ORB Algorithm Research Articles

An Image-Based User Interface Testing Method for Flutter Programming Learning Assistant System

Flutter has become popular for providing a uniform development environment for user interfaces (UIs) on smart phones, web browsers, and desktop applications. We have developed the Flutter programming learning assistant system (FPLAS) to assist its novice students’ self-study. We implemented the Docker-based Flutter environment with Visual Studio Code and three introductory exercise projects. However, the correctness of students’ answers is manually checked, although automatic checking is necessary to reduce teachers’ workload and provide quick responses to students. This paper presents an image-based user interface (UI) testing method to automate UI testing by the answer code using the Flask framework. This method produces the UI image by running the answer code and compares it with the image made by the model code for the assignment using ORB and SIFT algorithms in the OpenCV library. One notable aspect is the necessity to capture multiple UI screenshots through page transitions by user input actions for the accurate detection of changes in UI elements. For evaluations, we assigned five Flutter exercise projects to fourth-year bachelor and first-year master engineering students at Okayama University, Japan, and applied the proposed method to their answers. The results confirm the effectiveness of the proposal.

Augmenting Three-Dimensional Model Annotation System with Enhanced Reality

This study proposes an augmented reality-based three-dimensional model annotation system, integrating cloud anchors, three-dimensional reconstruction, and augmented reality technology to achieve explicit three-dimensional annotations on models. Employing an improved ORB algorithm, the annotated model is persistently anchored in three-dimensional space through cloud anchors, presenting accurate spatial information and showcasing the depth of scenes and relationships between elements. The system supports multiple data types for annotations, such as text and images. Through a comparison with traditional two-dimensional annotation in a drone experiment, the system demonstrates higher experimental efficiency, providing more intuitive annotation guidance and enhancing remote guidance efficiency and user understanding of drones.

Research on Image Stitching Technology for Road Surface Cracks

Image stitching refers to the combination of multiple images with a certain over lappingarea rate acquired by the image acquisition device into one image, which has a full picture of each image. Image stitching technology is an important part in the field of digital image processing. With the increasing requirements for image quality and the limited resolution of ordinary cameras, it is impossible to obtain large angle of view and clear image at the same time. In this paper, the key technologies involved in pavement crack image stitching are intensively studied, including image feature point detection and matching, internal and external camera parameter estimation and optimization, and image fusion. The work done in this paper and the main innovations include: (1)Two main feature detection and description algorithms, SIFT algorithm and ORBalgorithm, are studied. In view of the fact that the SIFT algorithm has redundancy when the Gaussian pyramid is established, this paper appropriately reduces the number of groups and maintains the number and stability of feature point detection. Combined with the advantages and disadvantages of the improved SIFT algorithm and ORB algorithm, and the performance of the two algorithms in the detection of pavement cracks, the improvedSIF'T algorithm is used as the feature point detection algorithm, and rBRlEF algorithm for describing the feature points in the ORB algorithm is used as the feature point description algorithm. The description algorithm is called the BSIF'T algorithm. Experiments show that the robustness of BSIFT algorithm is similar to that of SIFT algorithm, which is better than ORB algorithm and faster than SIF'T algorithm. (2)Image fiusion algorithms are studied, including best seam-line search and image fusion. Aiming at the ghost phenomenon that may occur in overlapping regions, this paper proposes a best seam-line search algorithm based on distance transform.

Blowout Fluid Velocity Measurement Method based on High-speed Sequence Images

Given the high-temperature and high-pressure environment after the blowout, the staff usually cannot calculate and analyze various blowout parameters at close range and thus take effective measures to control accidents quickly. In this paper, a blowout fluid velocity measurement method is proposed based on high-speed sequence images, and simulation experiments are conducted. The scene conditions of blowout accidents are simulated and analyzed. On this basis, cameras are adopted to shoot a large number of simulated blowout fluid videos from different angles under outdoor conditions. Afterward, the video is converted into a multi-frame image, and the blowout fluid image feature point detection and extraction model are established based on the orb (oriented fast and rotated brief) algorithm. On the feature points extracted by the model, the gms algorithm is used to match the features of the blowout fluid images in the front and back frames. Additionally, the wrong matching points are eliminated using the ransac (random sample consensus) algorithm, and the obtained feature point pairs are employed into the flow velocity calculation model for flow velocity calculation. The results demonstrate that the measured fluid velocity is more accurate. The method proposed in this study can provide reliable data support for early blowout emergency rescue.

Improved ORB Algorithm Through Feature Point Optimization and Gaussian Pyramid

Improved ORB Algorithm Through Feature Point Optimization and Gaussian Pyramid

A Study of the Effectiveness of Popularized Perceptions of Religious Culture in the Context of Deep Learning on the Diffusion of Racial Justice Ideas

Abstract In the long historical evolution of humanity, religion has always demonstrated social, and cultural patterns and historical forms and has silently influenced some people’s ideological concepts and moral behaviors at all stages of development. This paper uses deep learning to investigate how the popularization of religious culture affects the dissemination of the concept of racial justice. The LBP-ORB algorithm model is constructed for the popularization method of religious culture, which is based on the improvement of the ORB algorithm, and the cognitive degree of different religious cultures can be improved by feature extraction through the LBP-ORB algorithm model. The model of this paper will be compared experimentally and then through fieldwork. The LBP-ORB algorithm model will be used to extract the features of local religious culture and popularize the local youths. Based on the analysis results, we explore the effect of the popularization of the cognition of religious culture on the spread of the concept of racial justice. After the popularization of religious culture, the college student group and middle school student group’s knowledge of the concept of racial justice increased substantially, with the number of people familiar with the college student group increasing by 88 and the number of people with general knowledge increasing by 122. The number of people in the middle school student population who were familiar with it reached 112, and the number of people with a general understanding reached 92. This paper’s LBPORB algorithm model has significantly increased the dissemination of the concept of racial justice after popularizing religious culture.

Practice and Exploration of Blended Teaching Based on VR Animation Laws of Motion Course

Abstract This study explores the application of virtual reality technology and traditional offline hybrid teaching mode for the specialized course “Animation Laws of Motion”. Through the improved ORB algorithm, this paper effectively combines the BRIEF and ORB image feature and matching algorithms to optimize capturing and matching image features. Two classes of animation majors in a school were selected for experiments in the study, and the effects of the two teaching modes were compared through half-term tests, pre- and post-test question analyses, and questionnaire surveys. The results show that after the teaching assisted by VR animation technology, students’ acceptance and emotional attitudes are significantly improved, and the students in the experimental class better mastered the six motion methods in the animation motion course and completed the animation design tasks with high quality. In the animation modeling course, the proportion of low-quality works in the experimental class is only 4%, showing significant teaching effects. The Research in this paper provides empirical support for the application of virtual reality technology in animation education. It demonstrates the potential of combining VR technology with traditional teaching in enhancing teaching effectiveness.

Registration and inpainting of biological slice images based on Bendlet transform and interval Shannon–Cosine wavelet

The fold that appeared in the micro-slice images needs to be inpainted exactly so that it can meet the requirements of the scientific experiments. The biological slice image usually consists of piecewise smooth regions with the closed loop contour which can be represented by the Bendlet function proposed in recent years. Therefore, a novel image inpainting method based on Bendlet and interval Shannon–Cosine wavelet is proposed. Since the deformation of the locust slice image is flexible, it is necessary to obtain as many feature points as possible to ensure the accuracy of the inpainting, so we introduce the curvature as a new registration element for registration. First, the homography matrix is obtained by calculating the correct feature points by our proposed registration method. Second, the fold position is located by homography matrix and inpainted by Shannon–Cosine interval wavelet interpolation. Finally, the pixel difference is eliminated through adaptive fusion. The results indicate that, in comparison to the SURF and ORB algorithms, our registration method significantly enhances the extraction of feature points, achieving a more even distribution. Furthermore, when compared to four other methods (K-SVD, BSCB, TV and Criminisi), as well as various interpolation methods such as cubic polynomial interpolation, cubic spline interpolation, and nearest neighbor interpolation, our approach consistently achieves the highest PSNR and SSIM values.

A study of digital exhibition visual design led by digital twin and VR technology

This paper addresses the challenge of enhancing the presentation style of visual exhibitions within museum settings, where the traditional approach can be perceived as unengaging. The study focuses on improving the performance of the ORB algorithm, a feature detection and matching method. Specifically, we introduce a novel technique that incorporates scale invariance into the algorithm by borrowing the detection concept from the BRISK algorithm. Furthermore, we integrate an improved PROSAC algorithm into the matching stage to enhance the quality of matched points. Our experimental results demonstrate significant improvements in the accuracy of the ORB algorithm. In comparison to the SIFT and GMS algorithms, the ORB algorithm exhibits enhanced matching accuracy, making it a compelling choice for feature-based computer vision applications. Moreover, the proposed approach creates a captivating illusion of seamlessly merging reality and virtual elements, meeting the expectations of system designers. This integration enhances the overall visitor experience within museum settings, fostering engagement and interaction.

Fast Automatic Registration of UAV Images via Bidirectional Matching.

Image registration plays a vital role in the mosaic process of multiple UAV (Unmanned Aerial Vehicle) images acquired from different spatial positions of the same scene. Aimed at the problem that many fast registration methods cannot provide both high speed and accuracy simultaneously for UAV visible light images, this work proposes a novel registration framework based on a popular baseline registration algorithm, ORB-the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elemental Features) algorithm. First, the ORB algorithm is utilized to extract image feature points fast. On this basis, two bidirectional matching strategies are presented to match obtained feature points. Then, the PROSRC (Progressive Sample Consensus) algorithm is applied to remove false matches. Finally, the experiments are carried out on UAV image pairs about different scenes including urban, road, building, farmland, and forest. Compared with the original version and other state-of-the-art registration methods, the bi-matching ORB algorithm exhibits higher accuracy and faster speed without any training or prior knowledge. Meanwhile, its complexity is quite low for on-board realization.

Fast Estimation of Loader’s Shovel Load Volume by 3D Reconstruction of Material Piles

Fast and accurate measurement of the volume of earthmoving materials is of great significance for the real-time evaluation of loader operation efficiency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fill factor estimation, and it has significant theoretical research and engineering application value.

Unmanned aerial vehicle remote sensing image registration based on an improved oriented FAST and rotated BRIEF- random sample consensus algorithm

Unmanned Aerial Vehicle (UAV) remote sensing image registration is the key step of remote sensing image stitching, image fusion and multi-frames image super-resolution. Its speed and accuracy determines the effect of remote sensing image applications, such as object detection, environment monitoring. To meet the speed and accuracy requirements of UAV remote sensing image registration, an improved Oriented FAST and Rotated BRIEF- Random sample consensus (ORB-RANSAC) algorithm is proposed. Firstly, images to be registered are divided into non-overlapping sub-images, and then a simplified image pyramid is constructed for these sub-images to get scale invariance. Secondly, the traditional FAST corner detection algorithm is improved by setting the adaptive corner detection threshold, and more feature points are detected. Meanwhile, the traditional quadtree algorithm is improved to remove redundant feature points and keep the remaining high-quality feature points. Thirdly, feature points coarse matching is done by bidirectional matching combined with cosine similarity method. Finally, the improved RANSAC algorithm is used for feature points fine matching to eliminate mismatches and calculate the transformation matrix. Experiment results show that, comparing with the traditional ORB algorithm, the number of feature points detected is significantly increased and its distribution is more uniform, and the correct matching rate increases by 58.10% in the case of image scale changing. Comparing with the state-of-the-art UAV remote sensing image registration algorithm, the correct matching rate and mutual information of our method are increased by 0.68% and 1.91% respectively, matching time and root mean square error are reduced by 3.89% and 11.2% respectively.

Retracted: Tobacco Leaves Disease Identification and Spot Segmentation Based on the Improved ORB Algorithm

Retracted: Tobacco Leaves Disease Identification and Spot Segmentation Based on the Improved ORB Algorithm

Measurement of displacement and top beam attitude angle of advanced hydraulic support based on visual detection

We constructed an advanced hydraulic support test bench and proposed a visual detection-based approach for measuring the displacement and attitude angle of the advanced hydraulic support. Firstly, our methodology involved using a binocular camera for image acquisition and stereo rectification of the advanced hydraulic support. Then, we employed the ORB algorithm to detect feature points and the SGBM matching algorithm to determine the disparity value. Finally, the attitude angle solution algorithm and the triangulation method are written to calculate the support displacement and the top beam attitude angle. Results showed that the advanced hydraulic support had an average absolute error of 8 mm in displacement measurement and 0.4° in attitude angle measurement, meeting the accuracy requirements for fully mechanized mining face operations.

A Feature Matching Method based on the Convolutional Neural Network

Nowadays, feature based 3D reconstruction and tracking technology have been widely used in the medical field. Feature matching is the most important step in feature-based 3D reconstruction process, as the accuracy of feature matching directly affects the accuracy of subsequent 3D point cloud coordinates. However, the matching performance of traditional feature matching methods is poor. To overcome this limitation, a method of matching based on convolutional neural network is presented. The convolutional neural network is trained by collecting a training set on the video sequence of a certain length from starting frame. The matched feature points in different endoscopic video frames are treated as the same category. The feature points in subsequent frames are matched by network classification. The proposed method is validated using the silicone simulation heart video and the endoscope video of the vivo beating heart obtained by Da Vinci’s surgical robot. Compared with SURF and ORB algorithms, as well as other methods, the experimental results show that the feature matching algorithm based on convolutional neural network is effective in the feature matching effect, rotation invariance, and scale invariance. For the first 200 frames of the video, the matching accuracy reached 90%.

Bubble Trajectory Tracking Based on ORB Algorithm

The system of gas-liquid two-phase bubbly flows is widely found in many industrial fields, such as nuclear energy, chemical, petroleum, and refrigeration. Bubbly two-phase flows measuring including detection and tracking affects the specific engineering problem solving to a great extent. The particle tracking velocity (PTV) algorithm is generally used for the tracking of the particles in the flow field. However, it does not take the shape change of particles into account in the process of flow. In this paper, a kind of bubble feature matching method based on ORB algorithm is proposed, and the edge detection method of findContours in OpenCV is used to extract the bubble contour in the image. The proposed algorithm implements the trajectory tracking of the bubbles with shape change when moving up in liquid. The feasibility of bubble trajectory tracking is shown by displaying of different bubble tracks in the plan, 3D plots and contour changing plots.

Critical Evaluation of Architectural Rudiments from the Perspective of Religion

India owns 60% of the world's second-largest agricultural land area. Around 10% to 40% of the crops are impacted by birds. We'll attempt to find an answer to the following questions in this project. Our main objective is to repel the birds without hurting them, so we are driven to complete the task without using any human effort. We have selected one crop and analyzed. There are four types of birds that are harming the paddy, according to the survey that was done. The peacock, house sparrow, pegion and grey patridge have the biggest effects as compared to other species. For machine learning and digital image processing, we use the ORB algorithm approach. The primary library employed is the open CV. The Pictures of birds entering the agriculture field is captured and compared with the above four species. If the result is positive then the signal is transmitted to the microcontroller. Then the microcontroller functions the necessary hardware to repel the birds. Key Words: Birds, ORB Algorithm, Affecting birds, Features of image.

Real-Time Relative Positioning Study of an Underwater Bionic Manta Ray Vehicle Based on Improved YOLOx

Compared to traditional vehicles, the underwater bionic manta ray vehicle (UBMRV) is highly maneuverable, has strong concealment, and is an emerging research field in underwater vehicles. Based on the completion of the single-body research, it is crucial to research the swarm of UBMRVs for the implementation of complex tasks, such as large-scale underwater detection. The relative positioning capability of the UBMRV is the key to realizing a swarm, especially when underwater acoustic communications are delayed. To solve the real-time relative positioning problem between individuals in the UBMRV swarm, this study proposes a relative positioning method based on the combination of the improved object detection algorithm and binocular distance measurement. To increase the precision of underwater object detection in small samples, this paper improves the original YOLOx algorithm. It increases the network’s interest in the object area by adding an attention mechanism module to the network model, thereby improving its detection accuracy. Further, the output of the object detection result is used as the input of the binocular distance measurement module. We use the ORB algorithm to extract and match features in the object-bounding box and obtain the disparity of the features. The relative distance and bearing information of the target are output and shown on the image. We conducted pool experiments to verify the proposed algorithm on the UBMRV platform, proved the method’s feasibility, and analyzed the results.

Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization.

Due to the influence of the shooting environment and inherent image characteristics, there is a large amount of interference in the process of image stitching a geological borehole video. To accurately match the acquired image sequences in the inner part of a borehole, this paper presents a new method of stitching an unfolded borehole image, which uses the image generated from the video to construct a large-scale panorama. Firstly, the speeded-up robust feathers (SURF) algorithm is used to extract the image feature points and complete the rough matching. Then, the M-estimator sample consensus (MSAC) algorithm is introduced to remove the mismatched point pairs and obtain the homography matrix. Subsequently, we propose a local homography matrix offset optimization (LHOO) algorithm to obtain the optimal offset. Finally, the above process is cycled frame by frame, and the image sequence is continuously stitched to complete the construction of a cylindrical borehole panorama. The experimental results show that compared with those of the SIFT, Harris, ORB and SURF algorithms, the matching accuracy of our algorithm has been greatly improved. The final test is carried out on 225 consecutive video frames, and the panorama has a good visual effect, and the average time of each frame is 100 ms, which basically meets the requirements of the project.

Hand-Drawn Electronic Component Recognition Using ORB

There is growth in interest to build a system that can recognize freely drawn symbols. In past two years, the situation in the world has become more difficult, almost all the work in their organizations has been digitalized. However, many challenges remain in terms of the recognition accuracy of different drawing styles. To address these challenges, a new approach is proposed to classify and predict the hand-drawn component. In this work Computer Vision approach using the ORB algorithm is used to recognize and predict the component. The drawing tools that are being used need to be upgraded in order to have a better learning experience in order to recognize the symbol that is drawn and directly convert it into digitized form. As it is very difficult to write it on an online platform every time this method would be very useful. Here 15 different electronic components are considered with three different orientations. In this approach, GUI is used to draw the symbol which is easy instead of picking and placing. This input-drawn image is given to the model developed using the CV algorithm, where the input image gets compared with the database image, according to ORB using the FAST method key points are generated for both images. Using a Brute force matcher both images get matched depending on the number of matched features, and the output image gets predicted. The results of the ORB and SIFT CV algorithms are analyzed and compared.