Standard Image Processing Research Articles

Deep Learning-Driven Thermal Imaging Hotspot Detection in Solar Photovoltaic Arrays Using YOLOv10

The effective management of solar photovoltaic (PV) arrays is vital to maximising energy generation and ensuring long-term performance reliability. The presence of faults, or partial shading, can give rise to hotspots in PV arrays, making their detection critical for timely maintenance and avoiding further impacts on their performance. This paper describes a new approach to the hotspot detection issue in thermal images of solar PV arrays by leveraging deep learning. This study employs the YOLOv10 (You Only Look Once, version 10) model to deliver very high accuracy and speed in identifying and localising hotspots under defined regions of interest (ROIs). The proposed method begins with taking thermal images from multiple solar PV installations while capturing a range of operational conditions and fault types. These images are annotated with hotspot regions to create a robust training dataset. Given YOLO's capability for real-time object detection with high precision and mean average precision (mAP), the YOLOv10 model is then implemented to train on this dataset. Multiple changes were made to the YOLOv10 hyperparameters to optimise them for detecting hotspots in thermal images. The experimental scenario of this paper demonstrates that this approach indeed performs significantly better than standard image processing methods as well as prior deep learning models for detecting both hotspot accuracy as well as speed of processing the images. The YOLOv10 method demonstrated the highest available classification performance with a mAP at intersection over union (IoU) of 0.5 accuracy of 0.91 with inference time suitable for real-time applications. The sample results demonstrated the model's continuous ability to detect hotspots and provide location data under various conditions, such as crossing through different times of day and weather. The results of this study demonstrate that YOLOv10 can be used for improved detection of hotspots in the explicably thermal imagery of solar PV arrays.

A Novel Fractional-Order Non-Convex TVα,p Model in Image Deblurring

In this paper, we propose a non-convex model with fractional-order applied to image deblurring problems. In the new model, fractional-order gradients have been introduced to preserve detailed features, and a source term with a blurry kernel is used for deblurring. This aspect of the model ensures that it can handle various blurring scenarios. Additionally, we devise an algorithm that maintains the non-expansiveness of the support set for image gradients, serving as a critical component in our approach to address image deblurring issues. After approximate linearization, the algorithm can be easily implemented. Some standard image processing techniques similar to fast Fourier transform can be utilized. Global convergence has likewise been confirmed and established. Moreover, we have also demonstrated that the proposed deblurring algorithm exhibits edge preservation properties. Compared with several existing classic models, the proposed method maintains a good balance between detail preservation, edge preservation, and deblurring. In addition, compared with several classic methods, the proposed method improved PSNR and SSIM by 0.9733 and 0.0111, respectively.

Description of a new method to calculate the equator of the crystalline lens using AS-OCT images: Accuracy in non-dilated measurements.

To establish a methodology for objectively estimating the Lens Equatorial Plane (LEP) from clinical images, comparing LEP with dilated versus non-dilated pupils. A cohort of 91 eyes from 60 patients undergoing preoperative assessments for cataract surgery was evaluated. Anterior Segment Optical Coherence Tomography (AS-OCT) images were analysed under conditions of pharmacologically induced pupil dilation versus a non-dilated pupil. Geometrical parameters, including LEP, intersection diameter (ID), lens thickness (LT), anterior and posterior lens thickness were automatically calculated by applying standard image processing techniques to clinical AS-OCT images. Significant differences in lens parameters, including LEP, were observed between dilated and non-dilated conditions (all p < 0.001). A strong linear correlation was found across all geometrical variables under both conditions (r[LEP] = 0.64, r[ID] = 0.78, r[LT] = 0.99, all p < 0.001); enabling reliable correction of these differences. The study introduces an objective methodology for LEP calculation, emphasising the need to consider the eye's physiological state during preoperative measurements. Incorporating LEP into future intraocular lens (IOL) power calculation formulas and replacing the habitual effective lens position may potentially improve the accuracy of IOL power estimation and thus postoperative visual outcomes.

Small animal brain surgery with neither a brain atlas nor a stereotaxic frame

BackgroundStereotaxic surgery is a cornerstone in brain research for the precise positioning of electrodes and probes, but its application is limited to species with available brain atlases and tailored stereotaxic frames. Addressing this limitation, we introduce an alternative technique for small animal brain surgery that requires neither an aligned brain atlas nor a stereotaxic frame. New methodThe new method requires an ex-vivo high-contrast MRI brain scan of one specimen and access to a micro-CT scanner. The process involves attaching miniature markers to the skull, followed by CT scanning of the head. Subsequently, MRI and CT images are co-registered using standard image processing software and the targets for brain recordings are marked in the MRI image. During surgery, the animal's head is stabilized in any convenient orientation, and the probe’s 3D position and angle are tracked using a multi-camera system. We have developed a software that utilizes the on-skull markers as fiducial points to align the CT/MRI 3D model with the surgical positioning system, and in turn instructs the surgeon how to move the probe to reach the targets within the brain. ResultsOur technique allows the execution of insertion tracks connecting two points in the brain. We successfully applied this method for neuropixels probe positioning in owls, quails, and mice, demonstrating its versatility. Comparison with existing methodsWe present an alternative to traditional stereotaxic brain surgeries that does not require established stereotaxic tools. Thus, this method is especially of advantage for research in non-standard and novel animal models.

An improved performance of reversible data hiding in encrypted images using decision tree algorithm

The advancements in increase of internet technology, transfer of data through insecure channel has become more popular. Now a day's most of the customers were turned to digital world and the channel is the main resource of communication. There is no guarantee that stored data will not be accessed by any hacker or cloud service provider. Privacy and Security is the main aspect of the rapid development in the digital storage. These conditions demand to make sure that the data is encrypted, stored and transmitted. Reversible Data Hiding in Encrypted images using machine learning is an emerging methodology used in the domain to secure the information for end-to-end secure data transmission. In this proposed method, all the data's can be Encrypted using Digital Keys, and these data can be embedded in cover images using decision tree algorithm. In the receiver side, the data can be retrieved back if receiver have right keys which is used while encryption. This system uses pseudo random number generation for generating multiple keys to encrypt and decrypt the secret data. This method is designed for encryption the Images and Text data. To increase further security and embedding capacity a machine learning algorithm is also implemented. The proposed Decision Tree method is tested on the standard image processing images and certain parameters are calculated for performance metrics and compare the results with existing schemes. The Algorithm is implemented in MATLAB and used the Statistics and Machine Learning library in MATLAB.

A standardized image processing and data quality platform for rodent fMRI

Functional magnetic resonance imaging in rodents holds great potential for advancing our understanding of brain networks. Unlike the human community, there remains no standardized resource in rodents for image processing, analysis and quality control, posing significant reproducibility limitations. Our software platform, Rodent Automated Bold Improvement of EPI Sequences, is a pipeline designed to address these limitations for preprocessing, quality control, and confound correction, along with best practices for reproducibility and transparency. We demonstrate the robustness of the preprocessing workflow by validating performance across multiple acquisition sites and both mouse and rat data. Building upon a thorough investigation into data quality metrics across acquisition sites, we introduce guidelines for the quality control of network analysis and offer recommendations for addressing issues. Taken together, this software platform will allow the emerging community to adopt reproducible practices and foster progress in translational neuroscience.

Monitoring deforestation, forest health, and environmental criticality in a protected area periphery using Geospatial Techniques.

Protected areas in South Asia face significant challenges due to human disturbance and deforestation. The ongoing debate surrounds the recent surge in illegal encroachment of forest buffer zones in the Musali divisional secretariat division (DSD), which has led to a significant loss of forest cover over the past three decades. In this context, detecting changes in forest cover, assessing forest health, and evaluating environmental quality are crucial for sustainable forest management. As such, our efforts focused on assessing forest cover dynamics, forest health, and environmental conditions in the DSD from 1988 to 2022. We employed standardized image processing techniques, utilizing Landsat-5 (TM) and Landsat-8 (OLI) images. However, the forest area in the DSD has shown minimal changes, and environmental conditions and forest health have illustrated considerable spatial-temporal variations over the 34 years. The results indicated that 8.5 km2 (1.9%) of forest cover in the DSD has been converted to other land use classes. Overall, the Normalized Difference Vegetation Index (NDVI) has declined over time, while Land Surface Temperature (LST) exhibits an increasing trend. The regression results demonstrated a robust inverse relationship between LST and NDVI. The declining vegetation conditions and the increasing LST contribute to an increase in environmental criticality. The derived maps and indices will be beneficial for forest authorities in identifying highly sensitive locations. Additionally, they could enable land use planners to develop sustainable land management strategies.

Robust zero-watermarking algorithm for multi-medical images based on FFST-Schur and Tent mapping

Aiming at issues such as the difficulty of centralized protection of multiple medical images and the high storage cost caused by repetitive operations, we proposed a robust zero-watermarking algorithm for multiple medical images based on fast finite Shearlet transform (FFST)-Schur and Tent mapping. First, multiple medical images were normalized, and the processed images were fused using a gray-weighted average fusion method for image fusion. Second, an FFST is applied to the fused image to divide the low-frequency sub-bands into blocks of the same size and perform Schur decomposition, which generates a feature image using the magnitude between the largest eigenvalue of each block and the overall average value. Finally, an image encryption algorithm based on Tent mapping is proposed to encrypt the logo image, and the encrypted image and feature image are then subjected to an exclusive OR operation to produce a zero-watermarking image. Experimental results demonstrate that the proposed approach effectively safeguards against standard image processing and geometric and combinatorial attacks. The average performance of the anti-attack algorithm was improved by approximately 3.2 % compared to similar algorithms, indicating the superior robustness of the proposed algorithm.

Stress Detection in It Professionals by Image Processing and Machine Learning

Abstract: The project aims to leverage image processing and machine learning techniques for the detection of stress in IT professionals. The primary focus is on monitoring the emotional well-being of individuals who spend extended periods working in front of a computer, with the goal of identifying and alleviating stress to create a more conducive work environment. Key objectives include predicting stress based on observed symptoms, assessing stress levels in employees, and delivering pertinent information. Previous research in stress detection for IT professionals has employed machine learning and image processing, prioritizing secure monitoring through captured images of authenticated users. These systems analyze stress levels using standard conversion and image processing methods, incorporating live detection and periodic analysis. In contrast to earlier systems, they provide personalized counseling and solutions for managing both physical and mental stress levels, integrating surveys for regular assessments. Furthermore, existing systems may incorporate digital signal processing, taking into account factors such as Galvanic skin response, blood volume, pupil dilation, and skin temperature. Alternatively, they may rely on a variety of physiological signals and visual features to monitor stress levels during work.

Performance Evaluation of Computer Vision Algorithms in a Programmable Logic Controller: An Industrial Case Study

This work evaluates the use of a programmable logic controller (PLC) from Phoenix Contact’s PLCnext ecosystem as an image processing platform. PLCnext controllers provide the functions of “classical” industrial controllers, but they are based on the Linux operating system, also allowing for the use of software tools usually associated with computers. Visual processing applications in the Python programming language using the OpenCV library are implemented in the PLC using this feature. This research is focused on evaluating the use of this PLC as an image processing platform, particularly for industrial machine vision applications. The methodology is based on comparing the PLC’s performance against a computer using standard image processing algorithms. In addition, a demonstration application based on a real-world scenario for quality control by visual inspection is presented. It is concluded that despite significant limitations in processing power, the simultaneous use of the PLC as an industrial controller and image processing platform is feasible for applications of low complexity and undemanding cycle times, providing valuable insights and benchmarks for the scientific community interested in the convergence of industrial automation and computer vision technologies.

Indicators for Hospitalization in Acute Pulmonary Embolism: Uncover the Association Between D-dimer Levels, Thrombus Volume and Radiomics

The advent of advanced computed tomography (CT) technology and the field of radiomics has opened up new avenues in diagnostic assessments. Increasingly, there is substantial evidence advocating for the incorporation of quantitative imaging biomarkers in the clinical decision-making process.This study aimed to examine the correlation between D-dimer levels and thrombus size in acute pulmonary embolism (PE) combining dual-energy CT (DECT) and radiomics and to investigate the diagnostic utility of a machine learning classifier based on dual-energy computed tomography (DECT) radiomics for identifying patients with a complicated course, defined as at least hospitalization at IMC. The study was conducted including 136 participants who underwent pulmonary artery CT angiography from January 2015 to March 2022. Based on DECT imaging, 107 radiomic features were extracted for each patient using standardized image processing. After dividing the dataset into training and test sets, stepwise feature reduction based on reproducibility, variable importance and correlation analyses were performed to select the most relevant features; these were used to train and validate the gradient-boosted tree models.Receiver operating characteristics (ROC) analysis was utilized to evaluate the association between volumetric, laboratory data and adverse outcomes. In the central PE group, we observed a significant correlation between thrombus volumetrics and D-dimer levels (p=0.0037), as well as between thrombus volumetrics and hospitalization at the Intermediate Care Unit (IMC) (p=0.0001). In contrast, no statistically significant differences were identified in thrombus sizes between patients who experienced complications and those who had a favorable course (p=0.3162). The trained machine learning classifier achieved an accuracy of 61% and 55% in identifying patients with a complicated course, as indicated by an area under the ROC curve of 0.63 and 0.58. In conclusion, our findings indicate a positive correlation between D-dimer levels and central PE's pulmonary embolic burden. Thrombus volumetrics may serve as an indicator for complications and outcomes in acute PE patients. Thus, thrombus volumetrics, as opposed to D-dimers, could be an additional marker for evaluating embolic disease severity. Moreover, DECT-derived radiomic feature models show promise in identifying patients with a complicated course, such as hospitalization at IMC.

Imaging-based risk stratification of patients with pulmonary embolism based on dual-energy CT-derived radiomics.

Technological progress in the acquisition of medical images and the extraction of underlying quantitative imaging data has introduced exciting prospects for the diagnostic assessment of a wide range of conditions. This study aims to investigate the diagnostic utility of a machine learning classifier based on dual-energy computed tomography (DECT) radiomics for classifying pulmonary embolism (PE) severity and assessing the risk for early death. Patients who underwent CT pulmonary angiogram (CTPA) between January 2015 and March 2022 were considered for inclusion in this study. Based on DECT imaging, 107 radiomic features were extracted for each patient using standardized image processing. After dividing the dataset into training and test sets, stepwise feature reduction based on reproducibility, variable importance and correlation analyses were performed to select the most relevant features; these were used to train and validate the gradient-boosted tree models. The trained machine learning classifier achieved a classification accuracy of .90 for identifying high-risk PE patients with an area under the receiver operating characteristic curve of .59. This CT-based radiomics signature showed good diagnostic accuracy for risk stratification in individuals presenting with central PE, particularly within higher risk groups. Models utilizing DECT-derived radiomics features can accurately stratify patients with pulmonary embolism into established clinical risk scores. This approach holds the potential to enhance patient management and optimize patient flow by assisting in the clinical decision-making process. It also offers the advantage of saving time and resources by leveraging existing imaging to eliminate the necessity for manual clinical scoring.

A Novel Standardized Collaborative Online Model for Processing and Analyzing Remotely Sensed Images in Geographic Problems

In recent years, remote sensing image processing technology has developed rapidly, and the variety of remote sensing images has increased. Solving a geographic problem often requires multiple remote sensing images to be used together. For an image processing analyst, it is difficult to become proficient in the image processing of multiple types of remote sensing images. Therefore, it is necessary to have multiple image processing analysts collaborate to solve geographic problems. However, as a result of the naturally large volumes of data and the computer resources they consume for analysis, remote sensing images present a barrier in the collaboration of multidisciplinary remote sensing undertakings and analysts. As a result, during the development of the collaborative analysis process, it is necessary to achieve the online processing and analysis of remote sensing images, as well as to standardize the online remote sensing image collaborative analysis process. To address the above issues, a hierarchical collaborative online processing and analysis framework was developed in this paper. This framework defined a clear collaborative analysis structure, and it identifies what kinds of online image processing and analysis activities participants can engage in to successfully conduct collaborative processes. In addition, a collaborative process construction model and an online remote sensing image processing analysis model were developed to assist participants in creating a standard collaborative online image processing and analysis process. In order to demonstrate the feasibility and effectiveness of the framework and model, this paper developed a collaborative online post-disaster assessment process that utilizes radar images and optical remote sensing images for a real forest fire event. This process was based on the BPMN2.0 and OGC dual standards. Based on the results, the proposed framework provides a hierarchical collaborative remote sensing image processing and analysis process with well-defined stages and activities to guide the participants’ mutual collaboration. Additionally, the proposed model can help participants to develop a standardized collaborative online image processing process in terms of process structure and information interactions.

SimpliPyTEM: An open-source Python library and app to simplify transmission electron microscopy and in situ-TEM image analysis.

Introducing SimpliPyTEM, a Python library and accompanying GUI that simplifies the post-acquisition evaluation of transmission electron microscopy (TEM) images, helping streamline the workflow. After an imaging session, a folder of image and/or video files, typically containing low contrast and large file size 32-bit images, can be quickly processed via SimpliPyTEM into high-quality, high-contrast.jpg images with suitably sized scale bars. The app can also generate HTML or PDF files containing the processed images for easy viewing and sharing. Additionally, SimpliPyTEM specifically focuses on in situ TEM videos, an emerging field of EM involving the study of dynamic processes whilst imaging. The package allows fast data processing into preview movies, averages, image series, or motion-corrected averages. The accompanying Python library offers many standard image processing methods, all simplified to a single command, plus a module to analyse particle morphology and population. This latter application is particularly useful for life sciences investigations. User-friendly tutorials and clear documentation are included to help guide users through the processing and analysis. We invite the EM community to contribute to and further develop this open-source package.

A generalizable deep learning regression model for automated glaucoma screening from fundus images

A plethora of classification models for the detection of glaucoma from fundus images have been proposed in recent years. Often trained with data from a single glaucoma clinic, they report impressive performance on internal test sets, but tend to struggle in generalizing to external sets. This performance drop can be attributed to data shifts in glaucoma prevalence, fundus camera, and the definition of glaucoma ground truth. In this study, we confirm that a previously described regression network for glaucoma referral (G-RISK) obtains excellent results in a variety of challenging settings. Thirteen different data sources of labeled fundus images were utilized. The data sources include two large population cohorts (Australian Blue Mountains Eye Study, BMES and German Gutenberg Health Study, GHS) and 11 publicly available datasets (AIROGS, ORIGA, REFUGE1, LAG, ODIR, REFUGE2, GAMMA, RIM-ONEr3, RIM-ONE DL, ACRIMA, PAPILA). To minimize data shifts in input data, a standardized image processing strategy was developed to obtain 30° disc-centered images from the original data. A total of 149,455 images were included for model testing. Area under the receiver operating characteristic curve (AUC) for BMES and GHS population cohorts were at 0.976 [95% CI: 0.967–0.986] and 0.984 [95% CI: 0.980–0.991] on participant level, respectively. At a fixed specificity of 95%, sensitivities were at 87.3% and 90.3%, respectively, surpassing the minimum criteria of 85% sensitivity recommended by Prevent Blindness America. AUC values on the eleven publicly available data sets ranged from 0.854 to 0.988. These results confirm the excellent generalizability of a glaucoma risk regression model trained with homogeneous data from a single tertiary referral center. Further validation using prospective cohort studies is warranted.

Implementation of a steganography system based on hybrid square quaternion moment compression in IoMT

Internet of Medical Things (IoMT) systems generate medical data transmissions between patients, medical experts, and medical centers over public networks, which require high levels of security to protect the content of medical images and the personal information they contain. In this paper, we propose a new stego image encryption scheme based on a new secret image compression method, wavelet transformation, QR decomposition of the cover image, and a new chaotic map. The secret image is compressed by the Hahn-Krawtchouk hybrid quaternion square moments (HK-HQSM), which are optimized by a new hybrid metaheuristic algorithm based on the Salp Swarm Algorithm (SSA) and the Arithmetic Optimization Algorithm (AOA). To increase the security level when transmitting the proposed stego images over public networks, we introduce a new chaotic map based on the 2D fractional Henon map to encrypt the stego image. To demonstrate the effectiveness of the proposed steganography scheme for IoMT, we implemented this scheme on a low-cost Raspberry Pi 4 hardware board. The results of the performed numerical experiments show that our method is secure and provides exceptional robustness against common standard image processing attacks (steganalysis attacks). The results also demonstrate that our strategy is able to work efficiently and quickly when implemented on a Raspberry Pi board.

CNN-based Image Processing algorithm for autonomous optical navigation of Hera mission to the binary asteroid Didymos

Hera mission is the European Space Agency’s contribution to the international collaboration with NASA for the planetary defence, i.e. Asteroid Impact Deflection Assessment aiming to deflect the trajectory of its target binary asteroid system (65803) Didymos. The Early Characterization Phase and the Detailed Characterization Phase of Hera mission are two phases of the proximity operations with the objective to physically and dynamically characterize Didymos. During these phases, an Image Processing algorithm is required to estimate the position of the centroid of the primary to enable Line of Sight navigation. However, the performance of standard Image Processing algorithms is affected by the disturbances of the image, such as poor illumination conditions, the presence of external bodies and the irregular shape of the target. This research addresses this challenge by developing a robust Convolutional Neural Networks-based Image Processing algorithm to estimate the position of the centroids of Didymos and its moon Dimorphos, the pseudorange from the primary and the Sun phase angle. The training, validation and testing datasets are generated with the software Planet and Asteroid Natural scene Generation Utility using the Early Characterization Phase and the Detailed Characterization Phase trajectories as case scenario. The position in the image of the centroids of Didymos and Dimorphos is estimated using their respective position vectors. To estimate the pseudorange, the developed algorithm regresses a set of keypoints on the visible border of Didymos and evaluates its apparent radius. For the Sun phase angle, the pixel position of the subsolar point of the primary is leveraged. The High-Resolution Network is the Convolutional Neural Network architecture applied to detect keypoints with superior spatial precision. Even with the considered disturbances, the analysis shows that the proposed algorithm is able to provide an accurate estimation of the mentioned outputs for all the Early Characterization Phase trajectory and for 77.33% of the Detailed Characterization Phase trajectory, improving the robustness and autonomy of the mission navigation.

A Laplace-Hamming Binarization Approach for Second-Generation HR-pQCT Rescues Fine Feature Segmentation.

Although second-generation high-resolution peripheral quantitative computed tomography (XCTII) provides the highest-resolution in vivo bone microstructure assessment, the manufacturer's standard image processing protocol omits fine features in both trabecular and cortical compartments. To optimize fine structure segmentation, we implemented a binarization approach based on a Laplace-Hamming (LH) segmentation and documented the reproducibility and accuracy of XCTII structure segmentation using both the standard Gaussian-based binarization and the proposed LH segmentation approach. To evaluate reproducibility, 20 volunteers (9 women, 11 men; aged 23-75 years) were recruited, and three repeat scans of the radii and tibias were acquired using the manufacturer's standard in vivo protocol. To evaluate accuracy, cadaveric structure phantoms (14 radii, 6 tibias) were scanned on XCTII using the same standard in vivo protocol and on μCT at 24.5 μm resolution. XCTII images were analyzed twice-first, with the manufacturer's standard patient evaluation protocol and, second, with the proposed LH segmentation approach. The LH approach rescued fine features evident in the grayscale images but omitted or overrepresented (thickened) by the standard approach. The LH approach significantly reduced error in trabecular volume fraction (BV/TV) and thickness (Tb.Th) compared with the standard approach; however, higher error was introduced for trabecular separation (Tb.Sp). The LH approach improved the correlation between XCTII and μCT for cortical porosity (Ct.Po) and significantly reduced error in cortical pore diameter (Ct.Po.Dm) compared with the standard approach. The LH approach resulted in improved precision compared with the standard approach for BV/TV, Tb.Th, Ct.Po, and Ct.Po.Dm at the radius and for Ct.Po at the tibia. Our results suggest that the proposed LH approach produces substantially improved binary masks, reduces proportional bias, and provides greater accuracy and reproducibility in important outcome metrics, all due to more accurate segmentation of the fine features in both trabecular and cortical compartments. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Three-dimensional modeling and defect quantification of existing concrete bridges based on photogrammetry and computer aided design

Bridge infrastructure is aging and deteriorating and requires innovative condition monitoring and assessment to ensure safety and maintain serviceability. Current practices for bridge condition assessment depend mainly on visual inspection. Innovative data collection technologies can assist in bridge inspection and defect identification and circumvent the limitations of traditional visual inspection. This research aims to apply photogrammetry principles and image processing techniques to develop a full-scale three-dimensional (3D) bridge structure model for defect identification and quantification. The data source to establish the point cloud models is a set of digital images taken at the bridge site. The point cloud models are developed by analyzing high-resolution images captured using a Nikon D4S camera. The images are captured from different positions/locations to provide extensive stereo coverage and maximum overlapping. Two hundred images of the bridge are processed with PhotoModeler software along with the calibration information, and ground control points (GCPs) coordinates to create the models. A standard digital close-range image processing is implemented to generate the 3D points cloud model. The calibration and orientation processes are made over a deformed situation. However, the developed CAD bridge drawings depict the original undeformed geometry. The extracted bridge inventory data from the 3D model, including bridge geometry information and bridge damage quantification, can provide a baseline for the bridge monitoring system to detect bridge deterioration over the years. The developed photogrammetric 3D model has an RMSE of 0.0081 m and uncertainty of ± 0.0041 m. Future research can aim at developing reusable parametric objects for bridges in Revit and coding scripts using programming languages such as Dynamo or Python to perform further analysis of the captured information.

Fully adaptive Bayesian algorithm for data analysis: FABADA

Abstract The discovery potential from astronomical and other data is limited by their noise. We introduce a novel non-parametric noise reduction technique based on Bayesian inference techniques, fully adaptive Bayesian algorithm for data analysis (FABADA) that automatically improves the signal-to-noise ratio of one- and two-dimensional data, such as astronomical images and spectra. The algorithm iteratively evaluates possible smoothed versions of the data, the smooth models, estimating the underlying signal that is statistically compatible with the noisy measurements. Iterations stop based on the evidence and the χ2 statistic of the last smooth model. We then compute the expected value of the signal as a weighted average of the whole set of smooth models. We explain the mathematical formalism and numerical implementation of the algorithm, and evaluate its performance in terms of the peak signal-to-noise ratio, the structural similarity index, and the time payload, using a battery of real astronomical observations. Our FABADA yields results that, without any parameter tuning, are comparable with standard image processing algorithms whose parameters have been optimized based on the true signal to be recovered, something that is impossible in a real application. On the other hand, state-of-the-art non-parametric methods, such as block-matching and three-dimensional filtering, offer slightly better performance at high signal-to-noise ratio, while our algorithm is significantly more accurate for extremely noisy data, a situation usually encountered in astronomy.