Background Objects Research Articles

Impact of Background, Foreground, and Manipulated Object Rendering on Egocentric Depth Perception in Virtual and Augmented Indoor Environments.

This research investigated how the similarity of the rendering parameters of background and foreground objects affected egocentric depth perception in indoor virtual and augmented environments. We refer to the similarity of the rendering parameters as visual 'congruence'. Study participants manipulated the depth of a sphere to match the depth of a designated target peg. In the first experiment, the sphere and peg were both virtual, while in the second experiment, the sphere is virtual and the peg is real. In both experiments, depth perception accuracy was found to depend on the levels of realism and congruence between the sphere, pegs, and background. In Experiment 1, realistic backgrounds lead to overestimation of depth, but resulted in underestimation when the background was virtual, and when depth cues were applied to the sphere and target peg. In Experiment 2, background and target pegs were real but matched with the virtual sphere; in comparison to Experiment 1, realistically rendered targets prompted an underestimation and more accuracy with the manipulated object. These findings suggest that congruence can affect distance estimation and the underestimation effect in the AR environment resulted from increased graphical fidelity of the foreground target and background.

SemiRS-COC: Semi-Supervised Classification for Complex Remote Sensing Scenes with Cross-Object Consistency.

Semi-supervised learning (SSL), which aims to learn with limited labeled data and massive amounts of unlabeled data, offers a promising approach to exploit the massive amounts of satellite Earth observation images. The fundamental concept underlying most state-of-the-art SSL methods involves generating pseudo-labels for unlabeled data based on image-level predictions. However, complex remote sensing (RS) scene images frequently encounter challenges, such as interference from multiple background objects and significant intra-class differences, resulting in unreliable pseudo-labels. In this paper, we propose the SemiRS-COC, a novel semi-supervised classification method for complex RS scenes. Inspired by the idea that neighboring objects in feature space should share consistent semantic labels, SemiRS-COC utilizes the similarity between foreground objects in RS images to generate reliable object-level pseudo-labels, effectively addressing the issues of multiple background objects and significant intra-class differences in complex RS images. Specifically, we first design a Local Self-Learning Object Perception (LSLOP) mechanism, which transforms multiple background objects interference of RS images into usable annotation information, enhancing the model's object perception capability. Furthermore, we present a Cross-Object Consistency Pseudo-Labeling (COCPL) strategy, which generates reliable object-level pseudo-labels by comparing the similarity of foreground objects across different RS images, effectively handling significant intra-class differences. Extensive experiments demonstrate that our proposed method achieves excellent performance compared to state-of-the-art methods on three widely-adopted RS datasets.

TUMamba: A novel tongue segment methods based on Mamba and U-Net.

Current tongue segmentation methods often struggle with extracting global features and performing selective filtering, particularly in complex environments where background objects resemble the tongue. These challenges significantly reduce segmentation efficiency. To address these issues, this article proposes a novel model for tongue segmentation in complex environments, combining Mamba and U-Net. By leveraging Mamba's global feature selection capabilities, this model assists U-Net in accurately excluding tongue-like objects from the background, thereby enhancing segmentation accuracy and efficiency. To improved the segmentation accuracy of the U-Net backbone model, we incorporated the Mamba attention module along with a multi-stage feature fusion module. The Mamba attention module serially connects spatial and channel attention mechanisms at the U-Net 's skip connections, selectively filtering the feature maps passed into the deep network. Additionally, the multi-stage feature fusion module integrates feature maps from different stages, further improving segmentation performance. Compared with state-of-the-art semantic segmentation and tongue segmentation models, our model improved the mean intersection over union by 1.17%. Ablation experiments further demonstrated that each module proposed in this study contributes to enhancing the model's segmentation efficiency. This study constructs a Tongue segmentation model based on U-Net and Mamba (TUMamba). The model effectively extracted global spatial and channel features using the Mamba attention module, captured local detail features through U-Net, and enhanced image features via multi-stage feature fusion. The results demonstrate that the model performs exceptionally well in tongue segmentation tasks, proving its value in handling complex environments.

Searching for Planets Orbiting Fomalhaut with JWST/NIRCam

We report observations with the JWST/NIRCam coronagraph of the Fomalhaut (α PsA) system. This nearby A star hosts a complex debris disk system discovered by the IRAS satellite. Observations in F444W and F356W filters using the round 430R mask achieve a contrast ratio of ∼4 × 10−7 at 1″ and ∼4 × 10−8 outside of 3″. These observations reach a sensitivity limit of <1 M Jup across most of the disk region. Consistent with the hypothesis that Fomalhaut b is not a massive planet but is a dust cloud from a planetesimal collision, we do not detect it in either F356W or F444W (the latter band where a Jovian-sized planet should be bright). We have reliably detected 10 sources in and around Fomalhaut and its debris disk, all but one of which are coincident with Keck or Hubble Space Telescope sources seen in earlier coronagraphic imaging; we show them to be background objects, including the “Great Dust Cloud” identified in Mid-Infrared Instrument (MIRI) data. However, one of the objects, located at the edge of the inner dust disk seen in the MIRI images, has no obvious counterpart in imaging at earlier epochs and has a relatively red [F356W]–[F444W] > 0.7 mag (Vega) color. Whether this object is a background galaxy, brown dwarf, or a Jovian-mass planet in the Fomalhaut system will be determined by an approved Cycle 2 follow-up program. Finally, we set upper limits to any scattered light from the outer ring, placing a weak limit on the dust albedo at F356W and F444W.

A bright triple transient that vanished within 50 min

ABSTRACT We report on three optically bright, ∼16th mag, point sources within 10 arcsec of each other that vanished within 1 h, based on two consecutive exposures at Palomar Observatory on 1952 July 19 (POSS I Red and Blue). The three point sources have continued to be absent in telescope exposures during 71 yr with detection thresholds of ∼21st mag. We obtained two deep exposures with the 10.4-m Gran Telescopio Canarias on 2023 April 25 and 27 in r and g band, both reaching magnitude 25.5 (3σ). The three point sources are still absent, implying they have dimmed by more than 10 mag within an hour back in 1952. When bright in 1952, the most isolated transient source has a profile nearly the same as comparison stars, implying the sources are subarcsec in angular size and they exhibit no elongation due to movement. This triple transient has observed properties similar to other cases where groups of transients (‘multiple transients’) have appeared and vanished in a small region within a plate exposure. The explanation for these three transients and the previously reported cases remains unclear. Models involving background objects that are optically luminous for less than 1 h coupled with foreground gravitational lensing seem plausible. If so, a significant population of massive objects with structure serving as the lenses, to produce three images, are required to explain the subhour transients.

TAEKWONDO POSE ESTIMATION WITH DEEP LEARNING ARCHITECTURES ON ONE-DIMENSIONAL AND TWO-DIMENSIONAL DATA

Practicing sports is an activity that helps people maintain and improve their health, enhance memory and concentration, reduce anxiety and stress, and train teamwork and leadership ability. With the development of science and technology, artificial intelligence in sports has become increasingly popular with the public and brings many benefits. In particular, many applications help people track and evaluate athletes' achievements in competitions. This study extracts images from Taekwondo videos and generates skeleton data from frames using the Fast Forward Moving Picture Experts Group (FFMPEG) technique using MoveNet. After that, we use deep learning architectures such as Long Short-Term Memory Networks, Convolutional Long Short-Term Memory, and Long-term Recurrent Convolutional Networks to perform the poses classification tasks in Taegeuk in Jang lessons. This work presents two approaches. The first approach uses a sequence skeleton extracted from the image by Movenet. Second, we use sequence images to train using video classification architecture. Finally, we recognize poses in sports lessons using skeleton data to remove noise in the image, such as background and extraneous objects behind the exerciser. As a result, our proposed method has achieved promising performance in pose classification tasks in an introductory Taekwondo lesson.

The physical origins of gas in the circumgalactic medium using observationally motivated TNG50 mocks

ABSTRACT Absorbers in the spectrum of background objects probe the circumgalactic medium (CGM) surrounding galaxies, but its physical properties remain unconstrained. We use the cosmological hydrodynamical simulation TNG50 to statistically trace the origins of ${\rm H\, {\small I}}$ Ly α absorbers around galaxies at z = 0.5 with stellar masses ranging from 108 to 1011 M⊙. We emulate observational CGM studies by considering all gas within a line of sight velocity range of ±500 kms−1 from the central, to quantitatively assess the impact of other galaxy haloes and overdense gas in the IGM that intersect sightlines. We find that 75 per cent of ${\rm H\, {\small I}}$ absorbers with column densities $\log [N(\mbox{${\rm H\, {\small I}}$})/\rm {cm}^{-2}]\gt 16.0$ trace the central galaxy within ±150 (80) kms−1 of M* = 1010(108) M⊙ central galaxies. The impact of satellites to the total absorber fraction is most significant at impact parameters 0.5Rvir &amp;lt; b &amp;lt; Rvir, and satellites with masses below typical detection limits (M* &amp;lt; 108 M⊙) account for 10 (40) per cent of absorbers that intersect any satellite bound to 1010 and 1011 (109) M⊙ centrals. After confirming outflows are more dominant along the minor axis, we additionally show that at least 20 per cent of absorbers exhibit no significant radial movement, indicating that absorbers can also trace quasi-static gas. Our work shows that determining the stellar mass of galaxies at zabs is essential to constrain the physical origin of the gas traced in absorption, which in turn is key to characterizing the kinematics and distribution of gas and metals in the CGM.

Classification of Chandra X-Ray Sources in Cygnus OB2

We have devised a predominantly Naive Bayes−based method to classify X-ray sources detected by Chandra in the Cygnus OB2 association into members, foreground objects, and background objects. We employ a variety of X-ray, optical, and infrared characteristics to construct likelihoods using training sets defined by well-measured sources. Combinations of optical photometry from the Sloan Digital Sky Survey (riz) and Isaac Newton Telescope Photometric Hα Survey (r I i I Hα), infrared magnitudes from United Kingdom Infrared Telescope Deep Sky Survey and Two-Micron All Sky Survey (JHK), X-ray quantiles and hardness ratios, and estimates of extinction A v are used to compute the relative probabilities that a given source belongs to one of the classes. Principal component analysis is used to isolate the best axes for separating the classes for the photometric data, and Gaussian component separation is used for X-ray hardness and extinction. Errors in the measurements are accounted for by modeling as Gaussians and integrating over likelihoods approximated as quartic polynomials. We evaluate the accuracy of the classification by inspection and reclassify a number of sources based on infrared magnitudes, the presence of disks, and spectral hardness induced by flaring. We also consider systematic errors due to extinction. Of the 7924 X-ray detections, 5501 have a total of 5597 optical/infrared matches, including 78 with multiple counterparts. We find that ≈6100 objects are likely association members, ≈1400 are background objects, and ≈500 are foreground objects, with an accuracy of 96%, 93%, and 80%, respectively, with an overall classification accuracy of approximately 95%.

EM-YOLO: An X-ray Prohibited-Item-Detection Method Based on Edge and Material Information Fusion.

Using X-ray imaging in security inspections is common for the detection of objects. X-ray security images have strong texture and RGB features as well as the characteristics of background clutter and object overlap, which makes X-ray imaging very different from other real-world imaging methods. To better detect prohibited items in security X-ray images with these characteristics, we propose EM-YOLOv7, which is composed of both an edge feature extractor (EFE) and a material feature extractor (MFE). We used the Soft-WIoU NMS method to solve the problem of object overlap. To better extract features, the attention mechanism CBAM was added to the backbone. According to the results of several experiments on the SIXray dataset, our EM-YOLOv7 method can better complete prohibited-item-detection tasks during security inspection with detection accuracy that is 4% and 0.9% higher than that of YOLOv5 and YOLOv7, respectively, and other SOTA models.

Quantifying surfcam imagery to measure the 2022 Hunga Tonga-Hunga Ha'apai tsunami along the California coast

On January 15, 2022, the eruption of the Hunga Tonga-Hunga Ha'apai volcano created a tsunami that reached a height of 20.5 m in the near field. The tsunami impacted the entire Pacific Ocean basin and was the largest tsunami to impact California since the 2011 Tōhoku tsunami. Videos recorded on surfcams during the event were downloaded from three sites along the California coast (Santa Cruz, Pismo Beach, and Santa Barbara Harbor). Airborne Lidar data, shadow projections, and vessel traffic information was used to rectify imagery instead of traditional field methods. Water level was extracted at 2 Hz based on the change in brightness relative to a fixed background object and filtered to remove swell and tidal influences. Tsunami inundation was captured at all three sites, with image extracted maximum tsunami heights measuring 1.42 m at Santa Cruz, 1.07 m at Pismo Beach, and 0.49 m at Santa Barbara Harbor. Comparisons to nearby in-situ water level measurements were made to calculate the accuracy, with a RMSE of 0.41 m, 0.52 m, and 0.17 m at each site, respectively. Analysis of surfcam extracted water levels measurements show promising results, and in combination with providing holistic monitoring of the scene, suggests an alternative platform for future tsunami events.

Outskirts of Abell 1795: Probing gas clumping in the intracluster medium

Contact. The outskirts of galaxy clusters host complex interactions between the intracluster and circumcluster media. During the evolution of clusters, ram-pressure stripped gas clumps from infalling substructures break the uniformity of the gas distribution, potentially leading to observational biases at large radii. However, assessing the contribution of gas clumping poses observational challenges and requires robust X-ray measurements in the background-dominated regime of the cluster outskirts. Aims. The main objectives of this study are to isolate faint gas clumps from field sources and from the diffuse emission in the Abell 1795 galaxy cluster, then to probe their impact on the observed surface brightness and thermodynamic profiles. Methods. We performed an imaging analysis on deep Chandra ACIS-I observations of the Abell 1795 cluster outskirts, extending out to ∼1.5r200 with full azimuthal coverage. We built the 0.7 − 2.0 keV surface brightness distribution from the adaptively binned image of the diffuse emission and looked for clumps in the form of &gt; + 2σ surface brightness outliers. Our classification of the clump candidates was based primarily on Chandra and SDSS data. Benefiting from the point source list resolved by Chandra, we extracted the thermodynamic profiles of the intracluster medium from the associated Suzaku XIS data out to r200 using multiple point source and clump candidate removal approaches. Results. We identified 24 clump candidates in the Abell 1795 field, most of which are likely to be associated with background objects, including active galactic nuclei, galaxies, and clusters or groups of galaxies, as opposed to intrinsic gas clumps. These sources had minimal impact on the surface brightness and thermodynamic profiles of the cluster emission. After correcting for clump candidates, the measured entropy profile still deviates from a pure gravitational collapse, suggesting complex physics at play in the outskirts, which may include potential electron–ion non-equilibrium and non-thermal pressure support.

Efficient background subtraction method based on fast independent component analysis in video-surveillance

Modern video surveillance has now become an active area of research with a large set of requirements and various applications. In order to detect moving objects in video surveillance scenes, background subtraction techniques are the most used. In this paper, we developed and tested an efficient background subtraction technique in video surveillance based on the fast-independent component analysis (fast-ICA) method. The proposed technique initiated, first, on the use of a developed fast-ICA algorithm in order to estimate the de-mixing matrix and the denoising matrix parameters. Second, the estimated foreground can simply model by multiplying the data matrix with the de-mixing matrix. After that, the data matrix is multiplied by the denoising matrix for removing the noise. In addition, we propose a pre-processing and post-processing operations to effectively segment the true foreground objects and improve our results. The proposed method is evaluated on the publicly available change detection datasets CDnet 2012 and CDnet 2014 using performance parameters such as recall, precision and experimental results show that our algorithm can detect effectively and accurately the moving objects in several background and foreground conditions compared to other methods in literature with real-time frame rate.

Methodology for Evaluating Controlled Moisture Distribution in Woven Cotton Fabric Surfaces

ABSTRACT The proposed study aims to discuss the methodology for measuring and evaluating controlled moisture distribution in woven fabric surfaces in the first part and the construction of woven fabric using hydrophobic and hydrophilic cotton yarns in the second part. Moisture distribution can be controlled in terms of the anisotropic (horizontal and vertical) and isotropic characteristics of the wetting of the cotton woven fabrics based on the combination of hydrophobic and hydrophilic threads. A set of experimental samples using hydrophobic and hydrophilic threads in warp and weft was produced to monitor the moisture (water) distribution in woven fabric surfaces. For evaluating the controlled distribution of moisture (water) in these atypical woven fabric constructions, an algorithm (methodology) was proposed, where the object (liquid) was separated from the background (fabrics) and various objects and geometric characteristics depending on time were evaluated on the object. The algorithm itself consisted of several steps and was executed in a loop for the current frame at a given time.

High-Resolution Network with Transformer Embedding Parallel Detection for Small Object Detection in Optical Remote Sensing Images

Small object detection in remote sensing enables the identification and analysis of unapparent but important information, playing a crucial role in various ground monitoring tasks. Due to the small size, the available feature information contained in small objects is very limited, making them more easily buried by the complex background. As one of the research hotspots in remote sensing, although many breakthroughs have been made, there still exist two significant shortcomings for the existing approaches: first, the down-sampling operation commonly used for feature extraction can barely preserve weak features of objects in a tiny size; second, the convolutional neural network methods have limitations in modeling global context to address cluttered backgrounds. To tackle these issues, a high-resolution network with transformer embedding parallel detection (HRTP-Net) is proposed in this paper. A high-resolution feature fusion network (HR-FFN) is designed to solve the first problem by maintaining high spatial resolution features with enhanced semantic information. Furthermore, a Swin-transformer-based mixed attention module (STMA) is proposed to augment the object information in the transformer block by establishing a pixel-level correlation, thereby enabling global background–object modeling, which can address the second shortcoming. Finally, a parallel detection structure for remote sensing is constructed by integrating the attentional outputs of STMA with standard convolutional features. The proposed method effectively mitigates the impact of the intricate background on small objects. The comprehensive experiment results on three representative remote sensing datasets with small objects (MASATI, VEDAI and DOTA datasets) demonstrate that the proposed HRTP-Net achieves a promising and competitive performance.

Let the loss impartial: a hierarchical unbiased loss for small object segmentation in high-resolution remote sensing images

ABSTRACT The progress in optical remote sensing technology presents both a possibility and challenge for small object segmentation task. However, the gap between human vision cognition and machine behavior still poses an inherent constrains to the interpretation of small but key objects in large-scale remote sensing scenes. This paper summarizes this gap as a bias of the machine against small object segmentation task, called scale-induced bias. The scale-induced bias causes the degradation in the performance of conventional remote sensing image segmentation methods. Therefore, this paper applies a straightforward but innovative insight to mitigate the scale-induced bias. Specifically, we propose a universal impartial loss, which leverages the hierarchical approach to alleviate two sub-problems separately. The pixel-level statistical methodology is applied to remove the bias between the background and small objects, and an emendation vector is introduced to alleviate the bias between small object categories. Extensive experiments explicitly manifest that our method is fully compatible with the existing segmentation structures, armed with the hierarchical unbiased loss, these structures will achieve satisfactory improvement. The proposed method is validated on two benchmark remote sensing image datasets, where it achieved a competitive performance and could narrow the gap between the human vision cognition and machine behavior.

Industrial UAV-Based Unsupervised Domain Adaptive Crack Recognitions: From Database Towards Real-Site Infrastructural Inspections

The defect diagnosis of modern infrastructures is crucial to public safety. In this work, we propose an unsupervised domain adaptive crack recognition framework. To fulfill the unsupervised domain adaptation (UDA) task of cracks recognition in infrastructural inspections, we propose a robust unsupervised domain adaptive learning strategy termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Crack-DA</i> to increase the generalization capacity of the model in unseen test circumstances. More specifically, we first propose leveraging the self-supervised depth information to help the learning of semantics. And then using the edge information to suppress non-edge background objects and noises. We propose the data augmentation-based consistency to enhance the performance. More importantly, we proposed to use the disparity in depth to evaluate the domain gap in semantics and explicitly consider the domain gap in network optimization. A database composed of a large number of crack images with detailed pixel-level labels for network training is established. Extensive experiments on UAV-captured highway cracks and real-site UAV inspections of building cracks demonstrate the robustness and effectiveness of our proposed domain adaptive crack recognition approach.

Экологическая оценка варьирования состава травянистого покрова по контрастным элементам мезорельефа Лесной Опытной Дачи РГАУ-МСХА имени К.А. Тимирязева

The present paper analyzes the results of a comparative ecological study of regional typological patterns of spatial variability of the herbaceous cover composition in the conditions of a gently sloping mesorelief at the Forest Experimental Station of the Russian Timiryazev State Agricultural University – the background object of environmental monitoring for the northern part of the Moscow metropolis. Standardized descriptions of the composition and biodiversity assessments of the vegetation cover are presented on five representative sites of the orographic transect, cutting in the direction from northeast to southwest the gently sloping moraine hill dominating in the mesorelief and its opposite slopes of the northeastern and southwestern exposures, including their foots – with a total height difference not more than 15 meters. The greatest variety of herbaceous species from the 20 families represented in the studied plots was found at the foot of slopes with more stable humidification conditions, and its minimum level was at the summit of a moraine hill with pronounced summer desiccation of the topsoil of the zonal sod Podzoluvisols characteristic for the Forest Experimental Station. Other important ecological factors of herbaceous biodiversity are the exposure and shape of the slope, which affect both the temperature regime and the seasonal dynamics of the topsoil moisture – under conditions of relatively insignificant variation in the tree canopy density and pronounced recreational load on the ground vegetation and topsoil. Humus-podzolic soils dominating on the bottom of the slope of the north-eastern exposure with a steadily increased moisture level are characterized by reduced resistance to recreational load, which leads to the maximum severity of the pathway network and relatively reduced biodiversity indices. The analysis of the patterns of spatial variation in the composition of the herbaceous cover allowed one to identify the problematic environmental situations and factors of reducing its biodiversity, that has to be taken into account when further planning the regulation of recreational load at the Forest Experimental Station and similar areas of the forest infrastructure of the northern part of Moscow, as well as when assessing the projected anthropogenic impact in the conditions of the Moscow megalopolis and comparable urban ecosystems of the southern taiga and mixed-forest subzones at the European territory of Russia. Keywords: BIODIVERSITY, URBAN FOREST PARKS, FOREST ECOSYSTEMS, MOSCOW, IMPACT ASSESSMENT, RECREATIONAL LOAD, HERBACEOUS COVER, ENVIRONMENTAL FACTORS, URBAN ECOSYSTEMS

Recognition and Extraction of Power Transmission Lines Based on Infrared Image Processing for Line-following Robots

To further improve the real-time performance and accuracy of power transmission line maintenance, this paper primarily focuses on the preliminary line recognition and extraction method based on thermal image processing of infrared images collected by line-following robots for thermal fault detection. Firstly, filtering and noise reduction techniques along with enhanced image processing are applied to preprocess the collected infrared images. This effectively addresses the noise and interference from background objects, which can affect the extraction of overheated areas on the lines, while also reducing the computational memory required for subsequent image processing. Subsequently, an improved Canny edge detection algorithm is employed to extract the edges of foreground objects in the images. Additionally, a region-growing algorithm combined with the geometric features of the lines is employed to filter out unwanted thermal sources, enabling the accurate segmentation and extraction of power transmission lines. This forms a solid foundation for subsequent detection and identification of abnormal hotspots on the extracted lines, and holds significance for the inspection and maintenance of existing thermal faults and potential hotspots in power transmission lines.

Transformer-Guided Video Inpainting Algorithm Based on Local Spatial-Temporal joint

INTRODUCTION: Video inpainting is a very important task in computer vision, and it’s a key component of various practical applications. It also plays an important role in video occlusion removal, traffic monitoring and old movie restoration technology. Video inpainting is to obtain reasonable content from the video sequence to fill the missing region, and maintain time continuity and spatial consistency.OBJECTIVES: In previous studies, due to the complexity of the scene of video inpainting, there are often cases of fast motion of objects in the video or motion of background objects, which will lead to optical flow failure. So the current video inpainting algorithm hasn’t met the requirements of practical applications. In order to avoid the problem of optical flow failure, this paper proposes a transformer-guided video inpainting model based on local Spatial-temporal joint.METHODS: First, considering the rich Spatial-temporal relationship between local flows, a Local Spatial-Temporal Joint Network (LSTN) including encoder, decoder and transformer module is designed to roughly inpaint the local corrupted frames, and the Deep Flow Network is used to calculate the local bidirectional corrupted flows. Then, the local corrupted optical flow map is input into the Local Flow Completion Network (LFCN) with pseudo 3D convolution and attention mechanism to obtain a complete set of bidirectional local optical flow maps. Finally, the roughly inpainted local frame and the complete bidirectional local optical flow map are sent to the Spatial-temporal transformer and the inpainted video frame is output.RESULTS: Experiments show that the algorithm achieves high quality results in the video target removal task, and has a certain improvement in indicators compared with advanced technologies.CONCLUSION: Transformer-Guided Video Inpainting Algorithm Based on Local Spatial-Temporal joint can obtain high-quality optical flow information and inpainted result video.

ALMA and Keck analysis of Fomalhaut field sources: JWST’s Great Dust Cloud is a background object

ABSTRACT At 7.7 pc, the A-type star Fomalhaut hosts a bright debris disc with multiple radial components. The disc is eccentric and misaligned, strongly suggesting that it is sculpted by interaction with one or more planets. Compact sources are now being detected with JWST, suggesting that new planet detections may be imminent. However, to confirm such sources as companions, common proper motion with the star must be established, as with unprecedented sensitivity comes a high probability that planet candidates are actually background objects. Here, Atacama Large Millimetre/Submillimetre Array and Keck observations of Fomalhaut are found to show significant emission at the same sky location as multiple compact sources in JWST Mid-Infrared Instrument coronagraphic observations, one of which has been dubbed the ‘Great Dust Cloud’ because it lies within the outer belt. Since the ground-based data were obtained between 6 and 18 yr prior to the JWST observations, these compact sources are unlikely to be common proper motion companions to Fomalhaut. More generally, this work illustrates that images collected at a range of wavelengths can be valuable for rejecting planet candidates uncovered via direct imaging with JWST.