Derivative Filter Research Articles

Local gradient patterns (LGP): An effective local-statistical-feature extraction scheme for no-reference image quality assessment

Gradient features are known to be effective for full-reference (FR) image quality assessment (IQA). However, only a few metrics utilize gradient features for no-reference (NR) IQA. To investigate the potential benefits of gradient magnitude and phase for NR-IQA, we propose a novel and effective local-statistical-feature extraction metric, namely Local Gradient Patterns (LGP), for general-purpose NR-IQA. Using a Gaussian partial derivative filter, the image is first decomposed into two complementary components: gradient magnitude and phase. The local statistical features (e.g., the conditional probability distributions) are then extracted from the complementary components, using the derived local gradient magnitude and phase pattern operators. Finally, to facilitate NR-IQA, local statistical features that convey important structural information are mapped to the subjective mean opinion score of the image, using a support vector regression (SVR) procedure. We evaluated our proposed LGP metric using images from two publicly available test databases; the results confirm that the proposed LGP metric provides predictive performance that is superior to most state-of-the-art NR-IQA metrics and has an acceptable level of computational complexity.

Steganalysis of content-adaptive JPEG steganography based on Gauss partial derivative filter bank

A steganalysis feature extraction method based on Gauss partial derivative filter bank is proposed in this paper to improve the detection performance for content-adaptive JPEG steganography. Considering that the embedding changes of content-adaptive steganographic schemes are performed in the texture and edge regions, the proposed method generates filtered images comprising rich texture and edge information using Gauss partial derivative filter bank, and histograms of absolute values of filtered subimages are extracted as steganalysis features. Gauss partial derivative filter bank can represent texture and edge information in multiple orientations with less computation load than conventional methods and prevent redundancy in different filtered images. These two properties are beneficial in the extraction of low-complexity sensitive features. The results of experiments conducted on three selected modern JPEG steganographic schemes—uniform embedding distortion, JPEG universal wavelet relative distortion, and side-informed UNIWARD—indicate that the proposed feature set is superior to the prior art feature sets—discrete cosine transform residual, phase aware rich model, and Gabor filter residual.

Simultaneous design of proportional–integral–derivative controller and measurement filter by optimisation

A method for optimization of PID controller parameters and measurement filter time constant is presented. The method differs from the traditional approach in that the controller and filter parameters are simultaneously optimized, as opposed to standard, sequential, design. Control performance is maximized through minimization of the integrated absolute error (IAE) caused by a unit step load disturbance. Robustness is achieved through Hinf constraints on sensitivity and complementary sensitivity. At the same time, noise attenuation is enforced by limiting either the H2 or Hinf norm of the transfer function from measurement noise to control signal. The use of exact gradients makes the synthesis method faster and more numerically robust than previously proposed alternatives. (Less)

Multiverse optimized fuzzy-PID controller with a derivative filter for load frequency control of multisource hydrothermal power system

In this paper, a multiverse optimized (MVO) fuzzy PID controller with a derivative filter (fuzzy-PIDF) is proposed for the load frequency control (LFC) of a two-area multisource hydrothermal power system. The superiority of the MVO algorithm is demonstrated by comparing the system LFC performance with integral and fuzzy-PIDF controllers, both optimized using MVO, as well as some of the recent heuristic optimization techniques such as the ant lion optimizer, gray wolf optimizer, differential evolution, bacterial foraging optimization algorithm, and particle swarm optimization. To the best of the knowledge of the authors, the use of the MVO technique has not yet been reported for LFC studies. Among many of the controllers implemented here for comparison, the proposed MVO fuzzy-PIDF controller exhibits the best performance under different operating conditions in terms of settling times, maximum overshoot, and values of cost function, i.e. integral time absolute error. Furthermore, the robustness of the proposed control scheme is also investigated against variation of system parameters within $\pm $10%, along with random step load disturbances. The proposed control scheme is not very sensitive to parametric variations and therefore keeps providing effective performance even under $\pm $10% variations in system parameters. System modeling and simulations are carried out using MATLAB/Simulink.

Finite Differences in Forward and Inverse Imaging Problems: MaxPol Design

A systematic and comprehensive framework for finite impulse response (FIR) lowpass/fullband derivative kernels is introduced in this paper. Closed form solutions of a number of derivative filters are obtained using the maximally flat technique to regulate the Fourier response of undetermined coefficients. The framework includes arbitrary parameter control methods that afford solutions for numerous differential orders, variable polynomial accuracy, centralized/staggered schemes, and arbitrary side-shift nodes for boundary formulation. Using the proposed framework, four different derivative matrix operators are introduced and their numerical stability is analyzed by studying their eigenvalues distribution in the complex plane. Their utility is studied by considering two important image processing problems, namely gradient surface reconstruction and image stitching. Experimentation indicates that the new derivative matrices not only outperform commonly used methods but provide useful insights to the numerical issues in these two applications.

Heat source reconstruction from noisy temperature fields using a gradient anisotropic diffusion filter

This paper presents a post-processing technique for noisy temperature maps based on a gradient anisotropic diffusion (GAD) filter in the context of heat source reconstruction. The aim is to reconstruct heat source maps from temperature maps measured using infrared (IR) thermography. Synthetic temperature fields corrupted by added noise are first considered. The GAD filter, which relies on a diffusion process, is optimized to retrieve as well as possible a heat source concentration in a two-dimensional plate. The influence of the dimensions and the intensity of the heat source concentration are discussed. The results obtained are also compared with two other types of filters: averaging filter and Gaussian derivative filter. The second part of this study presents an application for experimental temperature maps measured with an IR camera. The results demonstrate the relevancy of the GAD filter in extracting heat sources from noisy temperature fields.

A Fully Digital Background Calibration Technique for M-Channel Time-Interleaved ADCs

In this paper, we propose a pure digital blind calibration method to estimate and calibrate offset, gain and timing mismatches. Gain errors are calibrated based on first channel correction using an overall reference, whereas for the rest of the $$(M-1)$$ channels, the corrected first channel becomes the reference channel. Time skew calibration is performed using a derivative filter followed by a fractional delay filter and a scaling factor. The proposed technique significantly reduces the required hardware resources, specifically for the derivative and fractional delay filters for which no look-up table is required. In addition, the proposed method requires only two finite impulse response filters with fixed coefficients, thus reducing complexity and hardware resources, as compared to adaptive filter techniques. For a sampling frequency of 3.072 GHz, the maximum achievable signal-to-noise and distortion ratio is 67 dB, resulting in effective number of bits of 10.83 for a 12-bit resolution analog-to-digital converter.

Integrated tuning of PID-derivative load frequency controller for two area interconnected system

PID-derivative filter has been considered better than the normal PID control when it comes for the stability analysis of a power system. This paper presents a PID-derivative filter load frequency controller for the two thermal-thermal and hydrothermal interconnected areas. The proposed controller has been based on cascading of derivative filter with PID controller. The Ziegler- Nichols (Z-N) tuning method has been used for tuning the controllers. Results of the proposed derivative controller have been compared with conventional proportional integral derivative controller in time domain analysis. A remarkable improvement in stability of the system has been observed with PID-derivative filter controller justifying its applicability. Simulated results given in the paper show the feasibility of the proposed PID-derivative filter controller.Keywords: controller; derivative filter; tie line; two area

Design and analysis of fuzzy PID controller with derivative filter for AGC in multi‐area interconnected power system

This study presents a comprehensive analytical study of a fuzzy proportional-integral-derivative (fuzzy-PID) controller with derivative filter (fuzzy-PIDF) for automatic generation control (AGC) in multi-area power system tuned by a novel hybrid local unimodal sampling-teaching learning based optimisation (LUS-TLBO) algorithm. The relevant advantage of this recommended controller is its high sensitivity to load perturbation. Initially a two area non-reheat type thermal system is considered and the gains of PIDF controller are tuned employing hybrid LUS-TLBO algorithm. The supremacy of PIDF controller is shown by comparing the results with some recently published articles. The study is further extended to design fuzzy logic based PID controller with derivative filter (fuzzy-PIDF) for AGC of the same power system employing hybrid LUS-TLBO algorithm. Supremacy of the proposed approach is shown by comparing the results with another recently published article. In addition, the work is also extended to deal with AGC issues in two area multi source hydro thermal power system using the proposed controller. Finally, it is seen that fuzzy-PIDF controller retains its supremacy over PID/PIDF/fuzzy-PID controllers. In addition to it, robustness analysis is carried out for the non-reheat two area thermal power systems by varying different parameters within the range of ±50%.

Generalised proportional–integral–derivative filter

In this study, the filtering problem is investigated for both linear and non-linear systems. A new generalised proportional–integral–derivative filter (GPIDF) is proposed by introducing the idea of proportional–integral–derivative (PID) control. It utilises the past, current and future information to estimate the current state in order to achieve a better filtering performance. Besides, the GPIDF provides a unified structure to accommodate a variety of filtering scenarios as its special cases, including the popular generalised Kalman filter. Based on the new proposed GPIDF structure, the optimal PID filter and extended optimal PID filter are designed using the minimum mean square error criterion at each time step. In addition, the practical strong robust proportional–integral filter and extended strong robust proportional–integral filter are developed without requirements of knowledge about model uncertainty. The developed filters are recursive and suitable for real-time online applications. Finally, two simulation studies are carried out to demonstrate the effectiveness and applicability of the authors' proposed methods.

Structural features of the southern Tulum Fault System, western central Argentina, through gravimetric data and geomorphologic analyses

A gravimetric analysis over the Tulum Valley was made. This data was used to reveal the structural setting of the Tulum Fault System situated in the southeastern part of San Juan province in the arid western part of Argentina. This system is the boundary between two geological provinces, the eastern Precordillera Oriental and the Sierras Pampeanas Occidentales. Gravity data was processed using upward continuation and vertical derivative filters and all the results were compared with the geomorphological and the drainage systems maps of the area. Our assessment confirms the presence of two structures in the Pampeano basement with positive anomalies similar to those found in Valdivia and Barboza hills, two important depocenters with low gravimetric gradients separated by a zone with higher gravity anomalies than the depocenters to the east and west. In view of this, a structural map is proposed for the area. This system is important not only because it is the boundary between two geological provinces and has significance regarding regional tectonic issues but also because it controls the surface drainage, soils distribution and groundwater flow of the Tulum basin conditioning the land use distribution.

Geophysical investigation of banded iron ore mineralization at Ero, North – Central Nigeria

Abstract The banded iron ore mineralization at Ero was investigated using aeromagnetic, resistivity and induced polarization (IP) methods with the aim of characterizing the deposit. Analysis of the aeromagnetic data involved the application of reduced-to-equator transformation, derivative filters, analytic signal and source parameter imaging techniques. Computer modelling of some of the identified anomalies was undertaken. The electrical resistivity and IP methods helped in discriminating between the iron ore and the host rock. The results showed that the banded iron formations (BIFs) were characterized by spherical analytic signal anomalies ranging from 0.035 nT/m to 0.06 nT/m within the granite gneiss and magnetic susceptibility of 0.007-0.014 SI. The iron ore had low chargeability (0.1-5.0 msec) and resistivity (1.5 × 102 to 2.5 × 103 Ωm). Structural features trending in the NE-SW, E-W, and NW-SE were identified, suggesting that the area had undergone many episodes of tectonic events. Depth to the BIF varied from the surface up to about 200 m. The chargeability response of the iron bodies suggested an average grade of 20%-40%, making the prospect for economic exploitation attractive.

Proposed standardized definitions for vertical resolution and uncertainty in the NDACC lidar ozone and temperature algorithms – Part 1: Vertical resolution

Abstract. A standardized approach for the definition and reporting of vertical resolution of the ozone and temperature lidar profiles contributing to the Network for the Detection for Atmospheric Composition Change (NDACC) database is proposed. Two standardized definitions homogeneously and unequivocally describing the impact of vertical filtering are recommended. The first proposed definition is based on the width of the response to a finite-impulse-type perturbation. The response is computed by convolving the filter coefficients with an impulse function, namely, a Kronecker delta function for smoothing filters, and a Heaviside step function for derivative filters. Once the response has been computed, the proposed standardized definition of vertical resolution is given by Δz = δz × HFWHM, where δz is the lidar's sampling resolution and HFWHM is the full width at half maximum (FWHM) of the response, measured in sampling intervals. The second proposed definition relates to digital filtering theory. After applying a Laplace transform to a set of filter coefficients, the filter's gain characterizing the effect of the filter on the signal in the frequency domain is computed, from which the cut-off frequency fC, defined as the frequency at which the gain equals 0.5, is computed. Vertical resolution is then defined by Δz = δz∕(2fC). Unlike common practice in the field of spectral analysis, a factor 2fC instead of fC is used here to yield vertical resolution values nearly equal to the values obtained with the impulse response definition using the same filter coefficients. When using either of the proposed definitions, unsmoothed signals yield the best possible vertical resolution Δz = δz (one sampling bin). Numerical tools were developed to support the implementation of these definitions across all NDACC lidar groups. The tools consist of ready-to-use “plug-in” routines written in several programming languages that can be inserted into any lidar data processing software and called each time a filtering operation occurs in the data processing chain. When data processing implies multiple smoothing operations, the filtering information is analytically propagated through the multiple calls to the routines in order for the standardized values of vertical resolution to remain theoretically and numerically exact at the very end of data processing.

Ambient aerosol composition by infrared spectroscopy and partial least-squares in the chemical speciation network: Organic carbon with functional group identification

ABSTRACTThe Fourier-transform infrared (FT-IR) spectra of ambient fine aerosols were used with partial least-squares (PLS) regression to accurately, inexpensively, and nondestructively predict organic carbon (OC) on polytetrafluoroethylene (PTFE) filters in the U.S. Environmental Protection Agencies' Chemical Speciation Network (CSN). Recently, a similar FT-IR method was used for OC determination in the rural United States Interagency Monitoring of PROtected Visual Environments network, with the present work extending the method to urban aerosols with low mass loadings. In the present study, FT-IR spectra were calibrated to collocated thermal/optical reflectance (TOR) OC measurements following numerical processing with a second derivative filter, backward Monte Carlo unimportant variable elimination, and a quadratic discriminant analysis-PLS vapor correction routine. After processing and vapor correcting spectra, the number of model components (latent variables) were reduced from thirty-five to three with...

A new multisensor software architecture for movement detection: Preliminary study with people with cerebral palsy

Abstract A five-layered software architecture translating movements into mouse clicks has been developed and tested on an Arduino platform with two different sensors: accelerometer and flex sensor. The architecture comprises low-pass and derivative filters, an unsupervised classifier that adapts continuously to the strength of the user's movements and a finite state machine which sets up a timer to prevent involuntary movements from triggering false positives. Four people without disabilities and four people with cerebral palsy (CP) took part in the experiments. People without disabilities obtained an average of 100% and 99.3% in precision and true positive rate (TPR) respectively and there were no statistically significant differences among type of sensors and placement. In the same experiment, people with disabilities obtained 97.9% and 100% in precision and TPR respectively. However, these results worsened when subjects used the system to access a communication board, 89.6% and 94.8% respectively. With their usual method of access-an adapted switch- they obtained a precision and TPR of 86.7% and 97.8% respectively. For 3-out-of-4 participants with disabilities our system detected the movement faster than the switch. For subjects with CP, the accelerometer was the easiest to use because it is more sensitive to gross motor motion than the flex sensor which requires more complex movements. A final survey showed that 3-out-of-4 participants with disabilities would prefer to use this new technology instead of their traditional method of access.

Kernel discriminant analysis of Block-wise Gaussian Derivative Phase Pattern Histogram for palmprint recognition

This paper presents an efficient palmprint recognition technique for palmprints collected with visible as well as multispectral imaging system. ROI extraction is a challenging task for palmprint captured in unconstrained environment. ROI extracted by gaps between fingers and width of palm makes system rotation and translation invariant. Approximation ROI obtained by First-level decomposition of ROI using Haar wavelet reduces computational overhead as well as noise. Phase quantization of AROI by Gaussian derivative filter gives Gaussian derivative phase pattern image and its block-wise histograms are concatenated to form a single vector referred as BGDPPH descriptor. Dimension reduction is performed by increasing discrimination between genuine and impostor scores using chi-RBF kernel discriminant analysis (KDA). Weighted score level fusion of spectral palmprints on Fisher criterion improves recognition rate. Robustness of the proposed BGDPPH descriptor against blur and noise is evaluated on four gray-scale and two multispectral palmprint databases collected through touch-based and touch-less acquisition devices.

Application of statistical modeling of image spatial structures to automated visual inspection of product quality

Automated visual inspection (AVI) attracts increasing interest in product quality control both academic and industrial communities, particularly on mass production processes, because product qualities of most types can be characterized with their corresponding surface visual attributes. However, many product images in AVI systems are comprised of stochastically accumulative fragmentations (particles) of local homogeneity, without distinctive foregrounds and backgrounds, which brings great challenges in computer analysis, e.g., rice images, fabric images, and consequently, in the intelligent identification of the product qualities. A method of Weibull distribution (WD)-based statistical modeling of image spatial structures (ISSs) to inspect automatically the product quality is presented. The ISS, obtained with multi-scale and omnidirectional Gaussian derivative filters (OGDFs), was demonstrated to be subject to a representative WD model of integral form based on the theory of sequential fragmentation in advance. The WD-model parameters (WD-MPs) of the ISS, with essential human perceptual significance, were extracted as the visual features for product quality identification. The classification performance of the proposed product quality inspection method, namely, the proposed WD-MP features integrated with an introduced spline regression (SR) classifier in this study, was verified on two case studies in the field of the AVI of product quality, namely, automated rice quality classification, and intelligent fabric quality assessment in the corresponding assembly lines of industrial scale. Experimental results indicate that the proposed WD-MP features can effectively characterize the statistical distribution profiles of ISS of complex grain images, piled with a large number of stochastically accumulative fragmentations. The proposed method provides an effective tool for grain image modeling and analysis and consequently lays a foundation for the intelligent perception of product qualities on assembly lines.

Principal curvatures based rotation invariant algorithms for efficient texture classification

The histograms of oriented gradients (HOG) and co-occurrence HOG (CoHOG) algorithms are simple and intuitive descriptors. However, the HOG and CoHOG algorithms based on gradient computation still have some shortcomings: they ignore meaningful textural properties and are unstable to noise. In this paper, two new efficient HOG and CoHOG methods are proposed. The proposed algorithms are based on the Gaussian derivative filters, and the feature vectors are obtained by means of principal curvatures. The feature vectors are rotation invariant by means of the rotation invariance characteristic of principal curvatures (i.e. eigenvalues). The experimental results on the CUReT, KTH-TIPS, KTH-TIPS2-a, UIUC, Brodatz album, Kylberg and Xu datasets confirm that the developed algorithms have higher classification rates than state-of-the-art texture classification methods. The classification results also demonstrate that the developed algorithms are more stable to noise and rotation than the original HOG and CoHOG algorithms.

Visual Perception-Based Statistical Modeling of Complex Grain Image for Product Quality Monitoring and Supervision on Assembly Production Line.

Computer vision as a fast, low-cost, noncontact, and online monitoring technology has been an important tool to inspect product quality, particularly on a large-scale assembly production line. However, the current industrial vision system is far from satisfactory in the intelligent perception of complex grain images, comprising a large number of local homogeneous fragmentations or patches without distinct foreground and background. We attempt to solve this problem based on the statistical modeling of spatial structures of grain images. We present a physical explanation in advance to indicate that the spatial structures of the complex grain images are subject to a representative Weibull distribution according to the theory of sequential fragmentation, which is well known in the continued comminution of ore grinding. To delineate the spatial structure of the grain image, we present a method of multiscale and omnidirectional Gaussian derivative filtering. Then, a product quality classifier based on sparse multikernel–least squares support vector machine is proposed to solve the low-confidence classification problem of imbalanced data distribution. The proposed method is applied on the assembly line of a food-processing enterprise to classify (or identify) automatically the production quality of rice. The experiments on the real application case, compared with the commonly used methods, illustrate the validity of our method.

Made in the humanities: Dual integral invariants for efficient edge detection

Abstract Motivated by the demand for robust character extraction from ancient documents with Script in 3D like cuneiform tablets, we are developing curvature based filter algorithms using integral invariants. While previous work was focused on analyzing 3D-models i. e. irregular triangular meshs in ℝ3, we introduce a fast, efficient adaption for edge detection in raster images. It is an alternative to the Canny edge detector algorithm and other complex algorithms using first and second order derivative filters. Our approach consists of two steps using (i) zero-crossings of volume integral invariants and (ii) surface integral invariants to discard false positives introduced by noise. The runtime of these Dual Integral Invariants (DII) is comparable with Difference of Gaussian filtering and it is independent of the number and length of edges. An additional benefit is the constant execution time as line tracing is not necessary. As the proposed algorithm has no smoothing included it preserves smaller features than recent variants of the Canny edge detector. We use artificial data sets with and without a known amount of noise to validate the accuracy of our method in comparison with operators supplied by the OpenCV library. The equidistant spacing of raster images allows fast estimation of the integral invariants based on filter masks like Roberts, Scharr and Kirsch. We show the results on well-known standard test images and synthetic data.