cost is introduced. This proposed matching cost function is robust against radiometric variations and textureless regions. Following this, an adaptive shape cross-based window is constructed for each pixel and a modified guided filter based on this adaptive shape window is implemented for cost aggregation. The final disparity map is obtained after disparity selection and multiple steps disparity refinement. Experiments were conducted on the Middlebury benchmark dataset to evaluate the effectiveness of the proposed cost measurement and cost aggregation strategy. The experimental results demonstrated that the average matching error rate on Middlebury standard image pairs is 9.40%. Compared with the traditional guided filter-based stereo matching method, the proposed method achieved a better matching result in textureless regions.

보간법 기반 가변 PRF 스퀸트 Spotlight SAR 처리 기법

The squinted spotlight SAR(Synthetic Aperture Radar) is an effective imaging mode for obtaining high-resolution SAR imagery in advance. However, a large-range cell migration heavily burdens the storage equipment, and the SAR system should consider its high risk for blind range and Nadir interference. Recently, the PRF(Pulse Repetition Frequency) variation and ASRW(Adaptive Sliding Range Window) have been proposed as effective techniques for squinted SAR image acquisition. This paper presents a signal processing algorithm for the squinted variable PRF spotlight-SAR. The algorithm is a two-step focusing algorithm combined with the interpolation method for compensating distortions due to PRF variation. The simulated variable PRF SAR raw data and processing results demonstrate the validity of the proposed approach.

Kirsch Direction Template Despeckling Algorithm of High-Resolution SAR Images-Based on Structural Information Detection

In order to overcome the drawback of the traditional Kirsch template despeckling usings fixed windows, an improved Kirsch direction template despeckling algorithm, based on structural information detection, is proposed for high-resolution synthetic aperture radar (SAR) images. First, the point targets are detected and preserved in the current region. Second, the window is enlarged adaptively based on the statistical characteristics of the local region. Finally, the window finally obtained is classified. The averaged filter is directly adopted if the region is homogeneous, or else the Kirsch template filter is used. Combining point target detection, adaptive windowing, and region classification, altogether the proposed algorithm can effectively improve the performance of the traditional Kirsch direction template despeckling. Despeckling experiments on simulated and real high-resolution SAR images demonstrate that the Kirsch direction template despeckling algorithm based on structural information detection can not only sufficiently suppress speckle in homogenous and edge regions, but also effectively preserve point targets and edge information, leading to good despeckling results.

Online Generative Adversary Network Based Measurement Recovery in False Data Injection Attacks: A Cyber-Physical Approach

State estimation plays a critical role in maintaining operational stability of a power system, which is however vulnerable to attacks. False data injection (FDI) attacks can manipulate the state estimation results through tampering the measurement data. In this paper, a cyber-physical model is proposed to defend against FDI attacks. It judiciously integrates a physical model which captures ideal measurements, with a generative adversarial network (GAN) based data model which captures the deviations from ideal measurements. To improve computation efficiency of GAN, a new smooth training technique is developed, and an online adaptive window idea is explored to maintain the state estimation integrity in real time. The simulation results on IEEE 30-bus system and IEEE 118-bus system demonstrate that our defense technique can accurately recover the state estimation data manipulated by FDI attacks. The resulting recovered measurements are sufficiently close to the true measurements, with the error lower than $1.5e^{-5}$ and $2e^{-2}$ p.u. in terms of voltage amplitude and phase angle, respectively.

Fully Adaptive Denoising of ECG Signals Using Empirical Mode Decomposition with the Modified Indirect Subtraction and the Adaptive Window Techniques

Electrocardiogram (ECG) is one of the major methods for the diagnosis of heart malfunctions. ECG signals are susceptible to both high-frequency and low-frequency noises such as electromyography (EMG), power line interference (PLI) and baseline wander noises, respectively. These noises deteriorate the quality of ECG signals and challenge the proper identification of heart illnesses. In this article, we report an improved fully adaptive method for canceling high-frequency noises using the empirical mode decomposition (EMD) with the modified indirect subtraction and adaptive window techniques. As high-frequency noises are approximately of zero mean and lower-order intrinsic mode functions (IMFs) contain high-frequency noises, a statistical test is performed to determine whether a particular combination of IMFs has zero mean. First, to remove the PLI noise, the modified indirect subtraction technique is used. The sum of IMFs which are dominated by the noise is passed to a Butterworth band-pass filter, and the resultant filtered signal is subtracted directly from the noisy ECG signal. Then, the EMG noise is removed by an improved adaptive window-based noise reduction technique. By exploiting this technique, in contrast to common EMD-based methods, the duration of the QRS complex is computed regarding the location of the peak of the R wave and the widest possible QRS complex which makes the proposed technique applicable to all types of ECG signals. The quantitative results of simulations performed on several records from the MIT–BIH arrhythmia database prove the better performance of the proposed method than the compared methods at different noise levels.

Best practices for velocity estimations in highly aerated flows with dual-tip phase-detection probes

Dual-tip phase-detection probes can be used to measure flow properties in gas-liquid flows. Traditionally, time-averaged interfacial velocities have been obtained through cross-correlation analysis of long time-series of phase fraction signals. Using small groups of detected particles, a recently developed adaptive window cross-correlation (AWCC) technique enables the computation of pseudo-instantaneous interfacial velocities and turbulence quantities in highly aerated flows, albeit subject to some smoothing which is due to the use of a finite window duration. This manuscript provides guidance on the selection of optimum processing parameters for the AWCC technique, additionally addressing shortcomings such as velocity bias correction and extrapolation of turbulence levels to single particles. The presented technique was tested for three self-aerated flows: smooth and rough-wall boundary layers (tunnel chute and stepped spillway), as well as breaking shear layer flows of a hydraulic jump. Robust estimations of mean velocities and velocity fluctuations were obtained for all flow situations, either using dual-tip conductivity or fiber optical probe data. The computation of integral time scales and velocity spectra is currently limited by the data rate and must be treated with caution.

Accurate and Rapid Auto-Focus Methods Based on Image Quality Assessment for Telescope Observation

Aiming at improving the speed and accuracy of auto-focus for telescope observation, algorithms for image estimation and auto-focus were investigated and are discussed in this article. Based on the image quality assessment, the auto-focusing process of the telescope system is realized by using the mountain-climb search method. Several evaluation functions were tested in different scenarios. It is demonstrated that the Tenengrad image estimation function (IEF) is suitable for an instant and accurate auto-focus process of the telescope. Furthermore, we implemented sampling and dynamic adaptive focusing window (ES-DAFW) methods with the Tenengrad IEF to enhance the sensitivity and accuracy of the auto-focus process. The experimental results showed that our ES-DATW method can provide more accurate results in less time for the auto-focus process compared to the conventional approaches, especially for a sparse image. These results promise significant applications to the auto-focusing of other telescopes with image quality assessment.

Hybrid Path Planning Based on Safe A* Algorithm and Adaptive Window Approach for Mobile Robot in Large-Scale Dynamic Environment

When mobile robot used in large-scale dynamic environments, it face more challenging problems in real-time path planning and collision-free path tracking. This paper presents a new hybrid path planning method that combines A* algorithm with adaptive window approach to conduct global path planning, real-time tracking and obstacles avoidance for mobile robot in large-scale dynamic environments. Firstly, a safe A* algorithm is designed to simplify the calculation of risk cost function and distance cost. Secondly, key path points are extracted from the planned path which generated by the safe A* to reduce the number of the grid nodes for smooth path tracking. Finally, the real-time motion planning based on adaptive window approach is adopted to achieve the simultaneous path tracking and obstacle avoidance (SPTaOA) together the switching of the key path points. The simulation and practical experiments are conducted to verify the feasibility and performance of the proposed method. The results show that the proposed hybrid path planning method, used for global path planning, tracking and obstacles avoidance, can meet the application needs of mobile robots in complex dynamic environments.

Research on flame location based on adaptive window and weight stereo matching algorithm

Flame location based on binocular vision has some problems such as low matching accuracy, slow matching speed and so on. To solve the above problems, we propose stereo edge matching algorithm based on adaptive window and weight. Since image edge regions has rich information, an edge based adaptive window algorithm is proposed. For non-edge regions, adaptive weight algorithm based on row and column is accumulated separately, which is used to reduce computational complexity. Finally it gets the position of the flame according to disparity map. The experimental results show that the matching algorithm based on adaptive window and weight can locate the fire source. Its error is within 10 cm and the matching speed is higher and the effect is better.

Adaptive Switching Weight Mean Filter for Salt and Pepper Image Denoising

Abstract We propose Adaptive Switching Weight Mean Filter (ASWMF) to remove the salt and pepper noise. Instead of using median or mean, ASWMF assigns value of a switching weight mean (SWM) to grey value of the centre pixel of an adaptive window. SWM is evaluated by eliminating all noisy pixels from the adaptive window and putting a low weight for pixels on the diagonals and a high weight for pixels outside the diagonals. ASWMF can remove noise with various noise levels effectively. It not only successes for low-density denoising, but also removes medium-density and high-density noise impressively. In experiments, we compare denoising results with other similar denoising methods. According to intuition as well as error metrics such as the peak signal-to-noise ratio and the structure similarity, ASWMF outperforms other methods.

Fast Constrained Dynamic Time Warping for Similarity Measure of Time Series Data

In this paper, we propose an efficient algorithm for reducing the computational complexity of dynamic time warping (DTW) for obtaining similarity measures between time series. The DTW technique exhibits superior classification accuracy compared to other algorithms but has a limitation of high computational complexity. To reduce the computational complexity of standard DTW, constrained DTW and fast DTW techniques have been proposed. The constrained DTW technique reduces the computational complexity of standard DTW by only considering limited alignments and prevent excessive alignments between two time series, which can reduce the overall classification accuracy. However, since the searching window for limited alignments is fixed, the computational complexity is still high when the length of time series is long. In contrast, fast DTW has a lower classification accuracy than the constrained DTW technique. However, fast DTW estimates the optimal alignment while considering only the alignments within an adaptive window; as two time series increase in length, the fast DTW technique more strongly reduces the computational complexity. Therefore, we propose a fast constrained DTW approach that applies the optimal alignment estimation of fast DTW within the limited alignments of constrained DTW. As the proposed fast constrained DTW operates within a fixed window area of the constrained DTW, which prevents excessive alignments, it has a classification accuracy similar to that of the constrained DTW. Also, in the fast constrained DTW, when the length of the time series is long, a low computational complexity is maintained by the influence of the adaptive window of the fast DTW. Experimental results on 19 UCR time series datasets show that the proposed fast constrained DTW method achieves computational complexity reductions of approximately 52.2% and 22.3% compared to the existing fast DTW and constrained DTW, while maintaining almost the same classification accuracy as the constrained DTW.

Adaptive Slide Window-Based Feature Cognition for Deceptive Information Identification

Numerous false information come up to be the primary threat for regular communication and cooperation, which always have similar expressions and diffusion patterns with regular information. To recognize deceptive information and alleviate their threat against business and economy, a number of research approaches for deceptive information identification have been proposed. However, these approaches suffer from their rough feature extraction processes, and thus cannot distinguish deceptive information from others. We introduce an adaptive slide-window based feature extraction, which captures semantic features and eliminates the trivial parts from them with adaptive slide windows on sentence elements, in order to facilitate accurate semantic structure representation of various texts. In addition, we propose a deep deceptive information identification model based on the proposed feature extraction scheme. Experiments on three real-world datasets demonstrate that the proposed deep deceptive information identification model can distinguish deceptive information from regular information accurately by extracting the significant features instead of trivial common expressions.

Robust Visual Object Tracking With Multiple Features and Reliable Re-Detection Scheme

In recent years, correlation filter based trackers have seen widespread success because of their high efficiency and robustness. However, a single feature based tracker cannot deal with complex scenes such as serious occlusion, motion blur and illumination variation. In this paper, we develop a novel tracking method combining color feature, Hog feature and motion feature. The motion feature is estimated between adjacent frames by large displacement optical flow. Besides, in order to cope with boundary effect existing in traditional correlation filter based trackers, an adaptive cosine window is introduced in our method, which can highlight the target region, suppress the background region and enlarge search region. Meanwhile, a novel judge scheme combining Hog correlation response and color response is adopted to evaluate the reliability of tracking result. Finally, inverse sparse representation is presented to locate coarse positions of target in case of tracking failures. Extensive experiments on five famous tracking benchmarks including OTB100, TColor-128, UAVDT, UAV123 and VOT2016 demonstrate our proposed method outperform other sate-of-the-art methods in terms of robustness and accuracy.

Generalized Adaptive Polynomial Window Function

We present a novel method to design and optimize window functions based on combinations of linearly independent functions. These combinations can be performed using different strategies, such a sums of sines/cosines, series, or conveniently using a polynomial expansion. To demonstrate the flexibility of this implementation, we propose the Generalized Adaptive Polynomial (GAP) window function, a non-linear polynomial form in which all the current window functions could be considered as special cases. Its functional flexibility allows fitting the expansion coefficients to optimize a certain desirable property in time or frequency domains, such as the main lobe width, sidelobe attenuation, and sidelobe falloff rate. The window optimization can be performed by iterative techniques, starting with a set of expansion coefficients that mimics a currently known window function and considering a certain figure of merit target to optimize those coefficients. The proposed GAP window has been implemented and several sets of optimized coefficients have been obtained. The results using the GAP exemplify the potentiality of this method to obtain window functions with superior properties according to the requirements of a certain application. Other optimization algorithms can be applied within this strategy to further improve the window functions.

A Contactless Zero-Value Insulators Detection Method Based on Infrared Images Matching

A contactless method based on infrared image matching is presented for zero-value insulator detection on outdoor porcelain insulator string. An improved SIFT (scale-invariant feature transformation) method is developed to extract features and accomplish the pre-matching between the insulator string to be detected and the standard string in the image library. An improved RANSAC (random sampling consistency) algorithm is developed to remove mismatching points and achieve more accurate and faster detection. Firstly, an adaptive circle window is designed to improve the sensitivity of the SIFT operator on arc features extraction from insulators. Then, the dimension of the feature is decreased from128 to 32 by dividing the circle into 4 fan-shaped regions and descript the feature on 8 directions to achieve the lower computation burden and high accuracy. For the SIFT method and RANSAC method uses Euclidean distance to measure the similarity between features extracted from the string to be detected and the string in the standard library, mismatching may be caused. Spatial geometric features of the neighborhood around the feature point are used to construct the error function to improve the RANSAC method and remove the mismatching points. Faster matching is obtained by a threshold control for decreasing the number of data checking for the consensus set data models. Testing results show that the presented method is effective in the detection of zero-value resistances for outdoor insulator string. Compare with the SIFT method and SIFT + RANSAC method, the presented method has higher accuracy and faster detection speed.

A Crucial Wave Detection and Delineation Method for Twelve-Lead ECG Signals

Delineating the crucial waves in electrocardiogram records is a paramount work for the automatic diagnosis system of heart diseases. In this paper, a novel method is described to determine the boundaries and the peaks of P waves, QRS complexes and T waves by utilizing twelve-lead electrocardiogram signals. It avoids the difficulty of setting the thresholds when determining the boundaries of crucial waves and also the trouble of selection of wavelet basis as the wavelet-based method does. The signals are first preprocessed by a bandpass filter. After that, the locations of QRS complexes are identified. And based on the QRS locations, adaptive search windows are set to detect the locations of P waves and T waves. Then, a method called local distance transform decides the wave boundary in each lead. Finally, the final boundary determination rule is applied to obtain reliable boundaries. We justify the performance of our algorithm on LUDB database. When the tolerance window interval is 40ms, the peak accuracies of P wave, QRS complex and T wave are all beyond 98% and their boundary accuracies are all above 96%. Compared with the derivative threshold method and the wavelet-based method where the tolerance window interval is 150ms, the algorithm shows a sensitivity and a positive predictive value of peaks and boundaries greater than or equal to 98.43% and 96.44% for the P wave, 99.89% and 99.86% for the QRS complex and 99.21% and 99.85% for the T wave. For the critera of average error and standard deviation, our method has the performance similar to those methods. In addition, our algorithm can also handle such several situations where the boundary determination of crucial waves is tough as high T wave, high noise and baseline wandering well.