Image Location Research Articles

A novel multi-frame image super-resolution model based on regularized nonlinear diffusion with Caputo time fractional derivative

In this work, we introduce an innovative fractional nonlinear parabolic model using a time-fractional order derivative, specifically employing the Caputo sense for fractional differentiation. This model aims to enhance traditional super-resolution models, particularly in the context of multi-frame image super-resolution. Additionally, we incorporate a regularized Perona–Malik diffusion mechanism to control the speed and direction of diffusion at each image location. We begin our study by exploring the theoretical solvability of our proposed model. Firstly, we employ the Faedo–Galerkin approach to establish the existence and uniqueness of a weak solution for an auxiliary fractional super-resolution model. Subsequently, we use the Schauder fixed point method to demonstrate the existence and uniqueness of a weak solution for our model. To validate the effectiveness of our model in the multi-frame super-resolution (SR) context, we conduct numerical experiments on images featuring diverse characteristics, including corners and edges, while applying various warping, decimation, and blurring matrices to the low-resolution (LR) images. We start the evaluation by introducing an adaptive discrete scheme tailored to the proposed model. To prove the robustness of our approach, we subject our images to varying levels of noise. Additionally, we perform simulations on real data (videos). The obtained high-resolution (HR) results demonstrate notable efficiency and robustness against noise, outperforming competitive models both visually and quantitatively.

Crowdsourced geolocation: Detailed exploration of mathematical and computational modeling approaches

In emergency situations, social media platforms produce a vast amount of real-time data that holds immense value, particularly in the first 72 h following a disaster event. Despite previous efforts, efficiently determining the geographical location of images related to a new disaster remains an unresolved operational challenge. Currently, the state-of-the-art approach for dealing with these first response mapping is first filtering and then submitting the images to be geolocated to a volunteer crowd, assigning the images randomly to the volunteers. In this work, we extend our previous paper (Ballester et al., 2023) to explore the potential of artificial intelligence (AI) in aiding emergency responders and disaster relief organizations in geolocating social media images from a zone recently hit by a disaster. Our contributions include building two different models in which we try to (i) be able to learn volunteers’ error profiles and (ii) intelligently assign tasks to those volunteers who exhibit higher proficiency. Moreover, we present methods that outperform random allocation of tasks, analyze the effect on the models’ performance when varying numerous parameters, and show that for a given set of tasks and volunteers, we are able to process them with a significantly lower annotation budget, that is, we are able to make fewer volunteer solicitations without losing any quality on the final consensus.

Non-consensual intimate image distribution: Nature, removal, and implications for the Online Safety Act

Research was conducted in partnership with the Revenge Porn Helpline (RPH) to examine the location and removal of non-consensual intimate image (NCII) abuse. By examining reports to the helpline, data were collected to uncover where intimate images were being non-consensually distributed, how they were proportionally distributed across platforms, and avenues for image removal. The data confirm that social media plays a key role in NCII distribution and provides further insight into where images are being distributed outside of social media platforms. Data on image removal indicate that knowledge of how to navigate different types of platforms is important for image removal success, making contributions from organisations such as the RPH vital, and highlighting the need to make reporting processes more accessible. The findings also indicate significant gaps within the Online Safety Act which will need to be addressed if the Act is to effectively protect victim-survivors. In particular, the need to move beyond focusing on services with the largest user numbers and broadening the scope to include smaller high-risk and problematic platforms.

Convolutional Neural Network‐Based CT Image Segmentation of Kidney Tumours

ABSTRACTKidney tumours are one of the most common tumours in humans and the main current treatment is surgical removal. The CT images are usually manually segmented by a specialist for pre‐operative planning, but this can be influenced by the surgeon's experience and skill and can be time‐consuming. Due to the complex lesions and different morphologies of kidney tumours that make segmentation difficult, this article proposes a convolutional neural network‐based automatic segmentation method for CT images of kidney tumours to address the most common problems of boundary blurring and false positives in tumour segmentation images. The method is highly accurate and reliable, and is used to assist doctors in surgical planning as well as diagnostic treatment, relieving medical pressure to a certain extent. The EfficientNetV2‐UNet segmentation model proposed in this article includes three main parts: feature extractor, reconstruction network and Bayesian decision algorithm. Firstly, for the phenomenon of tumour false positives, the EfficientNetV2 feature extractor, which has high training accuracy and efficiency, is selected as the backbone network, which extracts shallow features such as tumour location, morphology and texture in the CT image by downsampling. Secondly, on the basis of the backbone network, the reconstruction network is designed, which mainly consists of conversion block, deconvolution block, convolution block and output block. Then, the up‐sampling architecture is constructed to gradually recover the spatial resolution of the feature map, fully identify the contextual information and form a complete encoding–decoding structure. Multi‐scale feature fusion is achieved by superimposing all levels of feature map channels on the left and right sides of the network, preventing the loss of details and performing accurate tumour segmentation. Finally, a Bayesian decision algorithm is designed for the edge blurring phenomenon of segmented tumours and cascaded over the output of the reconstruction network, combining the edge features of the original CT image and the segmented image for probability estimation, which is used to improve the accuracy of the model edge segmentation. Medical images in NII special format were converted to Numpy matrix format using python, and then more than 2000 CT images containing only kidney tumours were selected from the KiTS19 dataset as the dataset for the model, and the dimensions were standardised to 128 × 128, and the experimental results show that the model outperforms many other advanced models with good segmentation performance.

English foreign language learners’ perceptions of cultural elements in Chinese EFL textbooks

Our study aims to culturally analyze the textbook series in the context of China using a questionnaire taken from Kilickaya (2004). To this end, 635 EFL students participated and filled out a questionnaire. In addition, 31 students were randomly selected for a semi-structured interview. The results of the study indicated that students’ perceptions of the meaning and purpose of textbooks were found to be positive. As for materials on culture, it is argued that textbooks should include both macro and micro-cultures. The findings showed that the teaching contributions did a good job of presenting various perceptions from the students' points of view. Students felt that the textbooks represented diverse societies and were relevant to their home culture, but they felt that the manuals were less effective in presenting the values of social groups. The results demonstrated that the same location of images and text in textbooks is usually the display index. EFL textbooks for language learners in China are mainly composed of index relationships between texts, pictures, and tasks, showing a focus on descriptive meaning. Ultimately, the outcomes of this inquiry provide practical implications for EFL stakeholders.

Features of determining the location of the reference image on the current half-tone using the method of binary slices

Features of determining the location of the reference image on the current half-tone using the method of binary slices

Interactive image colorization method via dual local self-attention

The interactive colorization allows users to add a desired color to any location in a grayscale image to obtain the desired color image. While transformers can capture larger receptive fields compared to convolutional neural networks, their computational complexity in image colorization is too high. As a solution, this paper introduces a novel image colorization framework for processing image colorization assignments. In our framework, the dual local self-attention mechanism model is formulated as a solution for reducing the computational complexity associated with global self-attention mechanisms, where the improvement is to combine shift window local self-attention and feature space local self-attention. Both local spatial connections and distant relations are captured to enhance the local quality for the reconstruction. Prior to computing feature space local self-attention, a brightness similarity metric is introduced to cluster the interested area to different local regions. Aiming for real-time inference, the DUpsampling is adopted for signal reconstruction. Specifically, the lightweight convolutional layer is employed before the DUpsampling to mitigate artifacts. Our experimental results on multiple datasets demonstrate that our proposed method outperforms existing interactive colorization methods while having lower model parameters and computational complexity than other Transformer-based models.

Automated interpretation of retinal vein occlusion based on fundus fluorescein angiography images using deep learning: A retrospective, multi-center study

PurposeFundus fluorescein angiography (FFA) is the gold standard for retinal vein occlusion (RVO) diagnosis. This study aims to develop a deep learning-based system to diagnose and classify RVO using FFA images, addressing the challenges of time-consuming and variable interpretations by ophthalmologists. Methods4028 FFA images of 467 eyes from 463 patients were collected and annotated. Three convolutional neural networks (CNN) models (ResNet50, VGG19, InceptionV3) were trained to generate the label of image quality, eye, location, phase, lesions, diagnosis, and macular involvement. The performance of the models was evaluated by accuracy, precision, recall, F-1 score, the area under the curve, confusion matrix, human-machine comparison, and Clinical validation on three external data sets. ResultsThe InceptionV3 model outperformed ResNet50 and VGG19 in labeling and interpreting FFA images for RVO diagnosis, achieving 77.63%–96.45% accuracy for basic information labels and 81.72%–96.45% for RVO-relevant labels. The comparison between the best CNN and ophthalmologists showed up to 19% accuracy improvement with the inceptionV3. ConclusionThis study developed a deep learning model capable of automatically multi-label and multi-classification of FFA images for RVO diagnosis. The proposed system is anticipated to serve as a new tool for diagnosing RVO in places short of medical resources.

Where do ICU trainees really look? An eye-tracking analysis of gaze patterns during central venous catheter insertion

Background: There is limited knowledge about gaze patterns of intensive care unit (ICU) trainee doctors during the insertion of a central venous catheter (CVC). The primary objective of this study was to examine visual patterns exhibited by ICU trainee doctors during CVC insertion. Additionally, the study investigated whether differences in gaze patterns could be identified between more and less experienced trainee doctors. Methods: In a real-life, prospective observational study conducted at the interdisciplinary ICU at the University Hospital Zurich, Switzerland, ICU trainee doctors underwent eye-tracking during CVC insertion in a real ICU patient. Using mixed-effects model analyses, the primary outcomes were dwell time, first fixation duration, revisits, fixation count, and average fixation time on different areas of interest (AOI). Secondary outcomes were above eye-tracking outcome measures stratified according to experience level of participants. Results: Eighteen participants were included, of whom 10 were inexperienced and eight more experienced. Dwell time was highest for CVC preparation table ( p = 0.02), jugular vein on ultrasound image ( p < 0.001) and cervical puncture location ( p < 0.001). Concerning experience, dwell time and revisits on jugular vein on ultrasound image ( p = 0.02 and p = 0.04, respectively) and cervical puncture location ( p = 0.004 and p = 0.01, respectively) were decreased in more experienced ICU trainees. Conclusions: Various AOIs have distinct significance for ICU trainee doctors during CVC insertion. Experienced participants exhibited different gaze behavior, requiring less attention for preparation and handling tasks, emphasizing the importance of hand-eye coordination.

Pengaruh Citra Merek Dan Lokasi Outlet Terhadap Minat Beli Konsumen Di Warung Makan Ayam Geprek Preksu Cabang Warungboto Yogyakarta

This research aims to analyze the influence of brand image and outlet location on consumer buying interest at Warung Makan Ayam Geprek Preksu, Warungboto Branch, Yogyakarta. Brand image is an important factor that can influence consumer perceptions of the quality of the products and services offered, while outlet location plays a significant role in determining accessibility and convenience for consumers. The research method used was a survey with a questionnaire distributed to 150 respondents who were customers of the Ayam Geprek Preksu Food Stall. Data analysis was carried out using multiple linear regression to determine the relationship and influence between the variables studied. The research results show that brand image and outlet location significantly influence consumer buying interest, with brand image having a more dominant influence. These findings provide practical implications for the management of Warung Makan Ayam Geprek Preksu to continue to improve its brand image through effective marketing strategies and considering location factors in future business expansion.

Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images

BackgroundNasal polyps and inverted papillomas often look similar. Clinically, it is difficult to distinguish the masses by endoscopic examination. Therefore, in this study, we aimed to develop a deep learning algorithm for computer-aided diagnosis of nasal endoscopic images, which may provide a more accurate clinical diagnosis before pathologic confirmation of the nasal masses.MethodsBy performing deep learning of nasal endoscope images, we evaluated our computer-aided diagnosis system’s assessment ability for nasal polyps and inverted papilloma and the feasibility of their clinical application. We used curriculum learning pre-trained with patches of nasal endoscopic images and full-sized images. The proposed model’s performance for classifying nasal polyps, inverted papilloma, and normal tissue was analyzed using five-fold cross-validation.ResultsThe normal scores for our best-performing network were 0.9520 for recall, 0.7900 for precision, 0.8648 for F1-score, 0.97 for the area under the curve, and 0.8273 for accuracy. For nasal polyps, the best performance was 0.8162, 0.8496, 0.8409, 0.89, and 0.8273, respectively, for recall, precision, F1-score, area under the curve, and accuracy. Finally, for inverted papilloma, the best performance was obtained for recall, precision, F1-score, area under the curve, and accuracy values of 0.5172, 0.8125, 0.6122, 0.83, and 0.8273, respectively.ConclusionAlthough there were some misclassifications, the results of gradient-weighted class activation mapping were generally consistent with the areas under the curve determined by otolaryngologists. These results suggest that the convolutional neural network is highly reliable in resolving lesion locations in nasal endoscopic images.

Weakly Supervised Pose Estimation of Surgical Instrument from a Single Endoscopic Image.

Instrument pose estimation is a key demand in computer-aided surgery, and its main challenges lie in two aspects: Firstly, the difficulty of obtaining stable corresponding image feature points due to the instruments' high refraction and complicated background, and secondly, the lack of labeled pose data. This study aims to tackle the pose estimation problem of surgical instruments in the current endoscope system using a single endoscopic image. More specifically, a weakly supervised method based on the instrument's image segmentation contour is proposed, with the effective assistance of synthesized endoscopic images. Our method consists of the following three modules: a segmentation module to automatically detect the instrument in the input image, followed by a point inference module to predict the image locations of the implicit feature points of the instrument, and a point back-propagatable Perspective-n-Point module to estimate the pose from the tentative 2D-3D corresponding points. To alleviate the over-reliance on point correspondence accuracy, the local errors of feature point matching and the global inconsistency of the corresponding contours are simultaneously minimized. Our proposed method is validated with both real and synthetic images in comparison with the current state-of-the-art methods.

Empowering Crisis Response Efforts: A Novel Approach to Geolocating Social Media Images for Enhanced Situational Awareness

Social media and online platforms play an important role in assessing the impact of a natural disaster, especially in the immediate aftermath of the event. The information from social media demonstrated the potential to accelerate the response to a crisis. Locating social media images is critically important to help the victims immediately, mobilize community support, and provide enhanced situational awareness. However, finding the location of relevant social media images still challenges humans and computational processes. This study introduces a social media image classifier aimed at enhancing crowdsourced geolocation. The model is trained using data annotated by experts in disaster risk management. Its main goal is to reduce the difficulty of geolocating images by detecting those that are ``easy'' to geolocate. We combined the classifier with an ad-hoc crowdsourcing platform and tested it with pictures posted during a crisis. The experimental results indicate that the proposed approach speeds up the geolocation process of social media images while increasing the level of location precision

An augmented reality surgical navigation system based on co-axial projection of surgical paths for open liver tumor surgery

The traditional surgical navigation system for open liver tumor resection has some problems, such as low tumor localization accuracy, high complexity of system operation, and heavy surgical burden on surgeons. To solve these problems, this paper proposes an augmented reality surgical navigation system based on coaxial projection of surgical paths. Firstly, the intraoperative tumor localization module is used to locate the tumor. An augmented reality coaxial projection module for surgical path projection has been developed. In addition, a unified view of the camera and projector in the module is designed. Secondly, we propose an algorithm for image registration and fusion. The actual scene image of the liver captured by the augmented reality coaxial projection module is registered and fused with the model image to determine the tumor’s location in the surgical image. Then, surgical path planning is carried out for the tumors in the surgical images. An augmented reality coaxial projection module projects the surgical path onto the surface of the liver to assist surgeons in tumor resection. Finally, we validate the performance of our proposed surgical navigation system through multiple sets of experiments on tumor resection. The experimental results show that the average error and time of tumor resection based on this system are 2.95 mm and 59 s, respectively. It can be seen that the system proposed in this study achieved better results under laboratory conditions. The development of this system lays the foundation for our future research in clinical surgical navigation systems.

Increasing the Usability and Accessibility of Voyager 2 Images of Triton

Much of what we know about Neptune’s moon Triton was inferred from the analysis of images returned by the Voyager 2 mission, the only spacecraft to have visited that putative ocean world. Unfortunately, the highest-resolution images (scales < 2 km pixel−1) are difficult to use because they are only available in nonstandard formats, and the locations of the images on Triton’s surface are incorrect by up to 200 km. Although image mosaics of Triton are publicly available, these do not include the highest-resolution data. Here we describe our effort to improve the usability and accessibility of Voyager 2 images of Triton. We used the USGS’s ISIS software to process 41 Triton images, including geometric calibration, radiometric calibration, and reseau removal. We improved the image locations using a photogrammetric control network with 958 points and 3910 image measurements. Least-squares bundle adjustment of the network yielded rms uncertainty of 0.50, 0.52, and 0.51 pixels in latitude, longitude, and radius, respectively, and maximum residuals of −4.21 and +3.20 pixels, respectively. Image-to-image alignment is therefore vastly improved. We have released these processed images as cloud-optimized GeoTIFFs in orthographic projection at the original pixel scale of each image. Associated mosaics have also been created and released to provide geologic context for the individual images. These products provide the science community with analysis-ready data that enable new investigations of Triton, increase accessibility to this unique data set, and continue to enhance the scientific return from the Voyager 2 mission.

Automatic Number Plate Recognition

Automatic license plate recognition (ANPR) systems have become suitable for various applications, including traffic monitoring, law enforcement, and toll collection. This paper completes the study on automatic license plate recognition (ANPR) systems that use advanced imaging technology and machine learning algorithms to achieve accuracy in license plate verification and validation. The preparation process is adopted in various ways: image acquisition, preprocessing, location plate, character segmentation, and optical character recognition (OCR). The system, which integrates deep learning models for extraction and classification, performs better in different environments. Experimental results show that the identification accuracy of the proposed ANPR exceeds 95%, demonstrating its potential in practical applications. In addition, this paper also discusses the problems encountered in ANPR implementation, including changes in plate design, illumination, and shading, and provides suggestions for future research to improve stability and efficiency. Keywords: automatic plate recognition, image processing, machine learning, optical behavior recognition, deep learning, vehicle tracking, driving license inspection.

Remote sensing image location based on improved Yolov7 target detection

Remote sensing image location based on improved Yolov7 target detection

Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle.

In the context of construction and demolition waste exacerbating environmental pollution, the lack of recycling technology has hindered the green development of the industry. Previous studies have explored robot-based automated recycling methods, but their efficiency is limited by movement speed and detection range, so there is an urgent need to integrate drones into the recycling field to improve construction waste management efficiency. Preliminary investigations have shown that previous construction waste recognition techniques are ineffective when applied to UAVs and also lack a method to accurately convert waste locations in images to actual coordinates. Therefore, this study proposes a new method for autonomously labeling the location of construction waste using UAVs. Using images captured by UAVs, we compiled an image dataset and proposed a high-precision, long-range construction waste recognition algorithm. In addition, we proposed a method to convert the pixel positions of targets to actual positions. Finally, the study verified the effectiveness of the proposed method through experiments. Experimental results demonstrated that the approach proposed in this study enhanced the discernibility of computer vision algorithms towards small targets and high-frequency details within images. In a construction waste localization task using drones, involving high-resolution image recognition, the accuracy and recall were significantly improved by about 2% at speeds of up to 28 fps. The results of this study can guarantee the efficient application of drones to construction sites.

Automatic detection method of intracranial aneurysms on maximum intensity projection images based on SE-CaraNet

Conventional maximum intensity projection (MIP) images tend to ignore some morphological features in the detection of intracranial aneurysms, resulting in missed detection and misdetection. To solve this problem, a new method for intracranial aneurysm detection based on omni-directional MIP image is proposed in this paper. Firstly, the three-dimensional magnetic resonance angiography (MRA) images were projected with the maximum density in all directions to obtain the MIP images. Then, the region of intracranial aneurysm was prepositioned by matching filter. Finally, the Squeeze and Excitation (SE) module was used to improve the CaraNet model. Excitation and the improved model were used to detect the predetermined location in the omni-directional MIP image to determine whether there was intracranial aneurysm. In this paper, 245 cases of images were collected to test the proposed method. The results showed that the accuracy and specificity of the proposed method could reach 93.75% and 93.86%, respectively, significantly improved the detection performance of intracranial aneurysms in MIP images.

Novel utilization and quantification of Xsight diaphragm tracking for respiratory motion compensation in Cyberknife Synchrony treatment of liver tumors.

The Xsight lung tracking system (XLTS) utilizes an advanced image processing algorithm to precisely identify the position of a tumor and determine its location in orthogonal x-ray images, instead of finding fiducials, thereby minimizing the risk of fiducial insertion-related side effects. To assess and gauge the effectiveness of CyberKnife Synchrony in treating liver tumors located in close proximity to or within the diaphragm, we employed the Xsight diaphragm tracking system (XDTS), which was based on the XLTS. We looked back at the treatment logs of 11 patients (8/11 [XDTS], 3/11 [Fiducial-based Target Tracking System-FTTS]) who had liver tumors in close proximity to or within the diaphragm. And the results are compared with the patients who undergo the treatment of FTTS. The breathing data information was calculated as a rolling average to reduce the effect of irregular breathing. We tested the tracking accuracy with a dynamic phantom (18023-A) on the basis of patient-specific respiratory curve. The average values for the XDTS and FTTS correlation errors were 1.38±0.65 versus 1.50±0.26mm (superior-inferior), 1.28±0.48 versus 0.40±0.09mm (left-right), and 0.96±0.32 versus 0.47±0.10mm(anterior-posterior), respectively. The prediction errors for two methods of 0.65±0.16 versus 5.48±3.33mm in the S-I direction, 0.34±0.10 versus 1.41±0.76mm in the A-P direction, and 0.22±0.072 versus 1.22±0.48mm in the L-R direction. The coverage rate of FTTS slightly less than that of XDTS, such as 96.53±8.19% (FTTS) versus 98.03±1.54 (XDTS). The prediction error, the motion amplitude, and the variation of the respiratory center phase were strongly related to each other. Especially, the higher the amplitude and the variation, the higher the prediction error. The diaphragm has the potential to serve as an alternative to gold fiducial markers for detecting liver tumors in close proximity or within it. We also found that we needed to reduce the motion amplitude and train the respiration of the patients during liver radiotherapy, as well as control and evaluate their breathing.