Adaptive Background Research Articles

Enhancing defect detection in active infrared thermography using adaptive background suppression techniques

Enhancing defect detection in active infrared thermography using adaptive background suppression techniques

Unsupervised detection and mapping of sparks in the Electrochemical Discharge Machining (ECDM) process

Material removal in electrochemical discharge machining is caused by sparks generated in a tool immersed in an electrolytic solution. Being the primary machining agent in this non-contact machining process, mapping the locations of microscopic sparks is of great interest. The distribution of sparks around the tool surface could give insights into the machined hole properties like the size, surface finish, and depth as compared to the machining parameters such as applied voltage, tool size, rotation speed, and feed rate. This paper is focused on detecting sparks in photographs of the ECDM process captured using a high-speed camera. A novel approach of using a tri-planar reflective surface for capturing the location of sparks in 3D space using a 2D camera output is attempted. Traditional spark detection methods use neural network classifiers that need labeled data for training. This labeled data often comes from human intervention and contains inherent biases that could lead to misclassification. In this paper, an unsupervised spark detection methodology is demonstrated, which eliminates the need for human intervention and relies on the number of neighboring pixels detected in regions of interest (ROIs). The feasibility of using adaptive background modeling to classify thousands of images and identify the ones with sparks is demonstrated in this work. The masking technique combining effects of erosion followed by dilation is used to determine the exact boundaries of the spark contours in every image. Centroids for each of these contours are then transformed from the skewed coordinate system as observed in camera images, to a three-dimensional orthogonal coordinates system centered around the tool. The same procedure is repeated for various voltages to benchmark the distribution of sparks around a tool tip in an ECDM process.

Hyperspectral anomaly detection based on adaptive background dictionary construction and collaborative representation

ABSTRACT Collaborative representation-based (CR) methods have received widespread attention in hyperspectral anomaly detection, but the results are greatly affected by the quality of the background dictionary. Abnormal pixels and abnormal-mixed pixels in the background dictionary may affect the accuracy of linear representation and make its performance poor. To address the above problem, an adaptive background dictionary construction-based anomaly detection method is proposed. To ensure the purity of the background dictionary, abnormal pixels and abnormal-mixed pixels around each test pixel are removed adaptively through clustering and pure pixel extraction. Furthermore, the saliency weight of the test pixel is calculated through the pixels in the inner window and weighted into the linear representation process to improve the robustness of the method. Experimental results on four hyperspectral datasets show that the proposed method performs better than other CR methods and traditional detectors, and it can reduce the dependence of anomaly detection performance on dual window size.

Smart traffic management of vehicles using faster R-CNN based deep learning method

With constant growth of civilization and modernization of cities all across the world since past few centuries smart traffic management of vehicles is one of the most sorted after problem by research community. Smart traffic management basically involves segmentation of vehicles, estimation of traffic density and tracking of vehicles. The vehicle segmentation from videos helps realization of niche applications such as monitoring of speed and estimation of traffic. When occlusions, background with clutters and traffic with density variations, this problem becomes more intractable in nature. Keeping this motivation in this research work, we investigate Faster R-CNN based deep learning method towards segmentation of vehicles. This problem is addressed in four steps viz minimization with adaptive background model, Faster R-CNN based subnet operation, Faster R-CNN initial refinement and result optimization with extended topological active nets. The computational framework uses adaptive background modeling. It also addresses shadow and illumination issues. Higher segmentation accuracy is achieved through topological active net deformable models. The topological and extended topological active nets help to achieve stated deformations. Mesh deformation is achieved with minimization of energy. The segmentation accuracy is improved with modified version of extended topological active net. The experimental results demonstrate superiority of this framework with respect to other methods.

Genetic background of adaptation of Crimean-Congo haemorrhagic fever virus to the different tick hosts.

Crimean-Congo haemorrhagic fever orthonairovirus (CCHFV) is a negative-sense, single-stranded RNA virus with a segmented genome and the causative agent of a severe Crimean-Congo haemorrhagic fever (CCHF) disease. The virus is transmitted mainly by tick species in Hyalomma genus but other ticks such as representatives of genera Dermacentor and Rhipicephalus may also be involved in virus life cycle. To improve our understanding of CCHFV adaptation to its tick species, we compared nucleotide composition and codon usage patterns among the all CCHFV strains i) which sequences and other metadata as locality of collection and date of isolation are available in GenBank and ii) which were isolated from in-field collected tick species. These criteria fulfilled 70 sequences (24 coding for S, 23 for M, and 23 for L segment) of virus isolates originating from different representatives of Hyalomma and Rhipicephalus genera. Phylogenetic analyses confirmed that Hyalomma- and Rhipicephalus-originating CCHFV isolates belong to phylogenetically distinct CCHFV clades. Analyses of nucleotide composition among the Hyalomma- and Rhipicephalus-originating CCHFV isolates also showed significant differences, mainly in nucleotides located at the 3rd codon positions indicating changes in codon usage among these lineages. Analyses of codon adaptation index (CAI), effective number of codons (ENC), and other codon usage statistics revealed significant differences between Hyalomma- and Rhipicephalus-isolated CCHFV strains. Despite both sets of strains displayed a higher adaptation to use codons that are preferred by Hyalomma ticks than Rhipicephalus ticks, there were distinct codon usage preferences observed between the two tick species. These findings suggest that over the course of its long co-evolution with tick vectors, CCHFV has optimized its codon usage to efficiently utilize translational resources of Hyalomma species.

Investigation of radial basis function dynamic mesh method with rotation correction based on adaptive background mesh

Strategies to improve the quality, robustness, and efficiency of dynamic mesh deformation are investigated. Radial basis function (RBF) interpolation is used for the background mesh, and volume-weighted interpolation is utilized for the computational mesh. An adaptive background mesh with an octree structure is automatically generated. It is refined according to the resolution near the boundaries and the wall's interior is also covered by mesh which is of sufficient resolution and prevents a negative volume due to large deformations of complex configurations. Secondly, the RBF method based on adaptive background mesh, the RBF interpolation angle method based on adaptive background mesh, and the damping function-RBF interpolation angle method based on adaptive background mesh have been developed. This method is applicable to complex shapes with multiple gaps, ensuring a high-quality mesh and high computational efficiency for large deformations. The numerical validation of the interpolation rotation angles for different RBFs indicates that the CTPS C0 function is suitable for large gradients in the boundary deformation. The RBF interpolation angle method ensures a smoother deformation for rigid and large deformations. The damping function-RBF interpolation angle method ensures mesh orthogonality near the boundary during elastic deformation, improving the boundary layer's quality and deformation robustness. Several examples show that the proposed method improves the mesh quality by about 30% and increases the time consumption by about 10%. In addition, the methods are applicable to rigid/elastic deformation of objects with multiple boundaries.

OPTIMAL TRAFFIC CONTROL SYSTEM FOR TRAFFIC CONGESTION

A smart city's traffic management system is regarded as one of its primary components. Traffic jams are a common sight on the roadways in metropolitan areas due to the rapid increase in population and urban mobility. In order to address road traffic management issues and assist authorities with appropriate planning, an intelligent traffic management system utilizing the Yolo algorithm and Open CV approach is proposed in this project. A workable model for counting automobiles in traffic was developed using image processing as the basis. image processing methods classified and tallied moving vehicles in traffic scene video streams captured by stationary cameras. The following is the detection and tracking methodology. The adaptive background subtraction technique is initially used to separate the moving vehicles from the traffic scene. Using videos to isolated picture blobs are recognized as individual vehicles once the background is subtracted. Following blob identification, vehicles in a certain area are counted and classified. A count of vehicles was observed with an accuracy of ideal camera calibration. To support the goal, data is gathered from video footage of vehicles traveling toward and away from the camera in order to count and use signal switching. The created system's results demonstrate that, with more enhancements, it applicable to count and categorize vehicles in real-time. After that, an optimization framework makes use of these predictions to dynamically modify signal timings in response to shifting traffic conditions. The optimization method seeks to increase overall traffic flow efficiency, decrease delays, and shorten travel times. To sum up, utilizing real-time traffic data to optimize signal control presents a viable approach to improving urban traffic management. This method makes use yolo techniques to facilitate the creation of flexible and effective traffic control, which in turn helps to create more sustainable and seamless urban transportation networks. KEYWORDS: Image classification, Video tracking, Information analysis, Vehicle detection, Signal Switching.

Testing and modeling the possibility of using a lamellar grinding wheel with an adaptive background structure

In the article, the authors present the concept of a mathematical model used to study the influence of the properties of the base materials used to produce an innovative abrasive wheel background on the operating characteristics of the final tool. This model will be used to verify various material and construction solutions. The article also covers the outcomes of research that confirm the developed model’s effectiveness in relation to the operation of lamellar grinding discs, as per their intended technological goal. The experiments involved surface grinding with grinding wheels constructed on chosen backgrounds. A dedicated research stand was designed to measure the temperature in the grinding zone without direct contact, using both new and used grinding discs. The tests incorporated various pressure levels applied by the grinding disc on the workpiece, as well as different working angles of the grinding disc.

Multi-Modal Information Fusion for Localization of Emergency Vehicles

In urban and city environments, road transportation contributes significantly to the generation of substantial traffic. However, this surge in vehicles leads to complex issues, including hindered emergency vehicle movement due to high density and congestion. Scarcity of human personnel amplifies these challenges. As traffic conditions worsen, the need for automated solutions to manage emergency situations becomes more evident. Intelligent traffic monitoring can identify and prioritize emergency vehicles, potentially saving lives. However, categorizing emergency vehicles through visual analysis faces difficulties such as clutter, occlusions, and traffic variations. Visual-based techniques for vehicle detection rely on clear rear views, but this is problematic in dense traffic. In contrast, audio-based methods are resilient to the Doppler Effect from moving vehicles, but handling diverse background noises remains unexplored. Using acoustics for emergency vehicle localization presents challenges related to sensor range and real-world noise. Addressing these issues, this study introduces a novel solution: combining visual and audio data for enhanced detection and localization of emergency vehicles in road networks. Leveraging this multi-modal approach aims to bolster accuracy and robustness in emergency vehicle management. The proposed methodology consists of several key steps. The presence of an emergency vehicle is initially detected through the preprocessing of visual images, involving the removal of clutter and occlusions via an adaptive background model. Subsequently, a cell-wise classification strategy utilizing a customized Visual Geometry Group Network (VGGNet) deep learning model is employed to determine the presence of emergency vehicles within individual cells. To further reinforce the accuracy of emergency vehicle presence detection, the outcomes from the audio data analysis are integrated. This involves the extraction of spectral features from audio streams, followed by classification utilizing a support vector machine (SVM) model. The fusion of information derived from both visual and audio sources is utilized in the construction of a more comprehensive and refined traffic state map. This augmented map facilitates the effective management of emergency vehicle transit. In empirical evaluations, the proposed solution demonstrates its capability to mitigate challenges like visual clutter, occlusions, and variations in traffic density common issues encountered in traditional visual analysis methods. Notably, the proposed approach achieves an impressive accuracy rate of approximately 98.15% in the localization of emergency vehicles.

Intelligent Technique for Moving Object Detection from Problematic Video Captured through Camera Sensor

Aim: The significant aim of the proposed work is to develop an adaptive method to compute the threshold during run-time and update it adaptively for each pixel in the testing phase. It classifies motion-oriented pixels from the scene for moving objects using background subtraction and enhances using post-processing. Background: According to the huge demand for surveillance system, society is looking towards an intelligent video surveillance system that detect and track moving objects from video captured through a surveillance camera. So, it is very crucial and highly recommended throughout the globe in numerous domains such as video-based surveillance, healthcare, transportation, and many more. Practically, this research area faces lots of challenging issues such as illumination variation, cluttered background, camouflage, etc. So, this paper has developed an adaptive background subtraction method to handle such challenging problems. Objective: To focus and study the problematic video data captured through the camera sensor. To handle challenging issues available in real-time video scenes. To develop a background subtraction method and update the background model adaptively for moving object detection. Method: The proposed method has been accomplished using the following sections: Background model construction Automatic generation of threshold Background subtraction Maintenance of background model Results: The qualitative analysis of the proposed work is experimented with publicly available datasets and compared with considered state-of-the-art methods. In this work, library sequence (thermal data) of CDNET and other color video frame sequences Foreground aperture, Waving Tree and Camouflage are considered from Microsoft’s Wallflower. The quantitative values depicted in Table- 1. This work demonstrate the better performance of the proposed method as compared to state-ofthe- art methods. It also generates better outcomes and handles the problem of a dynamic environment and illumination variation. Conclusion: Currently, the world is demanding computer vision-based security and surveillancebased applications for society. This work has provided a method for the detection of moving information using an adaptive method of background subtraction approach for moving object detection in video scenes. The performance evaluation depicts better average results as compared to considered peer methods.

ИССЛЕДОВАНИЕ МИКРОЦИРКУЛЯЦИИ КРОВИ В УСЛОВИЯХ НЕВЕСОМОСТИ С ПОМОЩЬЮ ПОРТАТИВНЫХ ЛАЗЕРНЫХ ДОПЛЕРОВСКИХ ФЛОУМЕТРОВ

The paper reports the results of blood microcirculation studies in microgravity using portable laser analyzer LAZMAPF. Two cosmonauts (C-1 and C-2) performed pre-launch and in-flight (9 days) 8-minute sessions with the use of 2 portable flowmeters symmetrically attached to the temporal skin, palmer sides of distal phalanxes III, forearm superficies and planter surfaces of toe distal phalanxes I. In C-1, mean perfusion rate in toes decreased more than in half due to blood redistribution toward the upper body on the first two days in orbit and then, by way of adaptation, regained normal mean values. In C-2, compression of the proximal regions of lower extremities to induce blood deposition caused high-amplitude NO-dependent oscillations at about 1/min. On the background of adaptation during flight days 2–3, high-amplitude endothelial vasomotions in C-1 (one oscillation every 3–4 minutes) can be associated with an endothelial hyperpolarizing factor. In whole, on flight days 2–3 adaptation of the microcirculation system, particularly pronounced in the temporal and planter tissues, was characterized by a significant reduction of perfusion and increased vascular tone. Baseline prelaunch values were regained by flight day 6. For the first time, a research technique was proposed under microgravity conditions and data were obtained on the state of blood microcirculation of astronauts during the period of acute adaptation to microgravity conditions and readaptation after flight completion.

Factores sociodemográficos, audiológicos y audioprotésicos relacionados con la calidad de vida en pacientes con prótesis auditivas en Madrid (España)

Background and objectiveThe purpose of a prosthetic adaptation is not only to improve language understanding and sound recognition, but to improve the patient's quality of life.The purpose of this investigation is to measure quality of life of patients with hearing aids, and investigate which sociodemographic, audiological and audioprosthetic variables affect it. Material and methodsMulticenter cross-sectional descriptive study in the Community of Madrid (España). Sample 54 patients and Effectiveness of Auditory Rehabilitation (EAR) scale was used. ResultsMean age 66.4 years; 55.6% men. The mean score of 10 audiological EAR items was 64.54 (SD 16.43). The mean score of 10 ear audioprosthetic items was 71.84 (SD 13.49).Age, sex, work activity, level of education, degree of hearing loss, logoaudiometry results, free field, number and format of hearing aids, prescriber of the prostheses and year of manufacture of the prostheses, are associated with different items of the EAR scale (P<.05). ConclusionsThe audiological quality of life ranges from regular to good, and audioprosthetics is close to good.Younger age, female sex and work activity are associated with better quality of life. People with good logoaudiometric results are associated with better quality of life. Monaural adaptations, retroauricular hearing aids, more modern hearing aids and that the patient does not self-describe hearing aids, are associated with better quality of life.Hearing aids improve the quality of life of patients, provided that sociodemographic, audiological and audioprosthetic characteristics, specific to each patient are taken into account to make a personalized and optimal audioprosthetic adjustment.

Background-Aware Classification Activation Map for Weakly Supervised Object Localization.

Weakly supervised object localization (WSOL) relaxes the requirement of dense annotations for object localization by using image-level annotation to supervise the learning process. However, most WSOL methods only focus on forcing the object classifier to produce high activation score on object parts without considering the influence of background locations, causing excessive background activations and ill-pose background score searching. Based on this point, our work proposes a novel mechanism called the background-aware classification activation map (B-CAM) to add background awareness for WSOL training. Besides aggregating an object image-level feature for supervision, our B-CAM produces an additional background image-level feature to represent the pure-background sample. This additional feature can provide background cues for the object classifier to suppress the background activations on object localization maps. Moreover, our B-CAM also trained a background classifier with image-level annotation to produce adaptive background scores when determining the binary localization mask. Experiments indicate the effectiveness of the proposed B-CAM on four different types of WSOL benchmarks, including CUB-200, ILSVRC, OpenImages, and VOC2012 datasets.

A Hybrid of NHPP and Generalized Gaussian Mixture Model: A Combinatorial Approach for Background Elimination

For background removal, a software reliability model based on NHPP's combinatorial method and parametric modeling using the Chimp Optimization Algorithm (ChOA) and Generalized Gaussian Mixture Model is proposed in this paper. A novel combination of ChOA and GGMM algorithms with the NHPP strategy is devised to solve the major shortcomings of the original algorithms. The model is examined using the provided data set (COCO 2017). This methodology demonstrates how the suggested work more successfully recognizes background photos. The evaluation of the results is done by using several metrics like Accuracy, Recall, Precision, F-Score, Peak Signal Noise Ratio, and Mean Square Error. The outcomes are evaluated against several models based on frame differences, adaptive background removal, adaptive frame differences, and the Gaussian mixture model.

Co-occurrence spatial–temporal model for adaptive background initialization in high-dynamic complex scenes

Background information is an important aspect of pre-processing for advanced applications in computer vision. The literature has made rapid progress in background initialization. However, background initialization still suffers from high-dynamic complex scenes, such as illumination change, background motion, or camera jitter. Therefore, this study presents a novel Co-occurrence Spatial–Temporal (CoST) model for background initialization in high-dynamic complex scenes. CoST achieves a spatial–temporal model through a co-occurrence pixel-block structure. The proposed approach extracts the spatial–temporal information of pixels to self-adaptively generate the background without the influence of high-dynamic complex scenes. The efficiency of CoST is verified through experimental results compared with state-of-the-art algorithms. The source code of CoST is available online at: https://github.com/HelloMrDeng/CoST.git.

Effective Moving Object Tracking Using Adaptive Background Subtraction with Advanced Probability Evolutionary Algorithm

Tracking moving objects is a very difficult task for many computers. The task of tracking is a moving object. There are many virtual applications such as video surveillance and object recognition. Many ideas have been suggested for moving objects tracking and detection. One of the main problems solving tracking is finding identical objects in different frames. In this article, this application mainly focuses on the detection and tracking of moving objects using adaptive Background subtraction with adaptive probability evolutionary algorithm (AREA) method. Normal probability evolutionary algorithm will track only human motion but our algorithm tracks all the moving objects very accurately. Our proposed system shows the tracked objects with the red mark border.

A world of reindeer: A technological and functional reassessment of the Last Glacial Maximum osseous industry from Cotu Miculinți (northeastern Romania)

Processing of osseous materials was an important component of the repertoire of technological innovations throughout the Palaeolithic. The study of hard animal materials assemblages therefore provides crucial information regarding hominin adaptational ranges and cultural evolution. In contrast to the wide array of studies published on this topic in western and central Europe, the Palaeolithic osseous industries from Romania received comparatively limited attention. The organic industry recovered from Cotu Miculinți, a Late Glacial Maximum (LGM) site located in the Prut Valley (northeastern Romania), provides a case at point. The present study proposes a throughout reassessment of the collection of osseous artifacts from this site, covering several key aspects – raw materials, chaîne opératoire, and the functionality of the assemblage – in an attempt of setting the site’s osseous industry into the wider context of LGM cultural adaptations in the area east of the Carpathians.The results of our analysis reveal an assemblage predominantly made of reindeer antler and document a standardization of the technical transformation scheme, by use of both segmentation and extraction. This resulted in the production of two types of blanks: segmentation produced massive pieces that were transformed into hammers or perforated batons, and extraction by grooving led to standardized rods on flat blanks used to produce barbed points or smoothers. The finished pieces were used in domestic activities (processing of hides, bark, and wood) and for hunting. The assemblage of osseous artifacts from Cotu Miculinți displays many features in common with contemporaneous sites in the area, in particular Cosăuți (Moldova), which is strongly indicative for a similar cultural and adaptive background.

A Deep-Learning Based Pipeline for Estimating the Abundance and Size of Aquatic Organisms in an Unconstrained Underwater Environment from Continuously Captured Stereo Video

The utilization of stationary underwater cameras is a modern and well-adapted approach to provide a continuous and cost-effective long-term solution to monitor underwater habitats of particular interest. A common goal of such monitoring systems is to gain better insight into the dynamics and condition of populations of various marine organisms, such as migratory or commercially relevant fish taxa. This paper describes a complete processing pipeline to automatically determine the abundance, type and estimate the size of biological taxa from stereoscopic video data captured by the stereo camera of a stationary Underwater Fish Observatory (UFO). A calibration of the recording system was carried out in situ and, afterward, validated using the synchronously recorded sonar data. The video data were recorded continuously for nearly one year in the Kiel Fjord, an inlet of the Baltic Sea in northern Germany. It shows underwater organisms in their natural behavior, as passive low-light cameras were used instead of active lighting to dampen attraction effects and allow for the least invasive recording possible. The recorded raw data are pre-filtered by an adaptive background estimation to extract sequences with activity, which are then processed by a deep detection network, i.e., Yolov5. This provides the location and type of organisms detected in each video frame of both cameras, which are used to calculate stereo correspondences following a basic matching scheme. In a subsequent step, the size and distance of the depicted organisms are approximated using the corner coordinates of the matched bounding boxes. The Yolov5 model employed in this study was trained on a novel dataset comprising 73,144 images and 92,899 bounding box annotations for 10 categories of marine animals. The model achieved a mean detection accuracy of 92.4%, a mean average precision (mAP) of 94.8% and an F1 score of 93%.

In vivo active-targeting fluorescence molecular imaging with adaptive background fluorescence subtraction.

Using active tumor-targeting nanoparticles, fluorescence imaging can provide highly sensitive and specific tumor detection, and precisely guide radiation in translational radiotherapy study. However, the inevitable presence of non-specific nanoparticle uptake throughout the body can result in high levels of heterogeneous background fluorescence, which limits the detection sensitivity of fluorescence imaging and further complicates the early detection of small cancers. In this study, background fluorescence emanating from the baseline fluorophores was estimated from the distribution of excitation light transmitting through tissues, by using linear mean square error estimation. An adaptive masked-based background subtraction strategy was then implemented to selectively refine the background fluorescence subtraction. First, an in vivo experiment was performed on a mouse intratumorally injected with passively targeted fluorescent nanoparticles, to validate the reliability and robustness of the proposed method in a stringent situation wherein the target fluorescence was overlapped with the strong background. Then, we conducted in vivo studies on 10 mice which were inoculated with orthotopic breast tumors and intravenously injected with actively targeted fluorescent nanoparticles. Results demonstrated that active targeting combined with the proposed background subtraction method synergistically increased the accuracy of fluorescence molecular imaging, affording sensitive tumor detection.

The Landscape and Therapeutic Targeting of BRCA1, BRCA2 and Other DNA Damage Response Genes in Pancreatic Cancer.

Genes participating in the cellular response to damaged DNA have an important function to protect genetic information from alterations due to extrinsic and intrinsic cellular insults. In cancer cells, alterations in these genes are a source of genetic instability, which is advantageous for cancer progression by providing background for adaptation to adverse environments and attack by the immune system. Mutations in BRCA1 and BRCA2 genes have been known for decades to predispose to familial breast and ovarian cancers, and, more recently, prostate and pancreatic cancers have been added to the constellation of cancers that show increased prevalence in these families. Cancers associated with these genetic syndromes are currently treated with PARP inhibitors based on the exquisite sensitivity of cells lacking BRCA1 or BRCA2 function to inhibition of the PARP enzyme. In contrast, the sensitivity of pancreatic cancers with somatic BRCA1 and BRCA2 mutations and with mutations in other homologous recombination (HR) repair genes to PARP inhibitors is less established and the subject of ongoing investigations. This paper reviews the prevalence of pancreatic cancers with HR gene defects and treatment of pancreatic cancer patients with defects in HR with PARP inhibitors and other drugs in development that target these molecular defects.