A novel method for eliminating rotation deviation in sequential images mosaic

To eliminate rotation deviation of sequential images mosaic when measuring linear dimensions of large scale parts with computer vision, a novel algorithm based on the chain code searching method is proposed. After image preprocessing, including image filtering, image segmentation, and edge detection, the chain code length of the contour line can be searched out by the proposed method. Then, the angle from the contour line to the coordinate axis is computed with the length of the contour line. After that, the sequence is rotated in the opposite direction and the rotation deviation is eliminated. It is prepared for the next mosaic of sequences in eliminating shifting deviation. Experiments are carried out on parts with a linear profile rotating angle from 0° to 9°. The results show that compared with the commonly used Hough transform, the new method has higher precision and faster speed, which is important in realizing online high precision measurements of large scale parts with a linear profile.

主流媒体短视频传播中的国家形象建构研究——以中国网短视频节目《中国三分钟(China Mosaic)》为例

以中国网账号发布在境外社交媒体平台Facebook上的节目《中国三分钟(China Mosaic)》为例,使用Python工具软件抓取得到评论数据样本,能很好地分析海外受众对主题类型的关注偏向和态度取向。从整体上看,海外受众对我国国家形象呈现出积极认可的态度,主要表现为:高度肯定我国的经济实力和科教发展成果,赞许我大国外交所起到的作用和经济建设成就,向往中华优秀传统文化等。但由于意识形态、东西方认知的差异,在海外受众的评论中也存在一些杂音,需要我们理性对待、主动消弭。在对外传播中,要用海外受众乐于接受的话语表达方式、共通的符号,从大处着眼、小处入手讲述中国故事,掌握议题设置的主动权,传播好中国声音。

Landslide data mosaicking based on an airborne laser point cloud and multi-beam sonar images

Landslides are one of the most disastrous geological hazards in southwestern China.Once a landslide becomes unstable,it threatens the lives and safety of local residents.However,empirical studies on landslides have predominantly focused on landslides that occur on land.To this end,we aim to investigate ashore and underwater landslide data synchronously.This study proposes an optimized mosaicking method for ashore and underwater landslide data.This method fuses an airborne laser point cloud with multi-beam depth sounder images.Owing to their relatively high efficiency and large coverage area,airborne laser measurement systems are suitable for emergency investigations of landslides.Based on the airborne laser point cloud,the traversal of the point with the lowest elevation value in the point set can be used to perform rapid extraction of the crude channel boundaries.Further meticulous extraction of the channel boundaries is then implemented using the probability mean value optimization method.In addition,synthesis of the integrated ashore and underwater landslide data angle is realized using the spatial guide line between the channel boundaries and the underwater multibeam sonar images.A landslide located on the right bank of the middle reaches of the Yalong River is selected as a case study to demonstrate that the proposed method has higher precision thantraditional methods.The experimental results show that the mosaicking method in this study can meet the basic needs of landslide modeling and provide a basis for qualitative and quantitative analysis and stability prediction of landslides.

MOSAiC现场观测期间海冰厚度季节变化模拟误差分析

北极气候研究多学科漂流观测计划(Multidisciplinary drifting Observatory for the Study of Arctic Climate, MOSAiC)于2019年10月至2020年9月开展,期间获得了变量完整的大气、海洋、海冰厚度及积雪厚度观测,为海冰模式的发展提供了新的契机。本研究利用两个完整观测时段(2019年11月1日至2020年5月7日、2020年6月26日至7月27日)的大气和海洋强迫场,驱动一维海冰柱模式ICEPACK,模拟了MOSAiC期间海冰厚度的季节演变,同海冰厚度观测进行了对比,并诊断分析了海冰厚度模拟误差的原因。结果表明,在冬春季节,模式可以再现海冰厚度增长过程,但由于模式在春季高估了积雪向海冰的转化及对海冰物质平衡的贡献,模拟的春季海冰厚度偏厚。在夏季期间,2种热力学方案及3种融池方案的组合都表明模式高估了海冰表层的消融过程,导致模拟结束阶段的海冰厚度偏薄。我们的研究表明,使用变量完整的MOSAiC大气和海洋强迫场可以诊断目前海冰模式中的问题,为海冰模式的改进奠定基础。

A Real Time Mosaic Method for Remote Sensing Video Images from UAV

At present, in order to get a large field of view image, image mosaic technique has been widely applied in UAV remote sensing platform. The traditional mosaic system for UAV remote sensing image takes a lot of time and man power, it is difficult to complete the image stitching automatically. In the paper, an approach for geometric correction of remote sensing image without any ground control points is presented, and the SIFT algorithm is used to extract and match feature points. Finally, the weighted average fusion method is used to smooth the image after splicing and an automatic mosaic system for UAV remote sensing video images is developed. In order to verify the system, some splicing ex-periments using UAV actual aerial photography images have been done and good results have been achieved.

Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.

Background removal from global auroral images:Data-driven dayglow modeling

Global images of auroras obtained by cameras on spacecraft are a key tool for studying the near-Earth environment.However,the cameras are sensitive not only to auroral emissions produced by precipitating particles,but also to dayglow emissions produced by photoelectrons induced by sunlight.Nightglow emissions and scattered sunlight can contribute to the background signal.To fully utilize such images in space science,background contamination must be removed to isolate the auroral signal.Here we outline a data-driven approach to modeling the background intensity in multiple images by formulating linear inverse problems based on B-splines and spherical harmonics.The approach is robust,flexible,and iteratively deselects outliers,such as auroral emissions.The final model is smooth across the terminator and accounts for slow temporal variations and large-scale asymmetries in the dayglow.We demonstrate the model by using the three far ultraviolet cameras on the Imager for Magnetopause-to-Aurora Global Exploration(IMAGE)mission.The method can be applied to historical missions and is relevant for upcoming missions,such as the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.

Mosaic of the Curved Human Retinal Images Based on the Scale-Invariant Feature Transform

To meet the needs in the fundus examination,including outlook widening,pathology tracking,etc.,this paper describes a robust feature-based method for fully-automatic mosaic of the curved human retinal images photographed by a fundus microscope. The kernel of this new algorithm is the scale-,rotation-and illumination-invariant interest point detector & feature descriptor-Scale-Invariant Feature Transform. When matched interest points according to second-nearest-neighbor strategy,the parameters of the model are estimated using the correct matches of the interest points,extracted by a new inlier identification scheme based on Sampson distance from putative sets. In order to preserve image features,bilinear warping and multi-band blending techniques are used to create panoramic retinal images. Experiments show that the proposed method works well with rejection error in 0.3 pixels,even for those cases where the retinal images without discernable vascular structure in contrast to the state-of-the-art algorithms.

Deep learning-based inpainting of saturation artifacts in optical coherence tomography images

Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.

Artificial Intelligence and Computer Vision during Surgery: Discussing Laparoscopic Images with ChatGPT4—Preliminary Results

Introduction: Ultrafast latest developments in artificial intelligence (ΑΙ) have recently multiplied concerns regarding the future of robotic autonomy in surgery. However, the literature on the topic is still scarce. Aim: To test a novel AI commercially available tool for image analysis on a series of laparoscopic scenes. Methods: The research tools included OPENAI CHATGPT 4.0 with its corresponding image recognition plugin which was fed with a list of 100 laparoscopic selected snapshots from common surgical procedures. In order to score reliability of received responses from image-recognition bot, two corresponding scales were developed ranging from 0 - 5. The set of images was divided into two groups: unlabeled (Group A) and labeled (Group B), and according to the type of surgical procedure or image resolution. Results: AI was able to recognize correctly the context of surgical-related images in 97% of its reports. For the labeled surgical pictures, the image-processing bot scored 3.95/5 (79%), whilst for the unlabeled, it scored 2.905/5 (58.1%). Phases of the procedure were commented in detail, after all successful interpretations. With rates 4 - 5/5, the chatbot was able to talk in detail about the indications, contraindications, stages, instrumentation, complications and outcome rates of the operation discussed. Conclusion: Interaction between surgeon and chatbot appears to be an interesting frontend for further research by clinicians in parallel with evolution of its complex underlying infrastructure. In this early phase of using artificial intelligence for image recognition in surgery, no safe conclusions can be drawn by small cohorts with commercially available software. Further development of medically-oriented AI software and clinical world awareness are expected to bring fruitful information on the topic in the years to come.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

Reconstruction of Knowledge and Medical Images in the Convergence of Chinese and Western Medicine:Taking “Sweet Meat” as an Example

The pancreas is neither part of the five Zang organs(五脏) nor the six Fu organs(六腑).Thus,it has received little attention in Chinese medical literature.In the late 19th century,medical missionaries in China started translating and introducing anatomical and physiological knowledge about the pancreas.As for the word pancreas,an early and influential translation was “sweet meat”(甜肉),proposed by Benjamin Hobson(合信).The translation “sweet meat” is not faithful to the original meaning of “pancreas”,but is a term coined by Hobson based on his personal habits,and the word “sweet” appeared by chance.However,in the decades since the term “sweet meat” became popular,Chinese medicine practitioners,such as Tang Zonghai(唐宗海),reinterpreted it by drawing new medical illustrations for “sweet meat” and giving new connotations to the word “sweet”.This discussion and interpretation of “sweet meat” in modern China,particularly among Chinese medicine professionals,is not only a dissemination and interpretation of the knowledge of “pancreas”,but also a construction of knowledge around the term “sweet meat”.

A New Method of Mosaicking Context Camera (CTX) Images for the Geomorphological Study of Martian Landscape

Various spacecraft and satellites from the world’s best space agencies are exploring Mars since 1970, constantly with great ability to capture the maximum amount of dataset for a better understanding of the red planet. In this paper, we propose a new method for making a mosaic of Mars Reconnaissance Orbiter (MRO) spacecraft payload Context Camera (CTX) images. In this procedure, we used ERDAS Imagine for image rectification and mosaicking as a tool for image processing, which is a new and unique method of generating a mosaic of thousands of CTX images to visualize the large-scale areas. The output product will be applicable for mapping of Martian geomorphological features, 2D mapping of the linear feature with high resolution, crater counting, and morphometric analysis to a certain extent.

Development of a stable attenuated double-mutant of tobacco mosaic virus for cross-protection

Tobacco(Nicotiana tabacum)and tomato(Solanum lycopersicum)are two major economic crops in China.Tobacco mosaic virus(TMV;genus Tobamovirus)is the most prevalent virus infecting both crops.Currently,some widely cultivated tobacco and tomato cultivars are susceptible to TMV and there is no effective strategy to control this virus.Cross-protection can be a safe and environmentally friendly strategy to prevent viral diseases.However,stable attenuated TMV mutants are scarce.In this study,we found that the substitutions in the replicase p126,arginine at position 196(R^(196))with aspartic acid(D),glutamic acid at position 614(E^(614))with glycine(G),serine at position 643(S^(643))with phenylalanine(F),or D at position 730(D^(730))with S,significantly reduced the virulence and replication of TMV.However,only the mutation of S^(643) to F reduced the RNA silencing suppression activity of TMV p126.A double-mutant TMV-E614G-S643F induced no visible symptom and was genetically stable through six successive passages in tobacco plants.Furthermore,our results showed that TMV-E614G-S643F double-mutant could provide effective protection against the wild-type TMV infection in tobacco and tomato plants.This study reports a promising mild mutant for cross-protection to control TMV in tobacco and tomato plants.

Floristic Diversity, Stand Structure and Plant Life Traits in the Forest-Savanna Mosaic at Ndjole (Centre Cameroon)

With the aim of assessing floristic diversity, stand structure and vegetation ecology, the present work was carried out in the savanna mosaic forest of the locality of Ndjole (Centre Cameroon). A total of 30 floristic plots of 40 m × 40 m (1600 m2) were delineated in the different ecosystems (forest, forest-savanna contact zone and the savanna). Within each plot, all individuals with a diameter at breast height ≥ 10 cm at 1.30 m were taken into account. ANOVA was used to compare the means of the diversity indices of the different ecosystems, and the DUNCAN test at 5% significance level (XLSTAT 2016) to separate these means. A total of 85 species belonging to 67 genera and 33 families were recorded. That is, 68 species in semi-caducifolia forest, 45 in forest-savanna contact zone and 18 in savanna. Urticaceae, Combretaceae, Apocynaceae, Myristicaceae and Euphorbiaceae are the most diverse families in the forest. On the other hand, in contact zones, the Euphorbiaceae, Fabaceae, Annonaceae and Moraceae families are the most diversified, while in the savannas, the Combretaceae, Fabaceae and Annonaceae families are the most diversified. Shannon’s diversity index values (1.7 to 2.37 bits) reflect low specific diversity;Piélou’s equitability (0.86 to 0.9) shows good distribution of individuals within species. The diametric structure of woody species shows an inverted “J” shape in all plant communities. This indicates a high regeneration potential characterized by a greater number of small trees than large trees. Mesophanerophytes and megaphanerophytes are dominant in forests and forest-savanna contact zones, and microphanerophytes in the savannas. Endozoochory is the main mode of diaspore dissemination. As regards the distribution of phytogeographical types, it shows the overall dominance of Guineo-Congolese species in all plant communities. Our study provides detailed data on forest-savanna floristic and plant life traits in the mosaic and recommends further studies on the impact of environmental parameter variations on the forest encroachment into savanna.

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

CRISPR/CasRx-mediated resistance to Soybean mosaic virus in soybean

Soybean mosaic virus(SMV),an RNA virus,is the most common and destructive pathogenic virus in soybean fields.The newly developed CRISPR/Cas immune system has provided a novel strategy for improving plant resistance to viruses;hence,this study aimed to engineer SMV resistance in soybean using this system.Specifically,multiple sgRNAs were designed to target positive-and/or negative-sense strands of the SMV HC-Pro gene.Subsequently,the corresponding CRISPR/CasRx vectors were constructed and transformed into soybeans.After inoculation with SMV,39.02%,35.77%,and 18.70%of T_(1)plants were confirmed to be highly resistant(HR),resistant(R),and mildly resistant(MR)to SMV,respectively,whereas only 6.50%were identified as susceptible(S).Additionally,qRT-PCR and DAS-ELISA showed that,both at 15 and 30 d post-inoculation(dpi),SMV accumulation significantly decreased or was even undetectable in HR and R plants,followed by MR and S plants.Additionally,the expression level of the CasRx gene varied in almost all T_(1)plants with different resistance level,both at 15 and 30 dpi.Furthermore,when SMV resistance was evaluated in the T_(2)generation,the results were similar to those recorded for the T_(1)generation.These findings provide new insights into the application of the CRISPR/CasRx system for soybean improvement and offer a promising alternative strategy for breeding for resistance to biotic stress that will contribute to the development of SMV-immune soybean germplasm to accelerate progress towards greater soybean crop productivity.

Turnip mosaic virus pathogenesis and host resistance mechanisms in Brassica

Turnip mosaic virus(TuMV)is a devastating potyvirus pathogen that infects a wide variety of both cultivated and wild Brassicaceae plants.We urgently need more information and understanding of TuMV pathogenesis and the host responses involved in disease development in cruciferous crops.TuMV displays great versatility in viral pathogenesis,especially in its replication and intercellular movement.Moreover,in the coevolutionary arms races between TuMV and its hosts,the virus has evolved to co-opt host factors to facilitate its infection and counter host defense responses.This review mainly focuses on recent advances in understanding the viral factors that contribute to the TuMV infection cycle and the host resistance mechanism in Brassica.Finally,we propose some future research directions on TuMV pathogenesis and control strategies to design durable TuMV-resistant Brassica crops.

Automated Algorithms for Detecting and Classifying X-Ray Images of Spine Fractures

This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spinefractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picturesegmentation, feature reduction, and image classification. Two important elements are investigated to reducethe classification time: Using feature reduction software and leveraging the capabilities of sophisticated digitalprocessing hardware. The researchers use different algorithms for picture enhancement, including theWiener andKalman filters, and they look into two background correction techniques. The article presents a technique forextracting textural features and evaluates three picture segmentation algorithms and three fractured spine detectionalgorithms using transformdomain, PowerDensity Spectrum(PDS), andHigher-Order Statistics (HOS) for featureextraction.With an emphasis on reducing digital processing time, this all-encompassing method helps to create asimplified system for classifying fractured spine fractures. A feature reduction program code has been built toimprove the processing speed for picture classification. Overall, the proposed approach shows great potential forsignificantly reducing classification time in clinical settings where time is critical. In comparison to other transformdomains, the texture features’ discrete cosine transform (DCT) yielded an exceptional classification rate, and theprocess of extracting features from the transform domain took less time. More capable hardware can also result inquicker execution times for the feature extraction algorithms.