Background Separation Research Articles

Optimizing ExoMars Rover Remote Sensing Multispectral Science II: Choosing and Using Multispectral Filters for Dynamic Planetary Surface Exploration With Linear Discriminant Analysis

AbstractIn this paper we address two problems associated with data‐limited dynamic spacecraft exploration: data‐prioritization for transmission, and data‐reduction for interpretation, in the context of ESA ExoMars rover multispectral imaging. We present and explore a strategy for selecting and combining subsets of spectral channels captured from the ExoMars Panoramic Camera, and attempt to seek hematite against a background of phyllosilicates and basalts as a test case scenario, anticipated from orbital studies of the rover landing site. We compute all available dimension reductions on the material reflectance spectra afforded by 4 spectral parameter types, and consider all possible paired combinations of these. We then find the optimal linear combination of each pair whilst evaluating the resultant target‐vs.‐background separation in terms of the Fisher Ratio and classification accuracy, using Linear Discriminant Analysis. We find ∼50,000 spectral parameter combinations with a classification accuracy >95% that use 6‐or‐less filters, and that the highest accuracy score is 99.6% using 6 filters, but that an accuracy of >99% can still be achieved with 2 filters. We find that when the more computationally efficient Fisher Ratio is used to rank the combinations, the highest accuracy is 99.1% using 4 filters, and 95.1% when limited to 2 filters. These findings are applicable to the task of time‐constrained planning of multispectral observations, and to the evaluation and cross‐comparison of multispectral imaging systems at specific material discrimination tasks.

New Version of the Experimental Setup for the Measurement of γ-Quantum Emission Cross Sections in Nuclear Reactions Induced by 14.1 MeV Neutrons

Within the TANGRA project framework, a new experimental setup has been constructed for the measurement of reaction cross sections (n, X, γ) in the interaction of 14.1 MeV neutrons with nuclei. The facility has a special feature: the use of the tagged neutron method. This method enables efficient separation of background and useful events, as well as accurate tracking of neutron flux. Test measurements were performed on 28Si, 12C, and 16O nuclei, and the results showed satisfactory agreement with available experimental data. This paper presents the features of the setup design and the methodology for processing the obtained experimental data

Foreground–background separation transformer for weakly supervised surface defect detection

Foreground–background separation transformer for weakly supervised surface defect detection

Spatial Invariant Tensor Self-Representation Model for Hyperspectral Anomaly Detection.

With the development of hyperspectral imaging technology, the hyperspectral anomaly has attracted considerable attention due to its significant role in many applications. Hyperspectral images (HSIs) with two spatial dimensions and one spectral dimension are intrinsically three-order tensors. However, most of the existing anomaly detectors were designed after converting the 3-D HSI data into a matrix, which destroys the multidimension structure. To solve this problem, in this article, we propose a spatial invariant tensor self-representation (SITSR) hyperspectral anomaly detection algorithm, which is derived based on the tensor-tensor product (t-product) to preserve the multidimension structure and achieve a comprehensive description of the global correlation of HSIs. Specifically, we exploit the t-product to integrate spectral information and spatial information, and the background image of each band is represented as the sum of the t-product of all bands and their corresponding coefficients. Considering the directionality of the t-product, we utilize two tensor self-representation methods with different spatial modes to obtain a more balanced and informative model. To depict the global correlation of the background, we merge the unfolding matrices of two representative coefficients and constrain them to lie in a low-dimensional subspace. Moreover, the group sparsity of anomaly is characterized by l2.1.1 norm regularization to promote the separation of background and anomaly. Extensive experiments conducted on several real HSI datasets demonstrate the superiority of SITSR compared with state-of-the-art anomaly detectors.

WHITE BLOOD CELL CANCER CLASSIFICATION USING MODIFIED HONEY BEE OPTIMIZATION ALGORITHM (MHBO) BASED FEATURE SELECTION AND NEURAL NETWORK CLASSIFICATION MODEL

A blood malignancy known as leukemia is defined by the bone marrow's abnormal and uncontrollably high production of leukocytes, or white blood cells (WBCs). It is possible to identify and diagnose illnesses early by examining photographs of minute blood cells. lately, hematopathologists have been analyzing, detecting, and identifying leukemia kinds in patients utilizing image-processing techniques. Since no specialized equipment is required for lab testing, image detection is a quick and affordable way of detection. Several image processing tools are created for obtaining significant data from medical photos to enhance patient diagnosis. In this research, a feed forward-back propagation neural network framework is presented, and the kind of cancer found in the cells is ultimately predicted. First, the provided photos are subjected to the filtering procedure. The second method is the Fuzzy Inference System (FIS) technique, which is reliable in the face of changing illumination levels and considers the stability levels of color elements to identify edges among color bone marrow microscopic pictures. Third Active contour detection provides an accurate curve of the boundary edge of the cells. And then the background separation is done by Pinched Flow Fractionation (PFF). Then the feature extraction is carried out by the Modified Honey Bee optimization algorithm (MHBO). Finally, the Feed Forward Back Propagation Neural Network (FFBNN) framework-based classifier is proposed for predicting the white blood cells. Based on the findings, this suggested approach created an automated system that allows medical practitioners to accurately diagnose all forms and subtypes of the disease. Keywords: Bone marrow microscopic images, Fuzzy Inference System (FIS), Pinched Flow Fractionation (PFF), Modified honey bee optimization algorithm (MHBO), Feed Forward Back Propagation Neural Network framework

Background separation network for video anomaly detection

Video anomaly detection refers to the automatic identification of abnormal behaviors, objects, or events in videos. However, current methods for anomaly detection based on original frames lack a comprehensive understanding of the importance of foreground information, making it challenging to efficiently address video anomaly detection in the presence of complex background interference. In this paper, we propose a video anomaly detection algorithm based on Background Separation Network (BSN) to address this issue. Firstly, we utilize a video stabilization algorithm to reduce video jitter and enhance the quality of input video frames. Secondly, BSN shifts the focus from the entire frame to the foreground region with higher anomaly detection value. BSN utilizes the motion pixel distribution of the video as the basis for foreground extraction, enabling pixel-level background separation to obtain more accurate and complete foreground targets. Lastly, a certain proportion of foreground targets in the foreground image are masked as background, reducing the interference caused by redundant targets on the detection results. The proposed method achieves an accuracy of 96.2% on the UCSD ped2 dataset, demonstrating its effectiveness. This method contributes to accurately detecting abnormal behaviors in real-world surveillance videos to protect the safety of public lives and assets.

Control of the fluorescence lifetime in dye based nanoparticles.

Fluorescent dye based nanoparticles (NPs) have received increased interest due to their high brightness and stability. In fluorescence microscopy and assays, high signal to background ratios and multiple channels of detection are highly coveted. To this end, time-resolved imaging offers suppression of background and temporal separation of spectrally overlapping signals. Although dye based NPs and time-resolved imaging are widely used individually, the combination of the two is uncommon. This is likely due to that dye based NPs in general display shortened and non-mono-exponential lifetimes. The lower quality of the lifetime signal from dyes in NPs is caused by aggregation caused quenching (ACQ) and energy migration to dark states in NPs. Here, we report a solution to this problem by the use of the small-molecule ionic isolation lattices (SMILES) concept to prevent ACQ. Additionally, incorporation of FRET pairs of dyes locks the exciton on the FRET acceptor providing control of the fluorescence lifetime. We demonstrate how SMILES NPs with a few percent rhodamine and diazaoxatriangulenium FRET acceptors imbedded with a cyanine donor dye give identical emission spectra and high quantum yields but very different fluorescence lifetimes of 3 ns and 26 ns, respectively. The two spectrally identical NPs are easily distinguished at the single particle level in fluorescence lifetime imaging. The doping approach for dye based NPs provides predictable fluorescence lifetimes and allows for these bright imaging reagents to be used in time-resolved imaging detection modalities.

A multidimensional, event-by-event, statistical weighting procedure for signal to background separation

A multidimensional, event-by-event, statistical weighting procedure for signal to background separation

In Situ Root Dataset Expansion Strategy Based on an Improved CycleGAN Generator.

The root system plays a vital role in plants' ability to absorb water and nutrients. Insitu root research offers an intuitive approach to exploring root phenotypes and their dynamics. Deep-learning-based root segmentation methods have gained popularity, but they require large labeled datasets for training. This paper presents an expansion method for insitu root datasets using an improved CycleGAN generator. In addition, spatial-coordinate-based target background separation method is proposed, which solves the issue of background pixel variations caused by generator errors. Compared to traditional threshold segmentation methods, this approach demonstrates superior speed, accuracy, and stability. Moreover, through time-division soil image acquisition, diverse culture medium can be replaced in insitu root images, thereby enhancing dataset versatility. After validating the performance of the Improved_UNet network on the augmented dataset, the optimal results show a 0.63% increase in mean intersection over union, 0.41% in F1, and 0.04% in accuracy. In terms of generalization performance, the optimal results show a 33.6% increase in mean intersection over union, 28.11% in F1, and 2.62% in accuracy. The experimental results confirm the feasibility and practicality of the proposed dataset augmentation strategy. In the future, we plan to combine normal mapping with rendering software to achieve more accurate shading simulations of insitu roots. In addition, we aim to create a broader range of images that encompass various crop varieties and soil types.

Research on the vision system of lychee picking robot based on stereo vision

In this study, we propose a development scheme of litchi robot vision system based on stereo vision. The system includes three components: image acquisition, image analysis, and image processing. The obtained image can be corrected according to the light environment conditions with the corresponding algorithm for image color, calculate the Hue color layer, analyze the external characteristics and positional data (2D) of litchi, obtain the 3D coordinate information of fruits and vegetables, and provide to the running device for route planning and vegetable and fruit picking claw tools for picking. The integration test results for the robot arm and claw tool demonstrate the following: (1) Using image acquisition, the system can acquire high-quality images in real-time via a combination of automatic exposure and white balance conversion. (2) Using image analysis techniques such as background separation, edge detection, and external feature search, it is possible to successfully calculate the location coordinates of the litchi's tip, center, and bottom. (3) Using image processing, the claw of the picking machine can determine the condition of overlapping lychees and their state of ripening based on the depth information of feature points used in the picking operation. (4) Under the working range of 18.4 cm × 33.6 cm, the success rate of picking litchi with a diameter of 28 mm, 34 mm, and 42 mm was 93.3%, 91.1 %, and 84.4%, respectively, exceeding the design expectation level.

TVRPCA＋: Low-rank and sparse decomposition based on spectral norm and structural sparsity-inducing norm

Traditional low-rank sparse decomposition algorithms have trouble obtaining a clear and complete foreground representation in foreground–background separation due to the complex video environment and the noise. For this issue, We propose a more robust and higher-performance low-rank and sparse decomposition algorithm named TVRPCA＋ based on spectral norm, structured sparse norm and total variation (TV) regularization. The structured sparse norm and TV regularization are exploited to suppress noise and obtain much cleaner foregrounds. Spectral norm is used in our algorithm for the low-rank component to address the issue of over-punishment and restore more foreground information. Moreover, an efficient algorithm based on the inexact augmented Lagrange multiplier method is designed to solve the proposed optimization problem. Experimental results show that TVRPCA＋ obtained five top F-measures and three of the second-highest F-measures in eight noise-free test video sequences with complex backgrounds, while the highest average F-measure was also achieved in all ten experimental groups with noise.

Aesthetic-aware image retargeting based on foreground–background separation and PSO optimization

Aesthetic-aware image retargeting based on foreground–background separation and PSO optimization

A Novel Method Based on GPU for Real-Time Anomaly Detection in Airborne Push-Broom Hyperspectral Sensors

The airborne hyperspectral remote sensing systems (AHRSSs) acquire images with high spectral resolution, high spatial resolution, and high temporal dimension. While the AHRSS captures more detailed information from the terrain objects, the computational complexity of data processing is greatly increased. As an important application technology in the hyperspectral domain, anomaly detection (AD) processing must be real-time and high-precision in many cases, such as post-disaster rescue, military battlefield search, and natural disaster detection. In this paper, the real-time AD technology for the push-broom AHRSS is studied, the mathematical model is established, and a novel implementation framework is proposed. Firstly, the optimized kernel minimum noise fraction (OP-KMNF) transformation is employed to extract informative and discriminative features between the background and anomalies. Secondly, the Nyström method is introduced to reduce the computational complexity of OP-KMNF transformation by decomposing and extrapolating the sub-kernel matrix to estimate the eigenvector of the entire kernel matrix. Thirdly, the extracted features are transferred to hard disks for data storage. Then, taking the extracted features as input data, the background separation model-based CEM anomaly detector (BSM-CEMAD) is imported to detect anomalies. Finally, graphics processing unit (GPU) parallel computing is utilized in the Nyström-based OP-KMNF (NOP-KMNF) transformation and the BSM-CEMAD to improve the execution efficiency, and the real-time AD for the push-broom AHRSS could be realized. To test the feasibility of the implementation framework proposed in this paper, the experiment is carried out with the Airborne Multi-Modular Imaging Spectrometer (AMMIS) developed by the Shanghai Institute of Technical Physics as the data acquisition platform. The experimental results show that the proposed method outperforms many other state-of-the-art AD methods in anomalies detection and background suppression. Moreover, under the condition that the downlink data could retain most of the hyperspectral data information, the proposed method achieves real-time detection of pixel-level anomalies, with the initial delay not exceeding 1 s, the false alarm rate (FAR) less than 5%, and the true positive rate (TPR) close to 98%.

Search for third-generation vector-like leptons in pp collisions at sqrt{s} = 13 TeV with the ATLAS detector

A search for vector-like leptons in multilepton (two, three, or four-or-more electrons plus muons) final states with zero or more hadronic τ-lepton decays is presented. The search is performed using a dataset corresponding to an integrated luminosity of 139 fb−1 of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector at the LHC. To maximize the separation of signal and background, a machine-learning classifier is used. No excess of events is observed beyond the Standard Model expectation. Using a doublet vector-like lepton model, vector-like leptons coupling to third-generation Standard Model leptons are excluded in the mass range from 130 GeV to 900 GeV at the 95% confidence level, while the highest excluded mass is expected to be 970 GeV.

Background separation of sports athletes and motion image analysis based on skeleton segmentation algorithm

Background separation of sports athletes and motion image analysis based on skeleton segmentation algorithm

An infrared image enhancement method via content and detail Two-Stream deep convolutional neural network

Aiming at the problems of low contrast and blurry details of infrared images, a novel two-stream deep full convolutional neural network is proposed for low-quality infrared image enhancement. An infrared detail enhancement sub-network and a global content-invariant sub-network are designed to achieve adaptive enhancement of infrared features. First, the detail enhancement network composed of mixed attention block (MAB) with multi-convolutions, residual learning (RL) and up-sampling unit is used to extract deep features from inputs, learn meaningful thermal radiation target information, restrain unnecessary background, and then perform the separation of target and background. Second, the content-invariant network mainly consisting of dilated convolutions and multi-scale convolutions captures rich contextual information to focus on the overall content, maintain the spatial structure, and avoid over-enhancement of local regions. Finally, the fine-tuning unit fuses the features extracted by the two-stream to complete the element complementation between different mappings and generate high-quality infrared images. Furthermore, experiments on public datasets and self-collected infrared datasets demonstrate that the proposed method outperforms other image enhancement methods not only for image quality on PSNR and SSIM, but also that has better visual quality with less artifact and noise.

Training Data Generation for U-Net Based MRI Image Segmentation using Level-Set Methods

Image segmentation has been a well-addressed problem in pattern recognition for the last few decades. As a sub-problem of image segmentation, the background separation in biomedical images generated by magnetic resonance imaging (MRI) has also been of interest in the applied mathematics literature. Level set evolution of active contours idea can successfully be applied to MRI images to extract the region of interest (ROI) as a crucial preprocessing step for medical image analysis. In this study, we use the classical level set solution to create binary masks of various brain MRI images in which black color implies background and white color implies the ROI. We further used the MRI image and mask image pairs to train a deep neural network (DNN) architecture called U-Net, which has been proven to be a successful model for biomedical image segmentation. Our experiments have shown that a properly trained U-Net can achieve a matching performance of the level set method. Hence we were able to train a U-Net by using automatically generated input and label data successfully. The trained network can detect ROI in MRI images faster than the level-set method and can be used as a preprocessing tool for more enhanced medical image analysis studies.

Weighted Schatten p-norm and Laplacian scale mixture-based low-rank and sparse decomposition for foreground–background separation

Low-rank and sparse decomposition (LRSD) plays a vital role in foreground–background separation. The existing LRSD methods have the drawback: imprecise surrogate functions of rank and sparsity. We propose the weighted Schatten p-norm (WSN) and Laplacian scale mixture (LSM) method based on LRSD for foreground–background separation, which introduces the WSN and LSM to improve this drawback. To demonstrate the performance of the proposed method, it is applied to foreground–background separation and gets the highest average F-measure score.

A rapid and simple capillary electrophoresis procedure for quantification of vanillylmandelic acid in urine samples

Aim: To develop a one-step vortex-assisted liquid-liquid extraction (VALLE) method, without the need for evaporation and reconstitution steps, to establish a rapid and straightforward treatment procedure based on capillary electrophoresis-diode array detection (CE-DAD) for the determination of vanillylmandelic acid (VMA) in human urine. Methodology: Optimization of VALLE and CE-DAD procedures were studied in detail. The effects of various experimental parameters, such as the type of the extraction solvent, sample pH, salt addition, and extraction time were investigated. Also, CE separation conditions including background electrolyte type, concentration, and pH, injection time and separation voltage were optimized as well. Results: A successful separation of VMA was achieved in less than 6 min using a basic background electrolyte composed of 60 mmol·L-1 acetate/ACN (acetonitrile) 50% (v/v) (final apparent pH is 5.73). The linear response was obtained over the concentration range from 1.0 to 14 μg·mL-1. The limit of detection and quantification were 0.30 and 1.0 μg·mL-1, respectively. The intra- and inter-day precisions were found to be less than 4.3%. The extraction recoveries of VMA were between 95%-97%. Conclusion: The developed method is found to be a simple, rapid, and reliable method for quantitative analysis of urinary VMA.

Open-Set Object Detection Using Classification-Free Object Proposal and Instance-Level Contrastive Learning

Detecting both known and unknown objects is a fundamental skill for robot manipulation in unstructured environments. Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background separation, and open-set object classification. In this letter, we present Openset RCNN to address the challenging OSOD. To disambiguate unknown objects and background in the first subtask, we propose to use classification-free region proposal network (CF-RPN) which estimates the objectness score of each region purely using cues from object's location and shape preventing overfitting to the training categories. To identify unknown objects in the second subtask, we propose to represent them using the complementary region of known categories in a latent space which is accomplished by a prototype learning network (PLN). PLN performs instance-level contrastive learning to encode proposals to a latent space and builds a compact region centering with a prototype for each known category. Further, we note that the detection performance of unknown objects can not be unbiasedly evaluated on the situation that commonly used object detection datasets are not fully annotated. Thus, a new benchmark is introduced by reorganizing GraspNet-1billion, a robotic grasp pose detection dataset with complete annotation. Extensive experiments demonstrate the merits of our method. We finally show that our Openset RCNN can endow the robot with an open-set perception ability to support robotic rearrangement tasks in cluttered environments.