Circular Filter Research Articles

Suppressing Chest Radiograph Ribs for Improving Lung Nodule Visibility by using Circular Window Adaptive Median Outlier (CWAMO)

Chest radiograph ribs obstruct lung nodules. To see the nodule under the chest radiograph ribs, remove or suppress them. The paper describes a circular median filter approach for finding outliers in chest radiographs. The method uses 147 Japanese Society of Radiological Technology x-ray pictures (JSRT). Pixels with intensities two standard deviations above the median are median outliers. Contrast-Limited Adaptive Histogram Equalization enhances nodule visibility (CLAHE). The method is tested on modest chest radiographs and compared to the Budapest University Bone Shadow Eliminated X-Ray Dataset methodology. The initial test uses 50 modest chest radiographs (Test 1). The proposed approach is applied after active shape modelling (ASM) lung segmentation. True positive nodules are seen on 89% of chest radiographs of various subtleties. Test-2 and Test-3 used 20 subtlety-level photos. In Test-2, the peak signal-to-noise ratio (PSNR), mean-to-standard deviation ratio (MSR), and universal image quality index (IQI) are evaluated for the full image and compared to the existing algorithm. For all three parameters, the suggested technique outperforms the algorithm. Test-3 computes nodule MSR and compares it to Budapest University's Bone Shadow Eliminated Dataset and original chest radiographs. The new algorithm improved nodule area contrast by 3.83% and 23.94% compared to the original chest radiograph. This approach improves chest radiograph nodule visualization.

Design of cost effective variable bandwidth 2D low-pass, high-pass and band-pass filters with improved circularity

This paper discusses two-dimensional (2D) finite impulse response (FIR) filter structures to achieve improved circularity and variability in bandwidth without restructuring. The P3 transformation employed in this work for the design of circular filters helps to achieve better circularity and less complexity. The variability is obtained by using two different approaches. In Method 1, the bandwidths of the 2D circular filters are changed by adjusting a parameter in the one-dimensional (1D) domain. In contrast to Method-1, Method-2 directly adjusts the bandwidths of the 2D filters using a parameter in the 2D domain. Low-pass, high-pass, and band-pass variable bandwidth filters are designed using both methods to illustrate the benefits of the two approaches. Comparing their implementation complexity, contour approximation error, and normalized root mean square error to those of other multiplier-less transformations demonstrates their efficacy. It can be seen that Method 1 and 2 respectively achieve around 5% and 20% reduction in the number of multipliers with a maximum normalized root mean square errors of 0.3129 and 0.3532 when compared to that of the 2D filters designed using the latest existing transformation.

Effects of Atmospheric Correction and Image Enhancement on Effective Plastic Greenhouse Segments Based on a Semi-Automatic Extraction Method

To improve the multi-resolution segmentation (MRS) quality of plastic greenhouses (PGs) in GaoFen-2 (GF-2) images, the effects of atmospheric correction and image enhancement on effective PG segments (EPGSs) were evaluated. A new semi-automatic method was also proposed to extract EPGSs in an accurate and efficient way. Firstly, GF-2 images were preprocessed via atmospheric correction, orthographical correction, registration, fusion, linear compression, or spatial filtering, and, then, boundary-removed point samples with adjustable density were made based on reference polygons by taking advantage of the characteristics of chessboard segmentation. Subsequently, the point samples were used to quickly and accurately extract segments containing 70% or greater of PG pixels in each MRS result. Finally, the extracted EPGSs were compared and analyzed via intersection over union (IoU), over-segmentation index (OSI), under-segmentation index (USI), error index of total area (ETA), and composite error index (CEI). The experimental results show that, along with the change in control variables, the optimal scale parameter, time of segmentation, IoU, OSI, USI, and CEI all showed strong changing trends, with the values of ETA all close to 0. Furthermore, compared with the control group, all the CEIs of the EPGSs extracted from those corrected and enhanced images resulted in lower values, and an optimal CEI involved linearly compressing the DN value of the atmospheric-corrected fusion image to 0–255, and then using Fast Fourier Transform and a circular low-pass filter with a radius of 800 pixels to filter from the spatial frequency domain; in this case, the CEI had a minimum value of 0.159. The results of this study indicate that the 70% design in the experiment is a reasonable pixel ratio to determine the EPGSs, and the OSI-USI-ETA-CEI pattern can be more effective than IoU when it is needed to evaluate the quality of EPGSs. Moreover, taking into consideration heterogeneity and target characteristics, atmospheric correction and image enhancement prior to MRS can improve the quality of EPGSs.

Real-time batch inspection system for surface defects on circular optical filters.

Optical filters, one of the essential parts of many optical instruments, are used to select a specific radiation band of optical devices. There are specifications for the surface quality of the optical filter in order to ensure the instrument's regular operation. The traditional machine learning techniques for examining the optical filter surface quality mentioned in the current studies primarily rely on the manual extraction of feature data, which restricts their ability to detect optical filter surfaces with multiple defects. In order to solve the problems of low detection efficiency and poor detection accuracy caused by defects too minor and too numerous types of defects, this paper proposes a real-time batch optical filter surface quality inspection method based on deep learning and image processing techniques. The first part proposes an optical filter surface defect detection and identification method for seven typical defects. A deep learning model is trained for defect detection and recognition by constructing a dataset. The second part uses image processing techniques to locate the accurate position of the defect, determine whether the defect is located within the effective aperture, and analyze the critical eigenvalue data of the defect. The experimental results show that the method improves productivity and product quality and reduces the manual workload by 90%. The proposed model and method also compare the results of surface defect detection with the actual measurement data in the field, verifying that the method has good recognition accuracy while improving efficiency.

Polarization-based optical characterization for color texture analysis and segmentation

• Hand-crafted texture characterization based on light polarization signatures. • Image segmentation relying on pixel based circular polarization signature. • Multispectral image (185 bands) with five total variation planes. • The Karhunen-Loeve transform and then k-means were applied to the 185 bands. • Superior segmentation accuracy (compared with classic k-means on RGB). Texture characterization is very useful for automatic analysis of object surface images for a plethora of applications in medicine, agriculture, industry or remote sensing. Various texture characterization techniques exist, from the classical Haralick descriptors, Gabor filters, local binary patterns to automatically-extracted features using machine learning models. We propose a new hand-crafted texture characterization technique, based on light polarization property, by deploying a circular polarization filter (rotated from 0° to 360° in steps of 10°) in the image acquisition process. The hypothesis is that different materials and surfaces will exhibit different polarization signatures defined as pixel values variation as a function of polarization angle. Such polarization signature is able to locally characterize texture as a consequence of light reflections captured in every pixel due to the texture intrinsic variations. We show the usefulness of our approach for surface/material classification for the purpose of color image segmentation of natural outdoor scenes.

A 3D printed bullet‐shaped filter based on zero‐gap circular waveguide cavities

This paper presents an improved zero-gap waveguide structure with convex-shaped gap waveguide pins and zero height air gap, which is used to enhance the structural strength and reduce the assembly errors due to the air gap variation. A fourth-order bullet-shaped circular waveguide filter is designed and fabricated to validate the proposed structure. To ease the 3D printing difficulty of the designed filter, the cosine-shaped top is utilized for self-supporting. Besides, a metallic ridge between adjacent cavities is introduced to enhance the electric coupling. The metallic ridge as well as the slots at the bottom plate help to increase the stopband suppression. Finally, the measured results of the fabricated filter excited with coaxial connectors match well with the simulation.

Chiral Luminescent Liquid Crystal with Multi-State-Reversibility: Breakthrough in Advanced Anti-Counterfeiting Materials.

Creating a security material that carries distinct information in reflective color, fluorescence, and chiroptical property will enhance anti‐counterfeiting levels to deter counterfeits ranging from currencies to pharmaceuticals, but is proven extremely challenging. In this work, an advanced anti‐counterfeiting material, with three‐state of each mode reversibly converted into multi‐mode materials including reflective color, fluorescence, and circularly polarized luminescence signal, is constructed by loading photofluorochromic spiropyran (SP) and zinc ion (Zn2+) into the chiral liquid crystal. Under UV irradiation, the complexes of SP and Zn2+ will be transformed into merocyanine (MC) and MC‐Zn2+, while the energy transfer occurs from MC‐Zn2+ to MC. Upon heating, MC is easy to recover to SP, while the MC‐Zn2+ remains unchanged. The MC and MC‐Zn2+ can be transformed into the SP and Zn2+ under visible light irradiation. The three states of each mode can reversibly convert. Furthermore, the reflective color or fluorescence of each state shows different intensities under left‐ and right‐handed circular polarized filters, enabling easy distinguishing by naked eyes. The advanced anti‐counterfeiting method with multi‐state of each mode for multi‐mode encryption information output will provide a new concept for designing and fabricating multi‐mode anti‐counterfeiting materials, improving the security level for practical application.

Iris recognition based on local circular Gabor filters and multi-scale convolution feature fusion network

Iris recognition based on local circular Gabor filters and multi-scale convolution feature fusion network

CPU and GPU real-time filtering methods for dense surface metrology using general matrix to matrix multiplications

Filtering is a required task in surface metrology for the identification of the components relevant for automated quality control. The calculation of real-time features about the surface is crucial to determining the mechanical and physical properties of the inspected product. The computation efficiency of the filtering operations is a major challenge in surface metrology, as current sensors provide massive volumes of data at very high acquisition rates. To overcome the challenges, this work presents different real-time filtering solutions comparing the performance on the CPU and on the GPU, using modern hardware. The proposed framework is focused on filtering techniques that can be expressed using a finite impulse response (FIR) kernel that includes the Gaussian kernel, the most common filtering technique recommended by ISO and ASME standards. This research work proposes variations of the double FIFO and double circular filters. The filters are transformed into a series of general matrix to matrix multiplications, which can be run extremely efficiently on different architectures. The proposed filtering approach provides superior performance compared with previous works. Additionally, tests are carried out to quantify the performance of the GPU in terms of data transfer and computation capabilities in order to diminish the penalty imposed by data transfer from main memory to the GPU in real-time operations. Based on the results, an efficient batch filtering technique is proposed that can be run on the GPU faster than the CPU even for small profile and kernel sizes, offloading this task from the host CPU for optimal system and application response.

An Effective Framework for the Classification of Retinopathy Grade and Risk of Macular Edema for Diabetic Retinopathy Images

It is well know that for a diabetic patient, Diabetic Retinopathy (DR) is a speedy spreading infection which results in total loss of vision. Hence for diabetic patient, prior DR identification is important issue to protect eyes furthermore supportive for opportune treatment. The DR identification should be possible physically and could likewise distinguished consequently. In previous framework, assessment of fundus pictures of retina for checking the phonological variety in Micro Aneurysms (MA), exudates, hemorrhages, macula and veins is a drawn-out and lavish errand. However in the robotized framework, picture handling strategies can be utilized for before DR identification. Here, a framework for DR discovery is proposed. To start with, the information picture is pre-prepared utilizing crossover CLAHE and circular average filter round normal channel and veins are extricated by Coye Filter. A short time later, picture is exposed to irregularities division, where division of MA, hemorrhages, exudates, and neovascularization are conveyed. Almost 36 distinct highlights are removed from sectioned pictures. A half breed salp swarm-feline multitude advancement (CSO) calculation is used for choosing the appropriate highlights. At last, an arrangement is conveyed by changed RNN-LSTM. Three orders are conveyed, (I) Classification of kind of retinopathy, (ii) Classification of evaluation of retinopathy, (iii) Risk of Macular Edema (ME). The order correctness’s got are: 99.73% for kind of DR, 95.6% for NPDR grade and 99.4% for NPDR Macular Edema Risk, 92.3% for PDR Macular Edema Risk. Our simulation results reveals that with Decision Tree (DT) and Random Forest (RF) Algorithm, this framework provides better results in terms of accuracy of affectability and explicitness and Precision.

A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature.

Aircraft detection in remote sensing images (RSIs) has drawn widespread attention in recent years, which has been widely used in the military and civilian fields. While the complex background, variations of aircraft pose and size bring great difficulties to the effective detection. In this paper, we propose a novel aircraft target detection scheme based on small training samples. The scheme is coarse-to-fine, which consists of two main stages: region proposal and target identification. First, in the region proposal stage, a circular intensity filter, which is designed based on the characteristics of the aircraft target, can quickly locate the centers of multi-scale suspicious aircraft targets in the RSIs pyramid. Then the target regions can be extracted by adding bounding boxes. This step can get high-quality but few candidate regions. Second, in the stage of target identification, we proposed a novel rotation-invariant feature, which combines rotation-invariant histogram of oriented gradient and vector of locally aggregated descriptors (VLAD). The feature can characterize the aircraft target well by avoiding the impact of its rotation and can be effectively used to remove false alarms. Experiments are conducted on Remote Sensing Object Detection (RSOD) dataset to compare the proposed method with other advanced methods. The results show that the proposed method can quickly and accurately detect aircraft targets in RSIs and achieve a better performance.

Projection Filtering with Observed State Increments with Applications in Continuous-Time Circular Filtering.

Angular path integration is the ability of a system to estimate its own heading direction from potentially noisy angular velocity (or increment) observations. Non-probabilistic algorithms for angular path integration, which rely on a summation of these noisy increments, do not appropriately take into account the reliability of such observations, which is essential for appropriately weighing one's current heading direction estimate against incoming information. In a probabilistic setting, angular path integration can be formulated as a continuous-time nonlinear filtering problem (circular filtering) with observed state increments. The circular symmetry of heading direction makes this inference task inherently nonlinear, thereby precluding the use of popular inference algorithms such as Kalman filters, rendering the problem analytically inaccessible. Here, we derive an approximate solution to circular continuous-time filtering, which integrates state increment observations while maintaining a fixed representation through both state propagation and observational updates. Specifically, we extend the established projection-filtering method to account for observed state increments and apply this framework to the circular filtering problem. We further propose a generative model for continuous-time angular-valued direct observations of the hidden state, which we integrate seamlessly into the projection filter. Applying the resulting scheme to a model of probabilistic angular path integration, we derive an algorithm for circular filtering, which we term the circular Kalman filter. Importantly, this algorithm is analytically accessible, interpretable, and outperforms an alternative filter based on a Gaussian approximation.

Convolutional Neural Network Based on Diverse Gabor Filters for Deepfake Recognition

Media synthesis and manipulation has reached unprecedented levels of realism owing to the proliferation of deep learning. Deepfake has been the de-facto tool for media manipulation. Although this technology has potential in the entertainment industry, its threats include political manipulation and bypassing biometric security systems. As a result, deepfake detection has garnered widespread attention among research communities. The intuition is to use deep learning to fix the problems created by deep learning. Although convolutional neural networks have shown their dominance in the filed of pattern recognition, the receptive field-model size dilemma still persists along with the lack of interpretation for such models. While the traditional Gabor function was proposed to fix these problems, it can only generate limited linear Gabor filters which makes it optimal for limited data and applications. The contribution of this paper is quadruple: (i) proposing a unified Gabor function capable of generating linear, elliptical, and circular Gabor filters. (ii) leveraging the back-propagation learning framework to incorporate the proposed function in convolutional neural networks and generate adaptive Gabor filters. (iii) presenting a dual scale large receptive field network for deepfake image recognition. (iv) demonstrating where the proposed model stands in terms of performance and architecture size compared to state-of-the-art models. The proposed model is evaluated on four benchmark datasets: Celeb-DF (v2), DeepFake Detection Challenge Preview, FaceForensics++ and Wilddeepfake. Experimental results show that the proposed adaptive Gabor filters reduce the model size by 64.9% compared to adaptive weighted filters without performance reduction.

Automatic Detection of Subsidence Troughs in SAR Interferograms Using Mathematical Morphology

In this paper, an automatic algorithm for the detection of subsidence areas in SAR interferograms is proposed. It is based on the analysis of spatial distribution of the interferogram phase, and its coherence and entropy. The developed method was tested for differential interferograms generated on the basis of Sentinel-1 SAR images covering mining areas in South Poland. The obtained results were compared with those achieved using a method based on circular Gabor filters. Performed analysis revealed that the detection rate for the proposed method varied from 34% to 83%. It is an improved method based on Gabor filters that achieved a detection rate from 30% to 53%.

Performance Evaluation of a Field-Scale Anaerobic Baffled Reactor as an Economic and Sustainable Solution for Domestic Wastewater Treatment

The present study explored the efficiency of a four-chambered anaerobic baffled reactor (ABR) as a cost-effective and sustainable method of organic pollutant and pathogen removal from domestic wastewater, under a range of environmental conditions. An ABR with a circular additional filter at the outlet pipe was constructed to treat wastewater from a residential colony of 108 households with an average inflow of 110 m3/day and a nominal hydraulic retention time (HRT) of 20 h. Analysis of the chemical oxygen demand (COD), total nitrogen, sulfate and phosphate load, and total coliform removal for 2 years of operation, 2015 and 2017, showed a COD of 46%, sulfate load of 28%, phosphate load of 51% and total nitrogen of 28% for 2015, compared to a COD of 48%, sulfate load of 44%, phosphate load of 58% and total nitrogen of 31% for 2017. The lack of a significant effect of sludge removal suggested a stable process. The overall efficiency of the ABR increased in the summer, including for pathogen removal, which was significantly higher during the summer months of both years. Overall, the ABR was found to be able to consistently treat primary wastewater, although tertiary effluent treatment was still required before water reuse or final discharge.

Highly Efficient Anisotropic Chiral Plasmonic Metamaterials for Polarization Conversion and Detection.

Plasmonic chiral metamaterials have attracted broad research interest because of their potential applications in optical communication, biomedical diagnosis, polarization imaging, and circular dichroism spectroscopy. However, optical losses in plasmonic structures severely limit practical applications. Here, we present the design concept and experimental demonstration for highly efficient subwavelength-thick plasmonic chiral metamaterials with strong chirality. The proposed designs utilize plasmonic metasurfaces to control the phase and polarization of light and exploit anisotropic thin-film interference effects to enhance optical chirality while minimizing optical loss. Based on such design concepts, we demonstrated experimentally optical devices such as circular polarization filters with transmission efficiency up to 90% and extinction ratio >180, polarization converters with conversion efficiency up to 90%, as well as on-chip integrated microfilter arrays for full Stokes polarization detection with high accuracy over a broad wavelength range (3.5-5 μm). The proposed design concepts are applicable from near-infrared to Terahertz regions via structural engineering.

Synthesis design of dual‐path coupled line circular ring resonator filter with tunable characteristic impedances and transmission zeros

A dual-path coupled-line circular ring resonator filter synthesis is presented. The topology comprises identical coupled lines in series duplicated to form dual-path coupled-line interconnections. A bandpass response of nth order equal to (n/2) + 1, corresponds to controllable transmission zeros (TZs) found on both sides of the passband by varying the values of the even- and odd-mode impedances of the coupled lines, denoted Zoe and Zoo, respectively. Concerning the efficiency of controllable electrical characteristics of the resonator filter, such as in-band matching level, high selective response, and wide bandwidth, and fixing the position of the TZ frequencies, the proposed method using asymmetrical quarter-wavelength coupled lines in a circular ring resonator filter is superior to existing methods. Ultimately, the proposed bandpass filter is implemented on contrast dielectric substrate FR-4 and Taconic TRF-45 substrates using a planar form of microstrip technology to verify the synthesis equation. The measurement results indicate that the filter achieved more than 40% fractional bandwidth with excellent in-band matching levels and a controllable filter order. Both measurement and simulation results are in good agreement regarding return and insertion loss values, thus validating the proposed designs.

Application of a New Semi-Automatic Algorithm for the Detection of Subsidence Areas in SAR Images on the Example of the Upper Silesian Coal Basin

The article presents a new method of automatic detection of subsidence troughs caused by underground coal mining. Land subsidence that results from mining leads to considerable damage to subsurface and surface infrastructure such as walls of buildings, road surfaces, and water relations in built-up areas. Within next 30 years, all coal mines are to be closed as part of the transformation of the mining industry in Poland. However, this is not going to solve the problem of subsidence in those areas. Thus, it is necessary to detect and constantly monitor such hazards. One of the techniques used for that purpose is DInSAR (differential interferometry synthetic aperture radar). It makes it possible to monitor land deformation over large areas with high accuracy and very good spatial and temporal resolution. Subsidence, particularly related to mining, usually manifests itself in interferograms in the form of elliptical interferometric fringes. An important issue here is partial or full automation of the subsidence detection process, as manual analysis is time-consuming and unreliable. Most of the proposed trough detection methods (i.e., Hough transform, circlet transform, circular Gabor filters, template recognition) focus on the shape of the troughs. They fail, however, when the interferometric fringes do not have distinct elliptical shapes or are very noisy. The method presented in this article is based on the analysis of the variability of the phase value in a micro-area of a relatively high entropy. The algorithm was tested for differential interferograms form the Upper Silesian Coal Basin (southern Poland). Due to mining, the studied area is particularly prone to various types of subsidence.

Positive bias in particulate matter emissions data due to sulfur dioxide adsorption and oxidation on glass fiber filters

ABSTRACT Recent particulate matter emisssion tests at an ArcelorMittal sintering line in Vitoria, Brazil have indicated a change in particulate matter emissions that was not consistent with the steady and proper operation of the combined electrostatic precipitator and pulse jet fabric filter control system. ArcelorMittal Brazil, ERM Brasil, and Air Control Techniques, P.C. have evaulated the use of borosilicate glass fiber filters in the U.S. EPA Method 5 sampling train as a possible reason for the anomolous change. The scope of this study included (1) possible adsorption and oxidation of SO2 on borosilicate glass and quartz filter media, and (2) the possible loss of fibers from borosilicaate glass and quartz filter media at normal sampling train vacuums and temperatures. Air Control Techniques, P.C. conducted a series of laboratory tests replicating gas stream conditions using both borosilicate and quartz circular and thimble filters. The laboratory tests confirmed the formation of sulfate artifacts due to sulfur dioxide adsorption on the borosilicate filters. These tests also confirmed that neither the borosilicate glass-fiber filters or the quartz filter lost significant filter weight during sampling at normal Method 5 sampling train vacuum levels and filter temperatures. Tests conducted by ERM Brasil, SA on the ArcelorMittal sintering process stack using both quartz and borosilicate glass fibers also indicated significant adsorption and oxidation of sulfur dioxide on the borosilicate filters. These laboratory and field tests confirmed the positive bias associated with glass fiber filters in SO2 environments reported as early as 1970. The authors recommend that only quartz fiber filter be used for particulate matter testing in SO2-containing gas streams. Furthermore, EPA Method 5 Section 7.1.1 should be revised to explicitly approve the use of quartz fiber filters. Implications: Filterable particulate matter emission tests using EPA Method 5 in sulfur dioxide laden gas streams have had a large positive bias due to outdated sampling practices. Our manuscript provides bias quantification data from both laboratory tests and tests on a sintering process stack. We recommend that (1) particulate matter emission tests conducted in SO2-containing gas streams be conducted using only quartz filter and (2) The U.S. EPA change the definition of filter media to be used in EPA Method 5 and other particulate matter test methods based on Method 5 specifically allow the use of quartz filters.

Optical spatial filtering readout techniques for IR/THz imaging and their performance analysis

Optical spatial filtering readout techniques (OSFRTs), such as knife-edge filtering and circular hole filtering, are broadly used in optomechanical microcantilever focal plane arrays for infrared (IR)/terahertz (THz) imaging. In order to further improve the responsivity of IR/THz imaging, it is important to improve the optical readout responsivity (ORR). However, the shape and location of the optical spatial filter cannot be well optimized for lack of a unified theoretical ORR model of OSFRTs. This paper gives and experimentally validates a unified ORR model of OSFRTs. Based on this model, the influence of the shape and location of the four commonly used spatial filters on the imaging resolution and light utilization efficiency is discussed. Both theory and experiment show that the slit filter has higher optical readout responsivity than the knife-edge filter and better light utilization efficiency than the rectangular or circular hole filters. Therefore, the slit filter should be the best of the four commonly used filters.