Bounding Rectangle Research Articles

Geometric feature extraction and its profile accuracy evaluation for pockets on curved aircraft skin from scanned point clouds

Abstract Dimension inspection is crucial for aircraft skin manufacturing and assembly. To meet weight loss and other functional requirements, large numbers of shallow pockets are distributed on the surface of thin-walled aircraft skin. It is difficult to evaluate the profile accuracy of the pockets, as the aircraft skin is composed of curved surfaces and its actual geometry is inconsistent with the nominal model. This study proposes a method to evaluate the profile and position accuracy for the pockets on aircraft skin utilizing point clouds obtained by 3D scanners. Firstly, the feature contour lines are identified and extracted from the scanned aircraft skin surface point clouds by adopting the local curvature-aware method and minimum bounding rectangle algorithm. Then, an algorithm is proposed to mitigate the contour identification deviations of the segmented area by employing the shortest geodesic distance and transition zone section projection, so as to enhance the measurement accuracy. Finally, the viability of the proposed method for evaluating pockets on aircraft skin is verified by the simulation, and the reliability and robustness of the proposed method are verified by the simulation and experiment. Experimental results show that the measurement accuracy of the pocket can be improved from 0.5mm to 0.0505mm, compared with traditional specialized conformation fixture methods.

SGIR-Tree: Integrating R-Tree Spatial Indexing as Subgraphs in Graph Database Management Systems

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs.

CSQUiD: an index and non-probability framework for constrained skyline query processing over uncertain data

Uncertainty of data, the degree to which data are inaccurate, imprecise, untrusted, and undetermined, is inherent in many contemporary database applications, and numerous research endeavours have been devoted to efficiently answer skyline queries over uncertain data. The literature discussed two different methods that could be used to handle the data uncertainty in which objects having continuous range values. The first method employs a probability-based approach, while the second assumes that the uncertain values are represented by their median values. Nevertheless, neither of these methods seem to be suitable for the modern high-dimensional uncertain databases due to the following reasons. The first method requires an intensive probability calculations while the second is impractical. Therefore, this work introduces an index, non-probability framework named Constrained Skyline Query processing on Uncertain Data (CSQUiD) aiming at reducing the computational time in processing constrained skyline queries over uncertain high-dimensional data. Given a collection of objects with uncertain data, the CSQUiD framework constructs the minimum bounding rectangles (MBRs) by employing the X-tree indexing structure. Instead of scanning the whole collection of objects, only objects within the dominant MBRs are analyzed in determining the final skylines. In addition, CSQUiD makes use of the Fuzzification approach where the exact value of each continuous range value of those dominant MBRs’ objects is identified. The proposed CSQUiD framework is validated using real and synthetic data sets through extensive experimentations. Based on the performance analysis conducted, by varying the sizes of the constrained query, the CSQUiD framework outperformed the most recent methods (CIS algorithm and SkyQUD-T framework) with an average improvement of 44.07% and 57.15% with regards to the number of pairwise comparisons, while the average improvement of CPU processing time over CIS and SkyQUD-T stood at 27.17% and 18.62%, respectively.

Research on Process Control of Laser-Based Direct Energy Deposition Based on Real-Time Monitoring of Molten Pool

In the process of laser-based direct energy deposition (DED-LB), the quality of the deposited layer will be affected by the process parameters and the external environment, and there are problems such as poor stability and low accuracy. A molten pool monitoring method based on coaxial vision is proposed. Firstly, the molten pool image is captured by a coaxial CCD camera, and the geometric features of the molten pool are accurately extracted by image processing techniques such as grayscale, median filtering noise reduction, and K-means clustering combined with threshold segmentation. The molten pool width is accurately extracted by the Canny operator combined with the minimum boundary rectangle method, and it is used as the feedback of weld pool control. The influence of process parameters on the molten pool was further analyzed. The results show that with an increase in laser power, the width and area of the molten pool increase monotonously, but exceeding the material limit will cause distortion. Increasing the scanning speed will reduce the size of the molten pool. By comparing the molten pool under constant power mode and width control mode, it is found that in width control mode, the melt pool width fluctuates less, and the machining accuracy is improved, validating the effectiveness of the real-time control system.

Observation routes and external watchman routes

We introduce the Observation Route Problem (ORP) defined as follows: Given a set of n pairwise disjoint obstacles (regions) in the plane, find a shortest tour (route) such that an observer walking along this tour can see (observe) each obstacle from some point of the tour. The observer does not need to see the entire boundary of an obstacle. The tour is not allowed to intersect the interior of any region (i.e., the regions are obstacles and therefore out of bounds). The problem exhibits similarity to both the Traveling Salesman Problem with Neighborhoods (TSPN) and the External Watchman Route Problem (EWRP). We distinguish two variants: the range of visibility is either limited to a bounding rectangle, or unlimited. We obtain the following results:(I) Given a family of n disjoint convex bodies in the plane, computing a shortest observation route does not admit a (clog⁡n)-approximation unless P=NP for an absolute constant c>0. (This holds for both limited and unlimited vision.)(II) Given a family of disjoint convex bodies in the plane, computing a shortest external watchman route is NP-hard. (This holds for both limited and unlimited vision; and even for families of axis-aligned squares.)(III) Given a family of n disjoint fat convex polygons in the plane, an observation tour whose length is at most O(log⁡n) times the optimal can be computed in polynomial time. (This holds for limited vision.)(IV) For every n≥5, there exists a convex polygon with n sides and all angles obtuse such that its perimeter is not a shortest external watchman route. This refutes a conjecture by Absar and Whitesides (2006).

Semi-supervised anchorless single-engine grip detection

The extensive application of artificial intelligence in wireless communication, 3D reconstruction, and target location has successfully solved the modeling problem. Grasping specific objects in a stacked scene is a difficult task to achieve robot grasping. In this paper, a semi-supervised anchor-less single-gun grasping detection framework is proposed to help robots grasp objects easily. The framework first designs the AFMaskDetDep (AFMDD) network and then predicts the contours of objects by looking at sensing modules. Finally, the boundary value of the minimum boundary rectangle is obtained by judging the optimal target and the optimal grasping point of the inference module, and the final result is obtained by rotating back to the coordinate output on the original image area. Our proposed approach yields state-of-the-art performance with improved accuracy and computational speed on the VMRD and Cornell datasets, respectively. Experiments show that the algorithm can help the robot grasp a single specific object and has a high success rate in overlapping scenes. The success rate of each grip exceeds 93.5%.

Detection and Removal of Assymmetrical Skin Lesions Using DU-Net for Patch Extraction

The incidence of melanoma, a highly fatal kind of malignant tumor, is exhibiting an upward trend in its progression. Timely intervention and management of skin cancer significantly enhance the likelihood of survival. Analyzing dermoscopic images poses challenges for dermatologists due to various distracting factors, including fluctuations in lighting and reflections on the skin's surface. Accurately delineating the area of skin pathology is crucial for diagnosing the specific skin condition. This study introduces DU-net, a method for identifying and removing pigmented skin lesions. The DU-net framework incorporates deep convolutional neural networks, notably YOLOv5, to perform patch detection. It also employs asymmetrical patch contouring methods to preserve edge information. Additionally, clustering algorithms are utilized to identify pixel groups and extract patches from the image. The De Trop Noise Exclusion technique, in conjunction with the process of in-painting, is employed to successfully eliminate hair from the images within the ISIC-2018 and 2019 datasets. Rigorous annotation of skin images with lesions of various sizes and shapes using rectangle bounding is carried out, and YOLOv5 hyper-parameters are fine-tuned to achieve high-confidence multiple lesion detection in dermoscopic images. Despite complex textures and unclear boundaries, our approach consistently detects and labels patches, accurately segmenting the areas of skin pathology. The model's performance is assessed on these datasets using various parameter metrics, the results of the study indicate that the segmentation strategies described in this research exhibit an average accuracy ranging from about 92% to 94%.

Towards an obstacle detection system for robot obstacle negotiation

PurposeTo solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles.Design/methodology/approachThe system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method.FindingsThe obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage.Originality/valueThis paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.

Comparison of the Minimum Bounding Rectangle and Minimum Circumscribed Ellipse of Rain Cells from TRMM

Comparison of the Minimum Bounding Rectangle and Minimum Circumscribed Ellipse of Rain Cells from TRMM

Action capture method of animated characters based on virtual reality technology

Abstract Virtual reality technology is a kind of animation technology that is widely used in the creation of film and television animation art, which makes the technological imprint of film and television animation art creation exceptionally obvious. The article utilizes virtual reality technology to create a virtual scene for character motion capture in film and television animation by combining the video motion capture system. The camera position-solving method is used to map the points in the three-dimensional space to the two-dimensional image plane, and then the high-dimensional image signals are converted to the low-dimensional signals of human body postures through the minimum bounding rectangle of the foreground image to realize the solving of the character’s action posture information. Based on the graph convolution neural network, a time domain graph convolution model is established with the help of layering and cross-frame connection of the null time domain convolution to realize the 3D pose estimation and capture of the character movement in film and television animation. The joints’ confidence smooths the pose sequence of the character’s action. The maximum error of the yaw, pitch, and roll angles in 15 frames does not exceed 0.4° in the pose resolution of the animated character. Real and measured joint flexion and extension angles have errors that are less than 0.3°. The average error of the Euler angle data for character motion capture is between 1.37 and 3.86°, and the MPJVE error of the smoothed data is reduced by 1.15 mm compared to the pre-processing data. The virtual scene constructed by relying on the virtual reality technology can help the video motion capture system to obtain the performance of film and television animated characters, and provide a reference to optimize the film and television animated characters’ movements.

Regular Building Outline Extraction Based on Multi-Level Minimum Bounding Rectangle

Regular Building Outline Extraction Based on Multi-Level Minimum Bounding Rectangle

STUDY ON AUTOMATIC REGISTRATION METHOD OF SOURCE DATA FOR CIM BUILDING MODEL CONSTRUCTION

Abstract. The City Information Model (CIM) is a digital model designed to describe, analyze, and manage urban spatial information. It integrates various urban elements, providing decision support for urban planning, design, and management. This study focuses on the construction of building models in CIM, particularly the issue of automatic registration of source data. We propose an automatic registration method based on DWG-format architectural design drawings and SHAPE-format remote sensing building outline information. This method extracts the axis line information of the building from the plan design drawings, indirectly obtains feature points, and selects registration base points, thereby achieving the registration of real estate subdivision data. Meanwhile, by automatically fitting the outer bounding rectangle of the building and dynamically adjusting the angle, this study realizes the automatic registration of the aligned multilayer real estate subdivision data with the building outline data obtained from remote sensing images. This research provides an effective method for the automatic and batch accurate alignment of data for the construction of CIM building models, thereby enhancing the precision and reliability of the modeling.

Multilevel Regularization Method for Building Outlines Extracted from High-Resolution Remote Sensing Images

Extraction of vectorized building outlines from high-resolution remote sensing images is highly useful for various application fields, such as map creation and urban planning. However, this process is often complicated by external factors, such as trees and shadows, which cause issues, such as excessive node redundancy, jagged lines, and unclear corner points. In this study, a multilevel regularization method was designed for building outlines, including the “overall–local–detail” levels. First, overall regularization was performed by combining the minimum bounding rectangle of the building outline with the Hausdorff distance method. Next, based on the convex hull of the building outline and the distribution characteristics of nodes along the outline, the building outline was divided into multiple line chains and classified for local regularization. Finally, the details of the building outline were processed, with the parallel and perpendicular characteristics enhanced to obtain the final regularization results. The experimental results showed that the proposed method effectively enhances the edge representation accuracy of building outlines and significantly improves the accuracy and regularity of building edges. Furthermore, it strengthens the orthogonal characteristics of building outlines, providing more accurate representations of true building outlines.

Simplification and Regularization Algorithm for Right-Angled Polygon Building Outlines with Jagged Edges

Building outlines are important for emergency response, urban planning, and change analysis and can be quickly extracted from remote sensing images and raster maps using deep learning technology. However, such building outlines often have irregular boundaries, redundant points, inaccurate positions, and unclear turns arising from variations in the image quality, the complexity of the surrounding environment, and the extraction methods used, impeding their direct utility. Therefore, this study proposes a simplification and regularization algorithm for right-angled polygon building outlines with jagged edges. First, the minimum bounding rectangle of the building outlines is established and populated with a square grid based on the smallest visible length principle. Overlay analysis is then applied to the grid and original buildings to extract the turning points of the outlines. Finally, the building orientation is used as a reference axis to sort the turning points and reconstruct the simplified building outlines. Experimentally, the proposed simplification method enhances the morphological characteristics of building outlines, such as parallelism and orthogonality, while considering simplification principles, such as the preservation of the direction, position, area, and shape of the building. The proposed algorithm provides a new simplification and regularization method for right-angled polygon building outlines with jagged edges.

Position measurement technology of boom-type roadheader based on binocular vision

Positioning of underground mining equipment in coal mines is a primary challenge in the intelligent development of tunnel excavation. Accurate and reliable position measurement plays a crucial role in improving excavation efficiency. However, the stability and accuracy of traditional measurement methods are difficult to guarantee due to factors such as vibration, magnetic interference, and the absence of GPS signals in coal mine environments. To address the problem of unstable measurement of the boom-type roadheader‘s position, this paper proposes a binocular vision-based measurement technique that utilizes four light spots as characteristic points. By processing the target images captured by the binocular camera and combining the minimum bounding rectangle and ellipse fitting of the spot regions, the method successfully obtains the four light spot characteristics. Subsequently, precise matching and stereo distance measurement of the target in the left and right images enable the determination of the boom-type roadheader’s posi-tion information. A positioning platform based on binocular vision is built and experimentally evaluated. The results demonstrate that this method can achieve accurate spot extraction and stable measurement of the machine’s position, even in complex backgrounds such as mixed lighting and low illumination. The planar measurement errors within a distance range of 50 m are all within ±25 mm, which basically meets the required construction precision for tunnel excavation.

Machine‐learning‐based morphological analyses of leaf epidermal cells in modern and fossil ginkgo and their implications for palaeoclimate studies

AbstractLeaf stomata form an essential conduit between plant tissue and the atmosphere, thus presenting a link between plants and their environments. Changes in their properties in fossil leaves have been studied widely to infer palaeo‐atmospheric‐CO2 in deep time, ranging from the Palaeozoic to the Cenozoic. Epidermal cells of leaves, however, have often been neglected for their usefulness in reconstructing past‐environments, as their irregular shape makes the manual analyses of epidermal cells a challenging and error‐prone task. Here, we used machine‐learning (using the U‐Net architecture, which evolved from a fully convolutional network) to segment epidermal cells automatically, to efficiently reduce artificial errors. We furthermore applied minimum bounding rectangles to extract length‐to‐width ratios (RL/W) from the irregularly shaped cells. We applied this to a dataset including over 21 000 stomata and 170 000 epidermal cells in 114 Ginkgo leaves from 16 locations spanning three climate zones in China. Our results show negative correlations between the RL/W and specific climatic parameters, suggesting that local temperature and precipitation conditions may have affected the RL/W of epidermal cells. We subsequently tested this methodology and the observations from the modern dataset on 15 fossil ginkgoaleans from the Lower to the Middle Jurassic (China). It suggested that the RL/W values of fossil ginkgo generally had a similar negative response to warmer climatic backgrounds as modern G. biloba. The automated analyses of large palaeo‐floral datasets provide a new direction for palaeoclimate reconstructions and emphasize the importance of hidden morphological characters of epidermal cells in ginkgoaleans.

Major and minor elliptical axes and bounding rectangle dimensions in clay brick powder particles from changing milling conditions

Starting from the observation that regular shape parameters such as equivalent diameter, area-perimeter ratio, circularity, compactness and roundness fail to adequately identify shapes of materials, alternative shape parameters are proposed to remedy this shortcoming. In this study, plugins in image analysis are extended to explore the major and minor elliptical axes and bounding rectangle dimensions in clay brick powder particles from changing milling conditions of different masses of clay bricks in ball mill. It turned out that with developed plugins in ImageJ, extracting such shape parameters of clay brick powder samples was not problematic. For the generated reciprocal aspect ratio (RAR) values ranging from 0.51 to 0.53, majority of the particles were noticed to be within the ellipse-inclined shapes. Using the feret major axis ratio (FMR) values for the formulated bounding rectangle dimensions, most particles appeared to be bound in rectangle-inclined shapes. Despite the generated major and minor elliptical axes and bounding rectangle parameters, grinding conditions in this research did not present any significant influence on such shape parameters of CBP specimens. It is believed that the developments of quantifying the major and minor elliptical axes and bounding rectangle dimensions of the CBP particles in this study are ground on solid foundations without need for extensive experimental works. The proposed developments are therefore valid and could be dedicated for characterising pozzolanic materials, because of their interests in cement-based composites.

A New Comprehensive Algorithm for the Cutting and Packing Problem with Position Constraints

<p>The cutting and packing (CP) is a widespread problem in the production processes of garment, shoes, board, and in other manufacturing industries. It is closely related to the production and management costs. This problem refers to the placement of pieces, without overlapping, in a limited space by maximizing the space utilization. In this work, a CP variant with position constraints, namely the horizontal striped fabric layout problem (HSFL), is investigated. We propose a solution approach for it, where a top-left fill (TLF) algorithm based on key points positioning strategy is used to deal with position constraints in the stripe alignment problem. This algorithm establishes a key points sequence for every piece, when determining the placement of pieces, compare the key points sequence of pieces. This algorithm also eliminates the error often occurred in the rectangle bounding box algorithm at the time of simplifying the layout process. The TLF algorithm based on key points positioning strategy and genetic algorithm (GA) are combined for obtaining the optimum layout. The datasets used in the computational experiment are collected from an apparel industry as well as from other publications. The comparison of our experimental results with the best known results shows that the proposed approach could improve the average computing speed and achieve higher utilization in 10 out of 16 instances. It can be concluded that the proposed approach is potential in balancing the computing speed and utilization rate. The algorithm has been successfully applied in an apparel company in China.</p> <p> </p>

SbMBR Tree—A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining

In the field of data analysis and mining, adopting efficient data indexing and compression techniques to spatiotemporal data can significantly reduce computational and storage overhead for the abilities to control the volume of data and exploit the spatiotemporal characteristics. However, traditional lossy compression techniques are hardly suitable due to their inherently random nature. They often impose unpredictable damage to scientific data, which affects the results of data mining and analysis tasks that require certain precision. In this paper, we propose a similarity-based minimum bounding rectangle (SbMBR) tree, a tree-based indexing and compression method, to address the aforementioned problem. Our method can hierarchically select appropriate minimum bounding rectangles according to the given maximum acceptable errors and use the average value contained in each selected MBR to replace the original data to achieve data compression with multi-layer loss control. This paper also provides the corresponding tree construction algorithm and range query processing algorithm for the indexing structure mentioned above. To evaluate the data quality preservation in cross-domain data analysis and mining scenarios, we use mutual information as the estimation metric. Experimental results emphasize the superiority of our method over some of the typical indexing and compression algorithms.

VDVM: An automatic vertebrae detection and vertebral segment matching framework for C-arm X-ray image identification.

C-arm fluoroscopy, as an effective diagnosis and treatment method for spine surgery, can help doctors perform surgery procedures more precisely. In clinical surgery, the surgeon often determines the specific surgical location by comparing C-arm X-ray images with digital radiography (DR) images. However, this heavily relies on the doctor's experience. In this study, we design a framework for automatic vertebrae detection as well as vertebral segment matching (VDVM) for the identification of vertebrae in C-arm X-ray images. The proposed VDVM framework is mainly divided into two parts: vertebra detection and vertebra matching. In the first part, a data preprocessing method is used to improve the image quality of C-arm X-ray images and DR images. The YOLOv3 model is then used to detect the vertebrae, and the vertebral regions are extracted based on their position. In the second part, the Mobile-Unet model is first used to segment the vertebrae contour of the C-arm X-ray image and DR image based on vertebral regions respectively. The inclination angle of the contour is then calculated using the minimum bounding rectangle and corrected accordingly. Finally, a multi-vertebra strategy is applied to measure the visual information fidelity for the vertebral region, and the vertebrae are matched based on the measured results. We use 382 C-arm X-ray images and 203 full length X-ray images to train the vertebra detection model, and achieve a mAP of 0.87 in the test dataset of 31 C-arm X-ray images and 0.96 in the test dataset of 31 lumbar DR images. Finally, we achieve a vertebral segment matching accuracy of 0.733 on 31 C-arm X-ray images. A VDVM framework is proposed, which performs well for the detection of vertebrae and achieves good results in vertebral segment matching.