Background Image Research Articles

Glow-type luminol chemiluminescence based on a supramolecular enhancer of cyclodextrin

BackgroundChemiluminescence (CL) bioassay is one of the most advanced and used detection method in clinical diagnosis and biomedical research because of the advantages of low background, easy operation, and wide-field imaging without a light source or microscope. The luminol/hydrogen peroxide/horseradish peroxidase (luminol/H2O2/HRP) system is the most popular CL system, but its application in high-throughput imaging detection is challenged due to its low luminescence efficiency and flash-type emission which is difficult in ensuring the reproducibility and consistency of detection results. ResultsWe reported a glow-type CL system of luminol@CD/H2O2/HRP by using a supramolecular enhancer of cyclodextrin (CD). This luminol@CD/H2O2/HRP system exhibited a luminescence lifetime of 41 min for sensitive and accurate imaging analysis. The long-lasting CL emission was attributed to the formation of a 1:1 host–guest complex between luminol and CD, which could stabilize the emitter and effectively reduce nonradiative relaxation. The formation of luminol@CD complex was determined through NMR experiments and theoretical analysis. Under optimum conditions, the luminol@CD/H2O2/HRP system showed higher sensitivity and much better precision than classical luminol/H2O2/HRP system for imaging detection of HRP. Especially, this glow-type luminol@CD/H2O2/HRP system realized CL imaging of microwell arrays on microfluidic chips. In addition, the luminol@CD/H2O2/HRP system was successfully applied for point-of-care detection of 17β-estradiol based on a competitive mechanism of host–guest recognition. SignificanceAn efficient CL system is crucial for obtaining reproducible and consistent results for accurate detection. Our luminol@CD/H2O2/HRP system emitted strong and persistent luminescence, resulting in reliability and efficiency at both CL macroscopic and microscopic imaging detection. We expected the luminol@CD/H2O2/HRP CL system to be applied in various detection fields.

Image deraining via invertible disentangled representations

Photos taken on rainy days usually suffer from a loss of background image information due to obstruction by rain streaks and the mist-like effect that rain creates. Recent Invertible Neural Networks (INNs) have shown promise in image deraining, as they are capable of extracting observed features without information loss for the purpose of restoration. However, it remains a challenge for INNs to directly learn the transformation from a rainy image to its rain-free version, given that rain streaks exhibit a diversity in transparency, shape, and brightness, leading to a complex distribution of lost details. To solve the problem, we design a novel Invertible Disentangling network (InvDis) to decompose the complex data distribution into simpler ones and makes the transformation much easier to learning. To be specific, InvDis separates feature channels of rain streaks and those of the polluted image details in the forward mapping. By only discarding rain channels and preserving polluted image details, in the backward mapping, InvDis focuses on generating missing details obstructed by rain streaks with a new sample from a prior distribution, and restores clean details from those polluted ones. Additionally, InvDis incorporates a channel interaction mechanism to facilitate the disentangling. It allows the rain streaks encoded in other channels to easily flow to those discarded channels, and conversely, allows the polluted image details to be directed towards the preserved channels. Experimental results on both synthetic and real-world datasets show that the InvDis not only improves the restoration quality but also has lower computational costs.

Foreign object detection in urban rail transit based on deep differentiation segmentation neural network

With the increasing scale of urban rail transit, foreign object intrusion has become a significant operational safety hazard in urban rail transit. Although the laser-based automatic foreign object detection system has advantages such as long-distance detection and insensitivity to light changes, it has drawbacks such as large blind spots and low visualization. In response to the problems existing in laser detection systems, we proposed a novel video-based deep differentiation segmentation neural network for foreign object detection. Firstly, the foreign object detection is transformed into a binary classification problem, and the foreign object is determined as the image's foreground using image segmentation principles. Secondly, build a deep segmentation network based on deep convolution. Finally, perform morphological operations and threshold judgment on the foreground segmentation image to filter out the final detection results. To improve the detection effect, we reduced the impact of airflow disturbance by sampling and calculating the average background image. At the same time, the channel attention model and spatial attention model are added to the deep differentiation neural network. Collecting real data on subway platforms for experiments shows that the proposed method has a detection accuracy of 95.8 %, which is superior to traditional detection methods and recent image segmentation neural networks.

Optimizing encoding strategies for 4D Flow MRI of mean and turbulent flow

For 4D Flow MRI of mean and turbulent flow a compromise between spatiotemporal undersampling and velocity encodings needs to be found. Assuming a fixed scan time budget, the impact of trading off spatiotemporal undersampling versus velocity encodings on quantification of velocity and turbulence for aortic 4D Flow MRI was investigated. For this purpose, patient-specific mean and turbulent aortic flow data were generated using computational fluid dynamics which were embedded into the patient-specific background image data to generate synthetic MRI data with corresponding ground truth flow. Cardiac and respiratory motion were included. Using the synthetic MRI data as input, 4D Flow MRI was subsequently simulated with undersampling along pseudo-spiral Golden angle Cartesian trajectories for various velocity encoding schemes. Data were reconstructed using a locally low rank approach to obtain mean and turbulent flow fields to be compared to ground truth. Results show that, for a 15-min scan, velocity magnitudes can be reconstructed with good accuracy relatively independent of the velocity encoding scheme (SSIMU=0.938±0.003)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SSI{M}_{U}=0.938\\pm 0.003)$$\\end{document}, good accuracy (SSIMU≥0.933\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SSI{M}_{U}\\ge 0.933$$\\end{document}) and with peak velocity errors limited to 10%. Turbulence maps on the other hand suffer from both lower reconstruction quality (SSIMTKE≥0.323\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SSI{M}_{TKE}\\ge 0.323$$\\end{document}) and larger sensitivity to undersampling, motion and velocity encoding strengths (SSIMTKE=0.570±0.110)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SSI{M}_{TKE}=0.570\\pm 0.110)$$\\end{document} when compared to velocity maps. The best compromise to measure unwrapped velocity maps and turbulent kinetic energy given a fixed 15-min scan budget was found to be a 7-point multi-Venc\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${V}_{enc}$$\\end{document} acquisition with a low Venc\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${V}_{enc}$$\\end{document} tuned for best sensitivity to the range of expected intra-voxel standard deviations and a high Venc\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${V}_{enc}$$\\end{document} larger than the expected peak velocity.

A diffusion model multi-scale feature fusion network for imbalanced medical image classification research

Background and objectiveMedicine image classification are important methods of traditional medical image analysis, but the trainable data in medical image classification is highly imbalanced and the accuracy of medical image classification models is low. In view of the above two common problems in medical image classification. This study aims to: (i) effectively solve the problem of poor training effect caused by the imbalance of class imbalanced data sets. (ii) propose a network framework suitable for improving medical image classification results, which needs to be superior to existing methods. MethodsIn this paper, we put in the diffusion model multi-scale feature fusion network (DMSFF), which mainly uses the diffusion generation model to overcome imbalanced classes (DMOIC) on highly imbalanced medical image datasets. At the same time, it is processed according to the cropped image augmentation strategy through cropping (IASTC). Based on this, we use the new dataset to design a multi-scale feature fusion network (MSFF) that can fully utilize multiple hierarchical features. The DMSFF network can effectively solve the problems of small and imbalanced samples and low accuracy in medical image classification. ResultsWe evaluated the performance of the DMSFF network on highly imbalanced medical image classification datasets APTOS2019 and ISIC2018. Compared with other classification models, our proposed DMSFF network achieved significant improvements in classification accuracy and F1 score on two datasets, reaching 0.872, 0.731, and 0.906, 0.836, respectively. ConclusionsOur newly proposed DMSFF architecture outperforms existing methods on two datasets, and verifies the effectiveness of generative model inverse balance for imbalance class datasets and feature enhancement by multi-scale feature fusion. Further, the method can be applied to other class imbalanced data sets where the results will be improved.

Infrared superpixel patch-image model for small target detection under complex background

The main problem of infrared small target detection in complex background is how to effectively eliminate the edge residue. In this paper, we propose an efficient method named Superpixel Patch Image (SPI) model to handle this challenging task. The SPI model can fit the edges of the background well, thus effectively eliminating edge interference in the process of target detection, and achieving excellent performance. The SPI method consists of three steps: Firstly, an improved Simple Linear Iterative Clustering (ISLIC) algorithm is proposed to generate compact superpixels that perfectly match the background edge. Secondly, setting each superpixel patch as a column, a large patch-image matrix is constructed, and the target foreground image and background image is separated by imprecisely augmented Lagrange multiplication. Finally, based on the comprehensively analysis of the distribution characteristics of the target and the highlighted edge in the foreground image, an adaptive threshold is used to extract the target from the foreground superpixel patch. The experimental results of real infrared scenes show that the presented SPI model achieves the best SCRG, BSF and ROC curves compared with the existing 9 state-of-art algorithms, and can effectively extract small targets under different complex backgrounds.

Contrast-Enhanced Sonography of the Liver: How to Avoid Artifacts.

Contrast-enhanced sonography (CEUS) is a very important diagnostic imaging tool in clinical settings. However, it is associated with possible artifacts, such as B-mode US-related artifacts. Sufficient knowledge of US physics and these artifacts is indispensable to avoid the misinterpretation of CEUS images. This review aims to explain the basic physics of CEUS and the associated artifacts and to provide some examples to avoid them. This review includes problems related to the frame rate, scanning modes, and various artifacts encountered in daily CEUS examinations. Artifacts in CEUS can be divided into two groups: (1) B-mode US-related artifacts, which form the background of the CEUS image, and (2) artifacts that are specifically related to the CEUS method. The former includes refraction, reflection, reverberation (multiple reflections), attenuation, mirror image, and range-ambiguity artifacts. In the former case, the knowledge of B-mode US is sufficient to read the displayed artifactual image. Thus, in this group, the most useful artifact avoidance strategy is to use the reference B-mode image, which allows for a simultaneous comparison between the CEUS and B-mode images. In the latter case, CEUS-specific artifacts include microbubble destruction artifacts, prolonged heterogeneous accumulation artifacts, and CEUS-related posterior echo enhancement; these require an understanding of the mechanism of their appearance in CEUS images for correct image interpretation. Thus, in this group, the most useful artifact avoidance strategy is to confirm the phenomenon's instability by changing the examination conditions, including the frequency, depth, and other parameters.

Insulator detection based on FA‐YOLO network with improved feature extraction ability

AbstractUnmanned aerial vehicle insulator detection that aims to recognize defective insulators from transmission lines has made significant progress in recent years. However, it still faces challenges, such as the complex background of aerial images and the small memory of unmanned aerial vehicles. This paper proposes a refined insulator detection algorithm that integrates the attention mechanism in YOLOv8 to improve the feature extraction ability. Specifically, this paper introduces a fast vision transformers structure in the you only look once (YOLO) v8 backbone section to enhance feature extraction by capturing local and global features. Additionally, the global attention mechanism is incorporated in the neck for additional feature extraction by merging comprehensive spatial and channel information into the output. Furthermore, we amalgamate depth‐wise convolution, graph convolution, and residual operation in the global attention mechanism module. This design can mitigate the issues of gradient vanishing or exploding and meanwhile enhance the distinction between spatial attention and channel attention. The proposed model is then applied to a public dataset and a set of real images from a specific power station, and the detection results show that it outperforms many competitors in terms of accuracy, efficiency, and memory size.

A shape-supervised feature fusion U-Net for tubular structure segmentation

Accurate segmentation of tubular structures, such as blood vessels and bile ducts, is pivotal for clinical diagnosis and subsequent treatment. However, challenges arise from their unique structure and morphology. These structures are thin and elongated, and often exhibit complex branching patterns, making precise delineation difficult. Additionally, the intricate and often noisy backgrounds in medical images complicate the extraction of detailed and shape features. These challenges collectively hinder the effectiveness of automated segmentation methods. To address these issues, this paper introduces the shape-supervised feature fusion U-Net (SFU-Net), a novel approach that extends the traditional U-Net architecture. Our network incorporates a feature fusion module that effectively combines different semantic information in both the encoder and decoder paths, facilitating the capture of detailed geometric representations of tubular structures. Furthermore, a novel distance-based tubularity loss is designed to capture boundary information and supervise segmentation integrity by imposing shape constraints. We conducted comprehensive experiments on three tubular structure segmentation tasks in medical imaging, including the dilated biliary tree, hepatic vessel, and pulmonary artery. The experimental results illustrate that the proposed SFU-Net achieves the best performance in terms of accuracy and completeness, with Dice scores of 76.14%, 80.42%, and 83.79%, and HD95 values of 8.59 mm, 6.43 mm, and 4.31 mm across three datasets, respectively. Compared to traditional U-Net-based segmentation methods and other tubular segmentation approaches, our method exhibits superior performance, underscoring its effectiveness in medical image segmentation with tubular structures.

Automatic Foreign Matter Segmentation System for Superabsorbent Polymer Powder: Application of Diffusion Adversarial Representation Learning

In current industries, sampling inspections of the quality of powders, such as superabsorbent polymers (SAPs) still are conducted via visual inspection. The size of samples and foreign matter are around 500 μm, making them difficult for humans to identify. An automatic foreign matter detection system for powder has been developed in the present study. The powder samples can be automatically delivered, distributed, and recycled, and images of them are captured through the hardware of the system, while the identification software of this system was developed based on diffusion adversarial representation learning (DARL). The background image is a foreign-matter-free powder image with an input image size of 1024 × 1024 × 3. Since DARL includes adversarial segmentation, a diffusion process, and synthetic image generation, the DARL model was trained using a diffusion block with the employment of a U-Net attention mechanism and a spatial-adaptation de-normalization (SPADE) layer through the adoption of a loss function from a vanilla generative adversarial network (GAN). This model was then compared with supervised models such as a fully convolutional network (FCN), U-Net, and DeepLABV3+, as well as with an unsupervised Otsu threshold segmentation. It should be noted that only 10% of the training samples were utilized for the DARL to learn and the intersection over union (IoU) of the DARL can reach up to 80.15%, which is much higher than the 59.00%, 53.47%, 49.39%, and 30.08% for the Otsu threshold segmentation, FCN, U-Net, and DeepLABV3+ models. Therefore, the performance of the model developed in the present study would not be degraded due to an insufficient number of samples containing foreign matter. In practical applications, there is no need to collect, label, and design features for a large number of foreign matter samples before using the developed system.

A novel framework for low-contrast and random multi-scale blade casting defect detection by an adaptive global dynamic detection transformer

The radiographic inspection plays a crucial role in ensuring the casting quality for improving the service life under harsh environments. However, due to the low-contrast between the defects and the image background, the random spatial position distribution, random shapes and aspect ratios of the defects, the development of an accurate defect automatic detection system is still challenging. To address these issues, this paper proposes a novel framework for low-contrast and random multi-scale casting defect detection, which is referred to as adaptive global dynamic detection transformer (AGD-DETR). A novel defect-aware data augmentation method is first proposed to adaptively highlight the feature of the low-contrast defect boundary. A multi-attentional pyramid feature refinement (MPFR) module is then established to refine and fuse the multi-scale defect features of random sizes. Afterwards, a novel global dynamic receptive fusion-transformer (GDRF-Transformer) detection scheme is designed to perform the global perception and feature dynamic extraction of complex internal casting defects. It includes 4D-anchor query and cross-layer box update strategy, query rectification by prior information of defect aspect ratio, and global adaptive-feed forward network (GA-FFN). A dataset comprising turbine blade casting defect radiographic (TBCDR) images, is used to demonstrate the high efficiency of the proposed AGD-DETR. The obtained results show that the proposed method can accurately capture the spatial position distributions and complex defect shapes. Furthermore, it outperforms existing state-of-the-art defect detection methods.

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Background and ObjectivesTechniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. MethodsTo construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered OCM and confocal fluorescence microscopy (CFM) images. ResultsComparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95 % confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95 % CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images. ConclusionsThe cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.

Non-local feature aggregation quaternion network for single image deraining

The existing deraining methods are based on convolutional neural networks (CNN) learning the mapping relationship between rainy and clean images. However, the real-valued CNN processes the color images as three independent channels separately, which fails to fully leverage color information. Additionally, sliding-window-based neural networks cannot effectively model the non-local characteristics of an image. In this work, we proposed a non-local feature aggregation quaternion network (NLAQNet), which is composed of two concurrent sub-networks: the Quaternion Local Detail Repair Network (QLDRNet) and the Multi-Level Feature Aggregation Network (MLFANet). Furthermore, in the subnetwork of QLDRNet, the Local Detail Repair Block (LDRB) is proposed to repair the backdrop of an image that has not been damaged by rain streaks. Finally, within the MLFANet subnetwork, we have introduced two specialized blocks, namely the Non-Local Feature Aggregation Block (NLAB) and the Feature Aggregation Block (Mix), specifically designed to address the restoration of rain-streak-damaged image backgrounds. Extensive experiments demonstrate that the proposed network delivers strong performance in both qualitative and quantitative evaluations on existing datasets. The code is available at https://github.com/xionggonghe/NLAQNet.

Colorectal cancer image recognition algorithm based on improved transformer

Aiming at the problems of the complex background of colorectal cancer tissue cell images and the difficulty of detection caused by the low differentiation of cancer cell regions, a deep learning method is used to detect the cancer cell regions. By integrating the skip feedback connection structure into U-Net and combining it with the Swin Transformer for feature extraction, we improve the multi-level feature extraction capabilities of the model. This algorithm enables end-to-end recognition of colorectal adenocarcinoma tissue images and achieves an accuracy of 95.8% on the NCT-CRC-HE-100K dataset, demonstrating its potential to significantly support colorectal cancer detection and treatment.

An improved smoking behavior detection algorithm via incorporating an interference information filtering network

Efficient and accurate identification of smoking behavior in public places is crucial for ensuring public health and safety. However, due to various factors like small target size, complex image background, varying cigarette angles and numerous similar objects, current methods still grapple with challenges such as missed detection and false detection when identifying cigarette targets in smoking behavior. This paper presents an enhanced version of the You Only Look Once version5-small algorithm to address these issues effectively. Firstly, to bolster the model's ability for feature extraction of cigarette targets, the Swin Transformer Block structure is integrated into the backbone network to capture long-range dependencies. Secondly, a novel Hybrid Spatial Pyramid Pooling-Fast with Cross Stage Partial Connection module is built based on the foundation of the Spatial Pyramid Pooling-Fast module by integrating both maximum pooling and average pooling to enhance the fusion ability of the multi-scale feature maps. Thirdly, a novel interference information filtering network is introduced to effectively reduce the impact of noise and confusion caused by similar objects, thus enhancing the performance of the model. According to the experimental results, the accuracy of the improved algorithm on the self-made cigarette target image data set reaches 93.5 %, and the recall reaches 89.1 %.

Rice Leaf Disease Recognition using CNN

Abstract: The occurrence of bacterial, viral, and fungal diseases on rice leaves significantly hampers rice production, posing a challenge to meet global demand for the staple crop. While the detection of rice leaf diseases is crucial, existing methods are constrained by limitations in image backgrounds and capture conditions. Convolutional Neural Network (CNN) models have emerged as a usefull avenue for disease recognition in rice leaves, yet current approaches suffer from decreased recognition rates when applied to independent datasets and are constrained by the need for large-scale network parameters. In this project, we propose an innovative CNN-based model aimed at mitigating these challenges by reducing network parameters. Through training multiple CNN- basedmodels to identify three common rice leaf diseases, our study aims to showcase the effectiveness and superiority of our approach compared to state-of-the-art CNN-based models for rice leaf disease recognition.

Investigation of an irradiated CCD device: Building and testing a Charge Transfer Inefficiency correction pipeline using the Pyxel framework

Pyxel is a powerful end-to-end detector simulations framework with the aim of being reusable, reliable and help facilitate knowledge transfer between a wide range of related fields. To show how beneficial Pyxel can be, we present an investigation into the effect of Charge Transfer Inefficiency (CTI) on an irradiated CCD-273 device produced by Teledyne-e2v, the type of detector used for the Euclid VIS instrument. This requires production of two pipelines. Firstly, a calibration pipeline to search the parameter space and obtain best fitting trap parameters based upon the CTI within the detector. Secondly, a correction pipeline which aims to correct the CTI-induced data using these trap parameters to obtain a nominal efficiency of 99.5%. The following presents work conducted in building, testing and validating the correction pipeline using Pyxel and the CTI model ArCTIC. This involves data from the unirradiated portion of a CCD-273 device using the scene projection system at the labs at the European Space Agency (ESA). The images are computationally altered by varying parameters such as background levels and readout noise, and by synthetically producing CTI trails from known trap parameters. The resulting images are corrected and analysed to understand how noise affects the correction efficiency at varying image background levels.

Concealed hazardous object detection for terahertz images with cross-feature fusion transformer

Terahertz (THz) waves can penetrate many non-metallic materials (such as paper, plastic, and clothing), making them highly valuable in security inspection areas. However, the low contrast between target objects and the background in THz images poses problems for object detection and recognition tasks. Though many schemes have been proposed to tackle the problems, the low detection accuracy and high computational complexity remain challenges. To address these challenges, we propose a concealed hazardous object detection method called Cross-Feature Fusion Transformer YOLO (CFT-YOLO) for THz images. We design a Multi-Channel Spatial Feature Module (MCSFM) to achieve cross-channel and cross-spatial information transfer between feature maps. MCSFM also realizes the feature parameter transfer between the backbone and neck of the network. Additionally, we develop a CrossFuse-Transformer (CF-Former) module that leverages self-attention to fully exploit the correlations and structural information between different feature maps. We conduct a series of experiments on public datasets. The results show that CFT-YOLO outperforms the other advanced methods in THz hazardous object detection tasks. Moreover, the CFT-YOLO achieves a better balance between model complexity and detection accuracy compared to YOLOv8s, making it more practical for real-world applications.

Reflections on Reading 'Journey to the West in China'

This paper re-examines the classic thesis 'Journey to the West'. Through in-depth analysis of the text, the historical background, social landscape, and character images presented in the work were explored. Re-examined the author's writing perspective and narrative strategy, revealing its unique value in documenting the Chinese revolutionary process. It analyzes the vivid description of the revolutionary struggle under the leadership of the CPC and the true portrayal of all social strata at that time. Further exploration was conducted on the important significance of "Journey to the West" in cross-cultural communication and historical inheritance, emphasizing its important role in enhancing contemporary readers' understanding and cognition of that special historical period, and providing new perspectives and reflections for in-depth research on Chinese revolutionary history.

Segmentation-free measurement of locomotor frequency in Caenorhabditis elegans using image invariants.

An animal's locomotor rate is an important indicator of its motility. In studies of the nematode Caenorhabditis elegans (C. elegans), assays of the frequency of body bending waves have often been used to discern the effects of mutations, drugs, or aging. Traditional manual methods for measuring locomotor frequency are low in throughput and subject to human error. Most current automated methods depend on image segmentation, which requires high image quality and is prone to errors. Here, we describe an algorithm for automated estimation of C. elegans locomotor frequency using image invariants, i.e. shape-based parameters that are independent of object translation, rotation, and scaling. For each video frame, the method calculates a combination of 8 Hu's moment invariants and a set of maximally stable extremal regions (MSER) invariants. The algorithm then calculates the locomotor frequency by computing the autocorrelation of the time sequence of the invariant ensemble. Results of our method show excellent agreement with manual or segmentation-based results over a wide range of frequencies. We show that compared to a segmentation-based method that analyzes a worm's shape and a method based on video covariance, our technique is more robust to low image quality and background noise. We demonstrate the system's capabilities by testing the effects of serotonin and serotonin pathway mutations on C. elegans locomotor frequency.