Background Subtraction Process Research Articles

Automated brightfield layerwise evaluation in three-dimensional micropatterning via two-photon polymerization.

Two-photon polymerization (TPP) is an advanced 3D fabrication technique capable of creating features with submicron precision. A primary challenge in TPP lies in the facile and accurate characterization of fabrication quality, particularly for structures possessing complex internal features. In this study, we introduce an automated brightfield layerwise evaluation technique that enables a simple-to-implement approach for in situ monitoring and quality assessment of TPP-fabricated structures. Our approach relies on sequentially acquired brightfield images during the TPP writing process and using background subtraction and image processing to extract layered spatial features. We experimentally validate our method by printing a fibrous tissue scaffold and successfully achieve an overall system-adjusted fidelity of 87.5% in situ. Our method is readily adaptable in most TPP systems and can potentially facilitate high-quality TPP manufacturing of sophisticated microstructures.

Klasifikasi Citra Mengkudu Berdasarkan Perhitungan Jarak Piksel pada Algoritma K-Nearest Neighbour

Noni fruit is included in exported food commodities in Indonesia. The size of noni fruit, based on human vision, generally has varied shapes with distinctive textures and various patterns, so that the process of filtering fruit based on color and shape can be done in large quantities. In this study, K-Nearest Neighbor (KNN) has been implemented as a classification algorithm because it has advantages in classifying images and is resistant to noise. Noni imagery is a personal image taken from a noni garden in the morning and undergoes a background subtraction process. The imagery quality improvement technique uses the Hue Saturation Value (HSV) color feature and the Gray Level Co-Occurrence Matrix (GLCM) characteristic feature. KNN accuracy without features is lower than using HSV and GLCM features. From the experimental results, the highest accuracy was obtained using HSV-GLCM at K is 1 and d is 1, namely 95%, while the lowest accuracy was 55% using KNN only at K is 5 and d is 8.

Detection and Characterization of In Vitro Payload-Containing Catabolites of Noncleavable Antibody-Drug Conjugates by High-Resolution Mass Spectrometry and Multiple Data Mining Tools.

The formation and accumulation of payload-containing catabolites (PCCs) from a noncleavable antibody-drug conjugate (ADC) in targeted and normal tissues are directly associated with the therapeutic effect and toxicity of the ADC, respectively. Understanding the PCC formation is important for supporting the payload design and facilitating preclinical evaluation of ADCs. However, detection and identification of PCCs of a noncleavable ADC are challenging due to their low concentrations and unknown structures. The main objective of this study was to develop and apply a generic liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method for profiling PCCs in vitro. Noncleavable ADCs, ado-trastuzumab emtansine (T-DM1) and ADC-1, were incubated in liver lysosomes, liver S9, and/or cancer cells followed by data acquisition using LC-HRMS. Profiling PCCs mainly relied on processing LC-HRMS datasets using untargeted precise and thorough background subtraction (PATBS) processing and targeted product ion filtering (PIF). As a result, 12 PCCs of T-DM1 were detected and structurally characterized in human liver lysosomal incubation, a majority of which consisted of 4-[N-maleimidomethyl]cyclohexane-1-carboxylate (MCC)-DM1 and a few amino acids. Additionally, the incubation of ADC-1 in human, rat, and monkey liver S9 and cancer cells generated one major and three very minor PCCs, verifying the payload design. The results demonstrate that PATBS enabled the comprehensive profiling of PCCs regardless of their molecular weights, charge states, and fragmentations. As a complementary tool, PIF detected specific PCCs with superior sensitivity. The combination of the in vitro metabolism systems and the LC-HRMS method is a useful approach to profiling in vitro PCCs of noncleavable ADCs in support of drug discovery programs. SIGNIFICANCE STATEMENT: Profiling in vitro payload-containing catabolites (PCCs) of a noncleavable antibody-drug conjugate (ADC) is important for optimization of the payload design and preclinical evaluation of ADC. However, currently used analytical approaches often fail to quickly provide reliable PCC profiling results. The work introduces a new liquid chromatography high resolution mass spectrometry method for comprehensive and rapid detection and characterization of PCCs released from a noncleavable ADC in liver lysosomes and S9 incubations.

An Accurate Quantitative X-ray Photoelectron Spectroscopy Study of Pure and Homogeneous ZrN Thin Films Deposited Using BPDMS

A quantitative X-ray Photoelectron Spectroscopy (XPS) study has been undertaken on different experimental data sets of ZrN thin films deposited using reactive Bipolar Pulsed Dual-Magnetron Sputtering (BPDMS) on silicon/stainless steel substates, to obtain dense, pure and homogeneous coatings, free from morphological defects. Zirconium nitride (ZrN) occupies a central role within the class of transition metal nitrides (TMN) for its excellent properties, such as high hardness, low resistivity and chemical/thermal stability when its stoichiometric ratio is 1:1. Many deposition techniques, reported in the literature, tried to obtain oxygen-free and defect-free structures, but they proved a hard task. In this paper it has been demonstrated, using quantitative XPS, that stoichiometric, pure and homogeneous ZrN films have been grown at certain deposition conditions, optimized also via optional accessories mounted on the deposition apparatus. Almost all the films considered for microanalytical characterization resulted as completely oxygen-free, pure (with a lowest-detection limit of 1%) and homogeneous. Apart from these features, a stoichiometric ratio (N/Zr) close to one was calculated for six samples of the ten investigated, with a precision of ± 0.01. In this frame XPS, widely known for being a highly surface-sensitive technique (average depth resolution of 20–30 Å), and powerful for characterizing the chemical composition of materials, has been extensively employed to extract information both in the surface regions and in depth. A cluster ion beam Ar+ 2500 facility on our main XPS chamber has not proved adequate for depth-profiling acquisitions. Therefore, Ar+ ion sputtering was performed instead. To the best of our best knowledge, the results achieved in the present paper possess a level of accuracy never reached before. Rigorous calibration procedures before and during experimental spectrum acquisitions and a careful and scrupulous data processing using software CasaXps v.2.3.24PR1 were carried out to ensure a low percentage error. Progress has also been made for shake-up satellite extraction and interpretation from Zr 3d high-resolution spectra with the help of the literature milestones reported in the text. The total absence of oxygen inside most of the films prevented the formation of zirconium oxide compounds during deposition, which are generally resonant with the binding energy of the shake-up satellite peaks and hide them. A little summary about the experimental shake-up satellite peaks revealed and extracted from the Zr 3d region, after Shirley background subtraction and data processing, will be presented in the last subparagraph of the “Results” section for the ZrN samples analyzed. Figures of Zr 3d deconvoluted spectra for in-depth area analysis have been reported. The quantitative satellite contribution to the Zr 3d total area would not be included in stoichiometric calculations.

The LAMA system: A “smart” magnetometer network for harbour protection

This work describes the development of an underwater anti-intrusion system based on a magnetometer self-informed network, whose purpose is to detect the presence of threats in the proximity of critical infrastructures (e.g, terrorist divers in harbours). In this context, the magnetic network fills the gaps of sonar systems at the critical boundaries of the water volume to be controlled (sea bed, docks, …), where acoustic performances deteriorate due to reflections and attenuations. The system operates in a port-protection scenario, characterized by a medium-high environmental magnetic noise that can hide the diver signal (a diver is a weak, quasi-point-like, moving source). The magnetometer network processes two inputs: the environmental magnetic noise and a signal including the target magnetic signal superimposed to the same noise; the frequencies of a diver signal lie within the noise band, hence frequency filtering proves inadequate for noise removal. The basic idea underlying the system is to measure and use the noise itself to filter the overall signal; measuring noise supports a background-subtraction process that allows to extract the target signal and therefore detect the threat presence. The effectiveness of the procedure depends on the positions of magnetometers: sensors must be close enough to one another to measure the common background noise, and, at the same time, should be distant enough from one another so that just one sensor can measure the target signal. To generate alarms when a threat is detected, a real-time software application processes data and activates a visual and acoustic alarm upon identification of a magnetic anomaly. Sea trials carried out in port areas provided extremely satisfactory results in the detection of intruders. The paper presents experimental results obtained during the method validation tests, when intruders were moving in the surrounding undersea environment.

Intelligent Deep Learning Based Automated Fish Detection Model for UWSN

An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces. It has paved the way for new opportunities that can address questions relevant to diversity, uniqueness, and difficulty of marine life. Underwater Wireless Sensor Networks (UWSNs) are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions. In this scenario, it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies. Several models have been developed earlier for fish detection. However, they lack robustness to accommodate considerable differences in scenes owing to poor luminosity, fish orientation, structure of seabed, aquatic plant movement in the background and distinctive shapes and texture of fishes from different genus. With this motivation, the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN, named IDLAFD-UWSN model. The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos, particularly in blurred and crowded environments. IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network (Mask RCNN) with Capsule Network as a baseline model for fish detection. Besides, in order to train Mask RCNN, background subtraction process using Gaussian Mixture Model (GMM) model is applied. This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions. Finally, Wavelet Kernel Extreme Learning Machine (WKELM) model is utilized as a classifier model. The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98% and 97% on the applied blurred and crowded datasets respectively.

Real-time motion detection with candidate masks and region growing for moving cameras

A fast and reliable motion detection method is proposed for moving cameras. Existing methods generally build background models and then apply background subtraction for moving pixels detection. In background modeling, statistical methods and/or features, such as edges, local binary patterns are used. Parameters of the proposed methods need to be updated adaptively for better performance. In recent studies, adaptive learning rates are used with main and candidate background models. Simple and adaptive thresholding methods are used for foreground detection in different studies. We build a statistical background model with hue–saturation–value color space and perform background subtraction with the contribution of optical flow vectors. Dense optical flow is applied between warped the background model and the current frame to estimate optical flow vectors. If a pixel has a larger magnitude, we set a weight value for each pixel in the background subtraction process. Then, we extract two candidate foreground masks and apply some postprocessing techniques to determine the final mask. Our proposed method works fast in real time, and the experimental results on two different datasets show that our approach has the highest performance compared with the methods in the literature.

Multimedia Concepts on Object Detection and Recognition with F1 Car Simulation Using Convolutional Layers

This paper presents a framework for detecting objects in images based on global features and contours. The first step is a shape matching algorithm that uses the background subtraction process. Object detection is accomplished by an examination of the oversegmentation of the image, where the space of the potential boundary of the object is examined to identify boundaries that have a direct resemblance to the prototype of the object type to be detected. Our analysis method removes edges using bilinear interpolation and reestablishes color sensors as lines and retracts background lines from the previous frame. Object contours are generated with clustered lines. The objects detected will then be recognized using the extraction technique. Here, we analyze the color and shape characteristics with which each object is capable of managing occlusion and interference. As an extension of object detection and recognition, F1 car simulation is experimented with simulation using various layers, such as layer drops, convolutionary layers, and boundary elimination, avoiding obstacles in different pathways.

An Efficient Dimension Reduction based Fusion of CNN and SVM Model for Detection of Abnormal Incident in Video Surveillance

Performing dimensionality reduction in the camera captured images without any loss is remaining as a big challenge in image processing domain. Generally, camera surveillance system is consuming more volume to store video files in the memory. The normally used video stream will not be sufficient for all the sectors. The abnormal conditions should be analyzed carefully for identifying any crime or mistakes in any type of industries, companies, shops, etc. In order to make it comfortable to analyze the video surveillance within a short time period, the storage of abnormal conditions of the video pictures plays a very significant role. Searching unusual events in a day can be incorporated into the existing model, which will be considered as a supreme benefit of the proposed model. The massive video stream is compressed in preprocessing the proposed learning method is the key of our proposed algorithm. The proposed efficient deep learning framework is based on intelligent anomaly detection in video surveillance in a continuous manner and it is used to reduce the time complexity. The dimensionality reduction of the video captured images has been done by preprocessing the learning process. The proposed pre-trained model is used to reduce the dimension of the extracted image features in a sequence of video frames that remain as the valuable and anomalous events in the frame. The selection of special features from each frame of the video and background subtraction process can reduce the dimension in the framework. The proposed method is a combination of CNN and SVM architecture for the detection of abnormal conditions at video surveillance with the help of an image classification procedure. This research article compares various methods such as background subtraction (BS), temporal feature extraction (TFE), and single classifier classification methods.

A Fully Residual Convolutional Neural Network for Background Subtraction

Background subtraction is an important step involved in solving computer vision problems. This paper proposes a novel background subtraction method with fully residual convolutional neural network (FR-CNN). This fully residual connection helps to fuse the fine scale and coarse scale feature information efficiently. The extracted non-handcrafted features are robust and promisingly efficient compared to the handcrafted features. Furthermore, the method uses temporal and spatial information for the background subtraction process. The optical flow image is used for extracting the temporal information. Additionally, a new background modeling technique is also proposed for the efficient background subtraction. The model is trained using the randomly selected 50 frames from each video sequence of the CDnet-2014 dataset and the FR-CNN model is evaluated by CDnet-2014 dataset. The results shown from the qualitative and quantitative analyses reveal that the FR-CNN model outperforms the state-of-the-art methods.

Fast background subtraction with adaptive block learning using expectation value suitable for real-time moving object detection

This paper presents a method of moving object detection through a fast background subtraction technique suitable for real-time performance in wide range of platforms. An intermittent background update using adaptive blocks individually calculates the learning rate through expected difference values. Then, coupled with a fast background subtraction process, the design achieves fast throughput with well-rounded performance. To compensate for the lagging effects of intermittent background update, an adaptation bias is devised to improve precision and recall metrics. Experiments show a versatile performance in varying scenes with overall results better than conventional techniques. The proposed method achieved a fast execution speed of up to 56 fps in PC using Full HD video. It also achieved 655 fps and 83 fps in PC and ARM core-embedded platform, respectively, using the minimum input resolution of 320 × 240. Overall, it is suitable for real-time performance applications.

Visual Attention and Background Subtraction With Adaptive Weight for Hyperspectral Anomaly Detection

Anomaly detection (AD) in hyperspectral target detection is of particular interest because no prior knowledge of ground object spectra is required. However, it is difficult to utilize the salient features of hyperspectral image (HSI) and mitigate the effects of noise in hyperspectral AD, which greatly limits the detection performance. Here, we report a strategy to implement hyperspectral AD by visual attention model and background subtraction with adaptive weight. Through band selection method, the most discriminating bands are selected as the input images for subsequent processing. Then, the hyperspectral visual attention model is introduced, for the first time, into hyperspectral AD for extracting the salient feature map of the input images. Furthermore, the background subtraction process that can reduce the background and noise in the salient feature map is developed via curvature filter. Using this operation, the initial anomaly area map is obtained. Finally, incorporating with the spectral information, an adaptive weight map is applied to the initial anomaly area map to further suppress the background. In the experiment, the proposed method is compared with seven other state-of-the-art methods on synthetic and real-world HSI. Most importantly, the results demonstrate that the proposed method is effective and performs better than alternative methods. We believe that this method can open a new avenue of visual processing methods for hyperspectral AD.

Soft x-ray spectrum unfold of K-edge filtered x-ray diode arrays using cubic splines.

Cubic spline interpolation is able to recover temporally and spectrally resolved soft x-ray fluxes from an array of K-edge filtered x-ray diodes without the need for a priori assumptions about the spectrum or the geometry of the emitting volume. The mathematics of the cubic spline interpolation is discussed in detail. The analytic nature of the cubic spline solution allows for analytical error propagation, and the method of calculating the error for radiation temperature, spectral power, and confidence intervals of the unfolded spectrally resolved flux is explained. An unfold of a blackbody model demonstrates the accuracy of the cubic spline unfold. Tests of cubic spline performance using spectrally convolved detailed atomic model simulation results have been performed to measure the method's ability to conserve spectral power to within a factor of 2 or better in line-dominated regimes. The unfold is also demonstrated to work when information from the x-ray diode array is limited due to high signal-to-noise ratios or the lack of signal due to over-attenuation or over-filtration of the x-ray diode signal. The robustness of the unfold with respect to background subtraction and raw signal processing, signal alignment between diode traces, limited signal information, and initial conditions is discussed. Results from an example analysis of a halfraum drive are presented to demonstrate the capabilities of the unfold in comparison with previously established methods.

High-speed imaging of defects in thin plate by scanning elastic wave source technique using an airborne ultrasound phased array

We proposed high-speed visualization of defects by guided waves using an airborne ultrasound phased array (AUPA). The AUPA can move the focal point of the acoustic wave at high-speed by electronic control. By applying this sound source to the elastic wave source scanning technique, the measurement time can be significantly reduced. This report demonstrated the proposed method to visualize thin metal plate defects. The results confirmed that the defect can be visualized by the amplitude peak distribution of the propagating guided waves. In addition, it was confirmed that applying background subtraction processing to the imaging results allows the defects to be more clearly visualized.

A Universal Foreground Segmentation Technique using Deep-Neural Network

Background subtraction is generally used for foreground segmentation (moving object detection) from video sequences. Several background subtraction methods have been proposed for visual surveillance applications. However, the existing methods fail in case of real-surveillance challenges such as camouflage, sudden illumination variation, hard shadow, camera-jitter, non-static background, etc. A deep-neural network based background subtraction model is presented for flexible foreground segmentation. In addition to background subtraction model, a novel background modeling technique is also proposed for flexible background subtraction process. The presented deep-neural network architecture performs the background subtraction operation using the non-handcrafted features. The proposed method uses optical-flow details to make use of temporal information. This temporal information and spatial information (from the background image and current processing frame) are used for the training purpose. The model is trained using randomly selected images and its ground truth images from CDnet-2014 dataset. The presented model is evaluated using CDnet-2014 dataset, and it gives significant results compared to the existing background subtraction methods in terms of qualitative and quantitative analyzes.

Counting the Number of Active Spermatozoa Movements Using Improvement Adaptive Background Learning Algorithm

The most important early stage in sperm infertility research is the detection of sperm objects. The success rate in separating sperm objects from semen fluid has an important role for further analysis. This research performed the detection and calculation of human spermatozoa. The detected sperm was the moving sperm in the video data. An improvement of Adaptive Background Learning was applied to detect the moving sperm. The purpose of this method is to improve the performance of Adaptive Background Learning algorithm in background subtraction process to detect and calculate moving sperm on the microscopic video of sperm fluid. This paper also compared several other background subtraction algorithms to conclude the appropriate background subtraction algorithm for sperm detection and sperm counting. The process done in this research was preprocessing using the Gaussian filter. The next was background subtraction process, followed by morphology operation. To test or validate the detection results of any background subtraction algorithm used, the foreground mask results from the morphological operation were compared to the ground truth of moving sperm image. For visualization purposes, every BLOB area (white object in binary image) on the foreground were given a bounding box to the original frame and the number of BLOB objects present in the foreground mask were counted. This shows that the system had been able to detect and calculate moving sperm. Based on the test results, Adaptive Background Learning method had a value of F-measure of 0.9205 and succeeded in extracting sperm shape close to the original form compared to other methods.

Anisotropic Sophisticated Spatio-Temporal Contours Based Deep Neural Learned Moving Objects Detection in Video

Object detection in the video sequence is a significant problem to be resolved in image processing because it used different applications in video compression, video surveillance, robot technology, etc. Few research works have been designed in conventional works to discover moving objects using various machine learning techniques. However, dynamic changing background, object size variations and degradation of video frames during the object detection process remained an open issue. In order to overcome such limitations, Anisotropic Sophisticated Spatiotemporal Contours based Deep Neural Network Learning (ASSC-DNNL) practice is projected. ASSC-DNL Technique initially obtains a number of video file as input at the input layer. After acquiring the video, input layer forward it to hidden layers. Subsequently, ASSC-DNL Technique accomplishes the encoding process in the first hidden layer using Anisotropic Stacked Autoencoder (ASA). During the encoding process, ASSC-DNL practice maps each video frames pixels in input video via code. This practice results in compressed video with enhanced quality. Afterward, ASSC-DNL practice transforms compressed video into a numeral of frames in the second concealed layer. Followed by, ASSC-DNL practice carried out Teknomo–Fernandez Spatiotemporal Based Background Subtraction (TS-BS) process at the third hidden layer, in which it effectively segments the foreground images from dynamic changing background. Then, ASSC-DNL practice deep analyzes the foreground image of video frames and mines some features like shape, color, texture, intensity, and size. Finally, ASSC-DNL Technique exactly finds the moving objects in video frames according to identified features with minimal time at the output layer. Therefore, ASSC-DNL Technique obtains enhanced moving objects detection performance when compared to existing works. The simulation of ASSC-DNL practice is conducted via different metrics such as accuracy, time and false positive rate towards in detection.

Detection of Moving Object in Dynamic Visual Sequences Based on Partial Least Squares Classifier.

Detection of moving object from a visual sequence plays a vital role for the tracking of object. The main objective of this proposed work is to detect and classify the various video sequences with the help of different classification algorithms. The input video sequences from the publicly available datasets are collected and the individual frames are extracted. These frames are pre-processed and then applied to the novel background subtraction process. Important features based on the Local Binary Pattern (LBP) and grey level co-efficient are extracted. Finally these features are classified by three different classifiers like SVM, PLS, and PNN. The performance of these different classifiers are evaluated and compared. It is found that PLS classifier produces more classification accuracy but with more computation time.

Foreground Gating and Background Refining Network for Surveillance Object Detection.

Detecting objects in surveillance videos is an important problem due to its wide applications in traffic control and public security. Existing methods tend to face performance degradation because of false positive or misalignment problems. We propose a novel framework, namely, Foreground Gating and Background Refining Network (FG-BR Net), for surveillance object detection (SOD). To reduce false positives in background regions, which is a critical problem in SOD, we introduce a new module that first subtracts the background of a video sequence and then generates high-quality region proposals. Unlike previous background subtraction methods that may wrongly remove the static foreground objects in a frame, a feedback connection from detection results to background subtraction process is proposed in our model to distill both static and moving objects in surveillance videos. Furthermore, we introduce another module, namely, the background refining stage, to refine the detection results with more accurate localizations. Pairwise non-local operations are adopted to cope with the misalignments between the features of original and background frames. Extensive experiments on real-world traffic surveillance benchmarks demonstrate the competitive performance of the proposed FG-BR Net. In particular, FG-BR Net ranks on the top among all the methods on hard and sunny subsets of the UA-DETRAC detection dataset, without any bells and whistles.

Exact background subtraction processing technique in RCS measurement in time-variant environment

Exact background subtraction processing technique in RCS measurement in time-variant environment