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PurposeFisheye camera is a wide-angle camera with a large field of view, which can obtain more information than conventional cameras, but its own disadvantage of large distortion can lead to shape distortion of objects in the captured image. To correct lens distortion, most scholars use manual calibration methods. The traditional manual calibration method relies on complex data computation and specialized mathematical knowledge, but the method is complex and not universal. Considering the nonlinear features of neural networks can be used to fit the nonlinear distortion of distorted images, this paper aims to correct the distortion of a fisheye image based on convolutional neural networks.Design/methodology/approachBy generating fisheye distortion images using readily available cityscape road data sets, this paper designs an effective correction model by taking advantage of the fact that the relationship between the pixel coordinates is basically stable after uniform distortion. In our training process, the corresponding distortion is firstly synthesized using the original image, the loss function is constructed using the geometric means of the Hough transform, the whole model is then trained with the help of the loss of the linear slope, and finally the predicted parameters are used to correct the fisheye image, which realizes the overall end-to-end framework.FindingsExperimental results show that the method proposed in this paper outperforms similar methods in terms of correction performance.Originality/valueThis paper proposed a fisheye distortion correction algorithm based on convolutional neural network, which uses distortion-free images based on a generalized fisheye imaging model and maps to generate a fisheye distortion picture data set. It also builds a loss function training model based on line reconstruction error loss of Hough transforms.

Forest structure plays a crucial role in maintaining the ecosystem balance. All the biogeochemical cycles need trees for the successful execution of the processes. Nowadays, one of the most critical concerns is the accurate and precise assessment of forest biomass. The biomass assessment can be done by knowing the canopy metrics, stem volume, and specific wood gravity. This research used a terrestrial laser scanner (TLS) to retrieve tree parameters, providing point cloud data (PCD). The parameters derived from PCD are diameter at breast height (DBH) and tree height using randomized Hough transformation (RHT). With these tree parameters, the stem volume of the tree was calculated and correlated with the Forest Survey of India (FSI) equation. The radius, DBH, tree height, and stem volume were also obtained using the Random Sample Consensus (RANSAC) algorithm. The volume calculated using the RANSAC algorithm is statistically analyzed with the volume calculated with the FSI equations available for specific tree species. The R2 value obtained for the volume calculated by the RANSAC and FSI equations is 0.95. In contrast, the correlation value obtained for the volume calculated by RHT and FSI equations is 0.99. Therefore, it shows that both algorithms are highly correlated and can be used as an alternative method for stem volume calculation, which will be less time-consuming and more accurate as well as precise. This method tries to explain the alternative method to calculate tree stem volume without using the species-specific FSI equations, which may sometimes produce biases and uncertainty in calculating stem volume and biomass.

Due to the complexity of underwater conditions, achieving stable long-endurance autonomous underwater navigation has always been a challenging issue. Polarized light navigation, which utilizes the polarization field in the underwater downward radiation field to determine the heading angle, requires a known horizontal attitude beforehand. In response to the significant deviations caused by interference in the existing underwater polarization attitude determination algorithms, this paper proposes an edge recognition method that integrates the Power theorem of circles and Improved 3D Conical Hough Transformation (PTC–3D-CoHT). This method has the advantages of pre-screening effective pixel points, better handling of distorted circles, and improving the deviation in extracting Snell’s window. The theoretical basis, model, and detailed calculation process of this method are provided in this paper. Underwater experiments show that, compared to the Circular Hough Transformation (CiHT) and 3D Conical Hough Transformation (3D-CoHT) algorithms, PTC–3D-CoHT enhances the robustness of Snell’s window extraction, verifying the effectiveness of the proposed method.

Background: High-resolution images from unmanned aerial vehicles (UAVs) can be used to describe the state of forests at regular time periods in a cost-effective manner. The purpose of this study was to assess the performance of a line template matching algorithm, the Hough transformation, for detecting fallen logs from UAV-based high-resolution RGB images. The suggested methodology does not aim to replace any known aerial method for log detection, rather it is more oriented to the detection of fallen logs in open forest stands with a high percentage of log visibility and straightness. Methods: This study describes a line template matching algorithm that can be used for the detection of fallen logs in an automated process. The detection technique was based on object-based image analysis, using both pixel-based and shape descriptors. To determine the actual number of fallen logs, and to compare with the ones predicted by the algorithm, manual visual assessment was used based on six high-resolution orthorectified images. To evaluate if a line matched, we used a voting scheme. The total number of detected fallen logs compared with the actual number of fallen logs based on several accuracy metrics. To evaluate predictive models we tested the cross-validation mean error. Finally, to test how close our results were to chance, we used the Cohen`s Kappa coefficient. Results: The detection algorithm found 136 linear objects, of which 92 of them were detected as fallen logs. From the 92 detected fallen logs, 86 were correctly predicted by the algorithm and 24 were falsely detected as fallen logs. The calculated amount of observed agreement was equal to 0.78, whereas the expected agreement by chance was 0.61. Finally, the kappa statistic was 0.44. Conclusions: Our methodology had high reliability for detecting fallen logs based on total user‘s accuracy (94.9%), whereas a Kappa of 0.44 indicated there was good agreement between the observed and predicted values. Also, the cross-validation analysis denoted the efficiency of the proposed method with an average error of 16%.

The LHC High Energy Physics experiments will face challenging trigger requirements in the next decade. The increase of the peak of luminosity to 5-7.5 × 10 –34 cm –2 s –1 will push the major experiments such as ATLAS to exploit the online tracking for their inner detector to achieve 10 kHz of events from 1 MHz. In this paper is presented the proposal for a tuned Hough Transform algorithm implemented on high-end FPGA, versatile and adaptable to different tracking situations. The platform developed allows to study different dataset, including ATLAS simulations, using a software simulating the firmware. Xilinx FPGA have been selected for this implementation, exploiting currently the VC709 commercial board and its PCI Express Generation 3 technology. The system provides the features to possibly process a 200 pile up event of ATLAS Run4 in the order of 10 μs averagely, with the possibility to run two events at a time. Preliminary tests showed a tracking efficiency > 95 % for single muon. The project plans to be proposed for the Event Filter TDAQ ATLAS Upgrade of Phase-II.

The level-1 trigger system of the Belle II experiment is designed to select physics events of interest with almost 100% efficiency for hadronic events. In terms of event timing decision, the level-1 trigger is required to have an accuracy of less than 10 ns. The Central Drift Chamber (CDC) level-1 trigger provides the event timing information as one of the level-1 timing sources. We developed the new algorithm to measure the event timing with an accuracy of about 10 ns based on the CDC hit timing. Two-dimensional charged track reconstruction by Hough transformation was utilized to reduce high background hits. We used a new-developed general-purpose FPGA board (Universal Trigger board 4) for this module for the first time. We report the performance of the new algorithm using e + e − collision data collected in 2020.

Shape recognition is considered as one of the challenges in automated digital image analysis and computer vision. One of the most commonly used shapes is the ellipse which is of great importance for many industrial and biomedical applications. In this study, a novel technique is proposed for segmenting and separating of overlapped elliptical shape objects using concavity analysis and several morphological image processing techniques. A comparative study of the detection speed and accuracy of elliptical objects between Iterative Random Hough Transformation (IRHT) algorithm approach and Direct Least Squares Fitting (DLSF) of Ellipses method has shown the great superiority of DLSF in both the speed and accuracy of recognition. The validation of the proposed techniques for segmentation and detection along with calculation of the efficiency of the system has shown those techniques to be robust and effective for automation of synthetic and real elliptical shapes. The red blood cells (RBCs) microscopic images of the blood smear in Hereditary Elliptocytosis disorder is studied as real elliptical shapes and a quantitative analysis was implemented on the detected RBCs, where the distribution parameters of the ellipse size (area), Roundness, Eccentricity, and Ellipticity are estimated in addition to RBCs counting. The proposed detection approach is successful in building a fully autonomous and accurate system with ellipse analysis capabilities.

We study the Hii regions associated with the NGC 6334 molecular cloud observed in the submillimeter and taken as part of the B-fields In STar-forming Region Observations Survey. In particular, we investigate the polarization patterns and magnetic field morphologies associated with these Hii regions. Through polarization pattern and pressure calculation analyses, several of these bubbles indicate that the gas and magnetic field lines have been pushed away from the bubble, toward an almost tangential (to the bubble) magnetic field morphology. In the densest part of NGC 6334, where the magnetic field morphology is similar to an hourglass, the polarization observations do not exhibit observable impact from Hii regions. We detect two nested radial polarization patterns in a bubble to the south of NGC 6334 that correspond to the previously observed bipolar structure in this bubble. Finally, using the results of this study, we present steps (incorporating computer vision; circular Hough transform) that can be used in future studies to identify bubbles that have physically impacted magnetic field lines.

Nowadays, video cameras are increasingly used for surveillance, monitoring, and activity recording. These cameras generate high resolution image and video data at large scale. Processing such large scale video streams to extract useful information with time constraints is challenging. Traditional methods do not offer scalability to process large scale data. In this paper, we propose and evaluate cloud services for high resolution video streams in order to perform line detection using Canny edge detection followed by Hough transform. These algorithms are often used as preprocessing steps for various high level tasks including object, anomaly, and activity recognition. We implement and evaluate both Canny edge detector and Hough transform algorithms in Hadoop and Spark. Our experimental evaluation using Spark shows an excellent scalability and performance compared to Hadoop and standalone implementations for both Canny edge detection and Hough transform. We obtained a speedup of 10.8 × and 9.3 × for Canny edge detection and Hough transform respectively using Spark. These results demonstrate the effectiveness of parallel implementation of computer vision algorithms to achieve good scalability for real-world applications.

In order to be able to quickly locate the position of the gap of an industrial device in the industrial inspection process, a fast gap detection algorithm based on machine vision is proposed by using the feature of a high gray level difference between the image gap and the background in machine vision. First, nonlinear equalization preprocessing is adopted, and then multiple one-dimensional functions are composed based on the sum of the gray values of the horizontal axis as well as the differentiation, and then the proximity position averaging operation is done after obtaining the positional information through the extreme value and the most value operation. The algorithm transforms the two-dimensional image into a combination of multiple one-dimensional functions with related features, and finally accurate position information is obtained by analysing and fusing the one-dimensional functions. Multiple discriminative bases are used for the same data source to ensure the accuracy of the results. Experiments show that the method proposed is faster and less error-prone, has higher accuracy and detection efficiency, and meets the needs of industrial real-time detection, compared with the traditional Hough transform algorithm and the LSD detection algorithm.