Bounding Rectangle Research Articles

Coarse-to-Fine Localization for Detecting Misalignment State of Angle Cocks.

The state of angle cocks determines the air connectivity of freight trains, and detecting their state is helpful to improve the safety of the running trains. Although the current research for fault detection of angle cocks has achieved high accuracy, it only focuses on the detection of the closed state and non-closed state and treats them as normal and abnormal states, respectively. Since the non-closed state includes the fully open state and the misalignment state, while the latter may lead to brake abnormally, it is very necessary to further detect the misalignment state from the non-closed state. In this paper, we propose a coarse-to-fine localization method to achieve this goal. Firstly, the localization result of an angle cock is obtained by using the YOLOv4 model. Following that, the SVM model combined with the HOG feature of the localization result of an angle cock is used to further obtain its handle localization result. After that, the HOG feature of the sub-image only containing the handle localization result continues to be used in the SVM model to detect whether the angle cock is in the non-closed state or not. When the angle cock is in the non-closed state, its handle curve is fitted by binarization and window search, and the tilt angle of the handle is calculated by the minimum bounding rectangle. Finally, the misalignment state is detected when the tilt angle of the handle is less than the threshold. The effectiveness and robustness of the proposed method are verified by extensive experiments, and the accuracy of misalignment state detection for angle cocks reaches 96.49%.

Stripe segmentation of oceanic internal waves in SAR images based on Gabor transform and K-means clustering

Oceanic internal waves are an active ocean phenomenon that can be observed, and their relevant characteristics can be acquired using synthetic aperture radar (SAR). The locations of oceanic internal waves must be determined first to obtain the important parameters of oceanic internal waves from SAR images. An oceanic internal wave segmentation method with integrated light and dark stripes was described in this study. To extract the SAR image characteristics of oceanic internal waves, the Gabor transform was initially used, and then the K-means clustering algorithm was used to separate the light (dark) stripes of oceanic internal waves from the background in the SAR images. The regions of the dark (light) stripes were automatically determined based on the differences between the three classes, that is, the dark stripes, light stripes, and background area. Finally, the locations of the dark (light) stripes were determined by shifting a given distance along the normal direction of the long side with the minimum bounding rectangle of the light (dark) stripes. The best segmentation results were obtained based on the intersection over the union of the images, and the accuracy of segmentation was verified. Furthermore, the effectiveness and practicability of the proposed method in the light and dark stripe segmentation of SAR images of oceanic internal waves were illustrated. The proposed method prepares the foundation for future inversion studies of oceanic internal waves.

Parameter Identification of Ice Drift toward Cross-River Bridges in Cold Regions

Ice load is one of the main loads that needs to be considered in the design and construction of wading bridges (bridges whose piers are submerged in water) in cold regions, and ice velocity, area, and thickness are the main parameters that affect ice load. A noncontact measurement technology based on the “ scale method” was proposed to realize small-range monitoring of river ice by laying calibration grids on the ice surface. The “bounding rectangle method” and Harris corner detection were used to accurately identify the area and velocity of a single ice drift. Also, the thickness of ice drift was estimated by statistical analysis. Considering the Huma River basin in the Daxing’an Mountains as the research object, field tests were performed to determine the flow ice parameters. The results indicate that noncontact measurement technology can accurately identify the velocity and area of ice drift and has wide application prospects, thereby providing a reference for the structural design and safety evaluation of wading bridges in cold areas.

Raster Intervals: An Approximation Technique for Polygon Intersection Joins

Many data science applications, most notably Geographic Information Systems, require the computation of spatial joins between large object collections. The objective is to find pairs of objects that intersect, i.e., share at least one common point. The intersection test is very expensive especially for polygonal objects. Therefore, the objects are typically approximated by their minimum bounding rectangles (MBRs) and the join is performed in two steps. In the filter step, all pairs of objects whose MBRs intersect are identified as candidates; in the refinement step, each of the candidate pairs is verified for intersection. The refinement step has been shown notoriously expensive, especially for polygon-polygon joins, constituting the bottleneck of the entire process. We propose a novel approximation technique for polygons, which (i) rasterizes them using a fine grid, (ii) models groups of nearby cells that intersect a polygon as an interval, and (iii) encodes each interval by a bitstring that captures the overlap of each cell in it with the polygon. We also propose an efficient intermediate filter, which is applied on the object approximations before the refinement step, to avoid it for numerous object pairs. Via experimentation with real data, we show that the end-to-end spatial join cost can be reduced by up to one order of magnitude with the help of our filter and by at least three times compared to using alternative intermediate filters.

A robust and lossless commutative encryption and watermarking algorithm for vector geographic data

As an organic integration of encryption and watermarking technology, commutative encryption and watermarking (CEW) overcomes the limitation that a single security protection technology cannot provide full-process data protection. Existing CEW algorithms for vector geographic data, however, only take attack robustness into account, making them inappropriate for copyright protection of high-precision vector geographic data since the influence of embedding watermark on data accuracy. This study presents a CEW algorithm that utilizes geometric features and the initial vertex order of features to address this issue. The minimum bounding rectangle (MBR) is chosen as the geometric characteristic in this method to provide independent operating space for watermark and encryption. The encryption is completed by randomly shifting the coordinates of the features. At the same time, the watermark information is embedded by modifying the features' starting vertex order. The watermark can be extracted either from the plaintext or the ciphertext, as the order of the element's starting points remains unchanged regardless of the coordinates. Finally, the commutative, robustness, and lossless ness of the algorithm in this study are experimentally validated. As can be demonstrated by the results, the proposed algorithm is more robust, lossless, and secure than some state-of-the-art algorithms.

PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children

Computer-aided diagnosis systems in adult chest radiography (CXR) have recently achieved great success thanks to the availability of large-scale, annotated datasets and the advent of high-performance supervised learning algorithms. However, the development of diagnostic models for detecting and diagnosing pediatric diseases in CXR scans is undertaken due to the lack of high-quality physician-annotated datasets. To overcome this challenge, we introduce and release PediCXR, a new pediatric CXR dataset of 9,125 studies retrospectively collected from a major pediatric hospital in Vietnam between 2020 and 2021. Each scan was manually annotated by a pediatric radiologist with more than ten years of experience. The dataset was labeled for the presence of 36 critical findings and 15 diseases. In particular, each abnormal finding was identified via a rectangle bounding box on the image. To the best of our knowledge, this is the first and largest pediatric CXR dataset containing lesion-level annotations and image-level labels for the detection of multiple findings and diseases. For algorithm development, the dataset was divided into a training set of 7,728 and a test set of 1,397. To encourage new advances in pediatric CXR interpretation using data-driven approaches, we provide a detailed description of the PediCXR data sample and make the dataset publicly available on https://physionet.org/content/vindr-pcxr/1.0.0/.

A building polygonal object matching method based on minimum bounding rectangle combinatorial optimization and relaxation labeling

AbstractVolunteered geographic information (VGI) is an emerging phenomenon where anyone can create geographic information and share it with others. Compared with traditional authoritative geospatial data, it has several advantages, such as enriched data, instant updates, and low cost. The object matching method is widely used in VGI quality assessment and data updates. However, VGI matching faces certain challenges, such as the levels of detail that vary from object to object, the uneven distribution of data quality, and the automated matching requirement. To resolve these problems, this article proposes a new matching method that effectively combines the advantages of minimum bounding rectangle combinatorial optimization (MBRCO) and relaxation labeling. The proposed method (1) avoids setting the similarity threshold and weights and does not require training samples. This process is realized based on contextual information and optimization. (2) It overcomes the disadvantage that the MBRCO algorithm cannot distinguish adjacent buildings with similar shapes. Our approach is experimentally validated using two publicly available spatial datasets: OpenStreetMap and AutoNavi map. The experimental studies show that the proposed automatic matching method outperforms all the threshold‐based MBRCO methods and achieves high accuracy with a precision of 97.8% and a recall of 99.2%.

Quality Assessment Algorithm of X-Ray Images in Overall Girth Welds Based on Deep Neural Network

Defect recognition and assessment are essential technologies for the safety of pipelines. Recently, deep neural network methods have shown great progress in recognizing pipeline weld defects. However, the current methods cannot achieve online quality assessment for overall girth welds. To solve this problem, this paper proposes a novel quality assessment algorithm for defect recognition and assessment of X-ray girth weld images. First, a two-stage defect classifier based on residual neural network (ResNet) is proposed, which consists of a ResNet18 region classifier and a ResNet50 defect classifier. It can make up for the lack of a single classifier so that the performance and speed of defect recognition can be improved. Second, the feature-extraction calculator is used to extract and calculate the area of the weld defects based on the smallest bounding rectangle method. Third, we propose a quality evaluator based on an improved ensemble learning algorithm and long short-term memory (LSTM), so that the girth welds can be well assessed. Finally, an experiment is conducted using pipeline girth weld X-ray images. The analytic hierarchy process (AHP) method is used to comprehensively assess the level of overall girth-weld defects. The experimental results show that the proposed method is effective in recognizing and evaluating the girth-weld defects of X-ray images. And the accuracy of the quality evaluator can reach 92% for the quality assessment of overall girth welds, compared with manual quality decisions. It effectively achieves online quality assessment for girth welds and has higher evaluation accuracy.

A deep learning approach for predicting visceral pleural invasion in cT1 lung adenocarcinoma.

Currently, patients with cT1 stage non-small cell lung cancer measuring less than 2 cm are recommended to undergo sublobar resection. However, it should be noted that there is tumor heterogeneity within these lung nodules. Potential visceral pleural invasion (VPI) is regarded as a significant factor that contributes to local recurrence and poorer prognosis after sublobar resection and postoperative upstaging of the T-stage. Currently, there are no effective techniques for preoperative or intraoperative prediction of the status of VPI in lung nodules. The primary objective of this study is to develop a machine learning model for the non-invasive prediction of VPI, thereby providing surgical decision-making support for surgeons during operations. A total of 983 patients with nodules located within 5 mm distance from the pleura were included. Machine learning models were developed utilizing preprocessed 2D, 2.5D, and 3D computed tomography (CT) imaging data. These models were employed to predict the status of VPI, leveraging radiomics and deep learning techniques. The aforementioned three groups of data were further categorized into region of interest (ROI)-only (exclusively focused on the ROI) and ROI-rect (the minimum bounding rectangle or cuboid of the ROI) groups, based on whether they included images outside the ROI. Receiver operating characteristic (ROC) curves were created for the assessment of predictive accuracy across different models. Employing the Youden's index, patients were categorized into high-risk and low-risk groups based on the model's criteria, which was then followed by an in-depth analysis of overall survival rates among the distinct patient cohorts. This study included a training cohort of 786 patients and a validation cohort of 197 patients. In comparison to radiomic and radiological models, deep learning models, especially the 2D-rect model, demonstrated better predictive performance. Although the 3D-ROI-only model exhibited the highest areas under the curve (AUC) value (0.952), its predictive performance for the status of VPI was found to be inferior according to the decision curve, calibration curve, and survival analysis. The developed deep learning signature offers a robust instrument for the precise prognostication of vascular invasion in stage cT1 lung adenocarcinoma, thereby enhancing stratification for prognostic evaluation. Moreover, the application of this advanced computational model aids in the refinement of therapeutic approach formulation for individuals diagnosed with cT1 lung adenocarcinoma.

A Palletizing System for Microchannel Heat Exchangers Based on 3D Visual Guidance

Aiming at the problems of slow manual palletizing, high risk and easy damage to products after the assembly of microchannel heat exchanger, a palletizing system based on 3D visual guidance is presented in this paper. Firstly, an automatic calibration method for RGB-D camera extrinsic parameters was adopted: used random sample consensus algorithm RANSAC for plane fitting, and the pallet was idealized as a plane. The camera extrinsic parameters were calculated according to the rotation relationship between the plane normal vector and the camera coordinate system. When it was performing detection, converted depth data into point cloud data and preprocessed, including coordinate transformation, downsampling, etc. After that, according to the characteristics of stacking, a point cloud segmentation algorithm based on depth and the number of points was used, which could not only segment the pallet and sponge strips, but also effectively detect obstacles. For different parts segmented, the pallet was positioned by fitting the minimum bounding rectangle, and the stacking depths were obtained by calculating the centroid of the sponge strips. Finally, a series of constraints were used to determine whether the unloading conditions were met. The experimental results show that the automatic calibration is effective, the time consuming is about 0.43s, and the depth range of the pallet is 11mm after calibration. The algorithm can accurately identify various situations above the stacking and the time consuming is about 1.63s.

SSWT-UUNet++ Submarine Oil Pipeline Spill Detection Approach

The existing leak detection methods of submarine oil pipelines mainly rely on compositing synthetic aperture radar (SAR) satellite images to detect oil spills on the sea surface or installing underwater cameras to monitor the joints of submarine pipelines manually. The former is poor in timeliness and high in cost, while the latter is inflexible. Aiming at this, this paper studies the underwater image processing method of intelligent oil spill recognition using an underwater camera. Nevertheless, this method is currently faced with challenges such as poor quality of datasets, unclear goals, and scarce quantity. The U-Net++ algorithm can theoretically reduce the dependence on the number of datasets. However, the pixel feature extraction method used by U-Net++ is difficult to achieve the segmentation effect of oil spill images, so a new network architecture SSWT (Suitable Superpixel With Texture)-UNet++ based on texture superpixel is proposed. In SSWT-UNet++, the input is the oil spill image with texture superpixel division, and the output was changed to the middle layer. In addition, the resolution of each layer and the ratio of up/down sampling were adjusted. This algorithm completes the classification of oil spill texture superpixels and finally realizes oil spill detection according to the minimum bounding rectangle of the classification result of the oil spill region. Experimental verification shows that the final detection effect of the proposed algorithm is higher than that of other algorithms, with an average precision of 0.985.

MAPP: An efficient multi-location task allocation framework with personalized location privacy-protecting in spatial crowdsourcing

Due to its wide coverage and strong scalability, spatial crowdsourcing (SC) has become a research hotspot in recent years. In order to assign tasks to closer workers, it is necessary for workers to provide accurate locations to the server. However, it will result in the leakage of the participants’ location privacy. Existing works provide each worker with the same level of location privacy protection, which cannot meet the different privacy requirements of various workers. In addition, most works assume that the tasks are single-location tasks, and do not consider multi-location tasks. In this paper, we propose the Multi-location Task Allocation Problem with personalized location privacy protection (MLTAP). As far as we know, we are the first to study MLTAP. We propose a Multi-location task Allocation framework with Personalized location Privacy-protecting (MAPP). In order to allocate tasks efficiently, we use the R-tree to store workers and minimum bounding rectangle to represent multi-location tasks, thus filtering the unreachable workers for tasks. To better eliminate the adverse effect of location confusion, the SC server sorts candidate workers by the ranking metrics and allocates multi-location tasks efficiently. Finally, we conduct experiments to verify that MAPP has good performance in terms of utility.

Fast Shape Recognition Method Using Feature Richness Based on the Walking Minimum Bounding Rectangle over an Occluded Remote Sensing Target

Remote sensing target recognition has always been an important topic of image analysis, which has significant practical value in computer vision. However, remote sensing targets may be largely occluded by obstacles due to the long acquisition distance, which greatly increases the difficulty of recognition. Shape, as an important feature of a remote sensing target, plays an important role in remote sensing target recognition. In this paper, an occluded shape recognition method based on the local contour strong feature richness (contour pixel richness, contour orientation richness, and contour distance richness) to the walking minimum bounding rectangle (MBR) is proposed for the occluded remote sensing target (FEW). The method first obtains the local contour feature richness by using the walking MBR; it is a simple constant vector, which greatly reduces the cost of feature matching and increases the speed of recognition. In addition, this paper introduces the new concept of strong feature richness and uses the new strategy of constraint reduction to reduce the complex structure of shape features, which also speeds up the recognition speed. Validation on a self-built remote sensing target shape dataset and three general shape datasets demonstrate the sophisticated performance of the proposed method. FEW in this paper has both higher recognition accuracy and extremely fast recognition speed (less than 1 ms), which lays a more powerful theoretical support for the recognition of occluded remote sensing targets.

Interior Distance Ratio to a Regular Shape for Fast Shape Recognition

A fast shape recognition method based on regular graphic is proposed in this paper. It is the Interior Distance Ratio to a regular Shape (minimum bounding rectangle (MBR) or minimum circumscribed circle (MCC)) (SIDR). Regular shapes themselves have either axisymmetric or origin symmetry, which gives them regularity. Shape, as a feature of an object, plays a significant role in computer vision and image analysis. The shape descriptor is widely used to compute remarkable features of the visual image, especially in image understanding and analysis. SIDR is a new remarkable feature of the shape, which is the distribution of the interior distance between the shape contour points and its minimum bounding rectangle or minimum circumscribed circle. It can provide more effective performance support for practical application fields of computer vision, such as object detection and recognition. The minimum bounding rectangle or minimum circumscribed circle can change according to the change in a shape’s position, scale and direction, which is extremely suitable for describing a shape that has deformation. In addition, the rectangularity and circularity derived from them also have the potential peculiarity to describe the shape feature. Therefore, this paper uses the interior distance ratio of the shape to represent the shape feature. First, the minimum bounding rectangle or minimum circumscribed circle of the shape is selected according to the rectangularity and circularity of the shape. Then, the interior distance proportional distribution from the shape contour point to the minimum bounding rectangle or minimum circumscribed circle is obtained. Finally, a histogram is used to represent the distribution feature, and shape matching and recognition are carried out. A self-built dataset and three international generic datasets are used to verify the validity of the method. The performance exhibits the sophisticated property (accuracy and matching speed) of the proposed method. It is worth mentioning that this simple method has a recognition rate of close to 100% on the self-built dataset and has achieved excellent results for other datasets compared with some international state-of-the-art methods.

Variable-Scale Visualization of High-Density Polygonal Buildings on a Tile Map

To better satisfy user’s needs for the accurate visualization of massive amounts of geographic data, the variable-scale expression of map content based on multilevel data organization has attracted increasing attention. Traditional methods based on vector data usually cannot handle tile data in the form of a grid on the network. Therefore, this paper proposes a variable-scale visualization method for high-density buildings based on a raster tile map. First, the buildings on a tile map are typified on the basis of linear spectral clustering (LSC) superpixel segmentation to reduce the number of buildings. Then, the shapes of buildings are simplified using the minimum bounding rectangle method. Lastly, the designed focus + glue + context (F + G + C) variable-scale model is used for visual output. The OpenStreetMap tile data are used to perform experiments. Compared with traditional methods, the proposed variable-scale visualization method in this paper considers the spatial distribution, quantity, and shape characteristics of buildings, reduces the clutter of data, and has a better (average value of building quantity, area and density is 57%) visual effect. Variable-scale visualization can be applied to unstructured map data sources and extended to grid data sources to improve the readability and recognizability of high-density buildings.

A Continuous Region-Based Skyline Computation for a Group of Mobile Users

Skyline queries, which are based on the concept of Pareto dominance, filter the objects from a potentially large multi-dimensional collection of objects by keeping the best, most favoured objects in satisfying the user′s preferences. With today′s advancement of technology, ad hoc meetings or impromptu gatherings involving a group of people are becoming more and more common. Intuitively, deciding on an optimal meeting point is not a straightforward task especially when conflicting criteria are involved and the number of criteria to be considered is vast. Moreover, a point that is near to a user might not meet all the various users′ preferences, while a point that meets most of the users′ preferences might be located far away from these users. The task becomes more complicated when these users are on the move. In this paper, we present the Region-based Skyline for a Group of Mobile Users (RSGMU) method, which aims to resolve the problem of continuously finding the optimal meeting points, herein called skyline objects, for a group of users while they are on the move. RSGMU assumes a centroid-based movement where users are assumed to be moving towards a centroid that is identified based on the current locations of each user in the group. Meanwhile, to limit the searching space in identifying the objects of interest, a search region is constructed. However, the changes in the users′ locations caused the search region of the group to be reconstructed. Unlike the existing methods that require users to frequently report their latest locations, RSGMU utilises a dynamic motion formula, which abides to the laws of classical physics that are fundamentally symmetrical with respect to time, in order to predict the locations of the users at a specified time interval. As a result, the skyline objects are continuously updated, and the ideal meeting points can be decided upon ahead of time. Hence, the users′ locations as well as the spatial and non-spatial attributes of the objects are used as the skyline evaluation criteria. Meanwhile, to avoid re-computation of skylines at each time interval, the objects of interest within a Single Minimum Bounding Rectangle that is formed based on the current search region are organized in a Kd-tree data structure. Several experiments have been conducted and the results show that our proposed method outperforms the previous work with respect to CPU time.

Stray Light Nonuniform Background Elimination Method Based on Image Block Self-Adaptive Gray-Scale Morphology for Wide-Field Surveillance

Space-based wide-field surveillance systems are of great significance in maintaining the security of space resources by avoiding collisions between space targets. However, their performance is hindered by stray light phenomena. The nonuniform background noise caused by stray light significantly hampers subsequent target detection, leading to a high frequency of false alarms. To solve this problem, we propose a robust and accurate nonuniform background elimination method based on image block self-adaptive gray-scale morphology (IBSGM). First, we define two kinds of structural operators with different sizes and domains, which make full use of the difference between the target pixels and surrounding background pixels. Then, we block the original surveillance image and find the size of the largest target in each block by the minimum bounding rectangle method to determine the optimal size of the structural operator suitable for each block. Finally, we perform morphological processing using the defined structural operators to eliminate nonuniform backgrounds from images. Experimental results on simulated and real image datasets demonstrate that the proposed IBSGM method has higher precision in eliminating the nonuniform background when compared to other methods.

VesNet-RL: Simulation-Based Reinforcement Learning for Real-World US Probe Navigation

Ultrasound (US) is one of the most common medical imaging modalities since it is radiation-free, low-cost, and real-time. In freehand US examinations, sonographers often navigate a US probe to visualize standard examination planes with rich diagnostic information. However, reproducibility and stability of the resulting images often suffer from intra- and inter-operator variation. Reinforcement learning (RL), as an interaction-based learning method, has demonstrated its effectiveness in visual navigating tasks; however, RL is limited in terms of generalization. To address this challenge, we propose a simulation-based RL framework for real-world navigation of US probes towards the standard longitudinal views of vessels. A UNet is used to provide binary masks from US images; thereby, the RL agent trained on simulated binary vessel images can be applied in real scenarios without further training. To accurately characterize actual states, a multi-modality state representation structure is introduced to facilitate the understanding of environments. Moreover, considering the characteristics of vessels, a novel standard view recognition approach based on the minimum bounding rectangle is proposed to terminate the searching process. To evaluate the effectiveness of the proposed method, the trained policy is validated virtually on 3D volumes of a volunteer’s in-vivo carotid artery, and physically on custom-designed gel phantoms using robotic US. The results demonstrate that proposed approach can effectively and accurately navigate the probe towards the longitudinal view of vessels.

Oriented object detection in aerial images based on area ratio of parallelogram

Oriented object detection is a challenging task in aerial images since the objects in aerial images are displayed in arbitrary directions and are frequently densely packed. The mainstream detectors describe rotating objects using a five-parameter or eight-parameter representations, which have representation ambiguity for orientated object definition and border loss discontinuity in the regression process. We proposed an innovative representation method based on area ratio of parallelogram, called area ratio of parallelogram (ARP). Specifically, ARP regresses the minimum bounding rectangle of the oriented object and three area ratios. Three area ratios include the area ratio of a directed object to the smallest circumscribed rectangle and two parallelograms to the minimum circumscribed rectangle. It simplifies offset learning and eliminates the issue of angular periodicity or label point sequences for oriented objects. To further remedy the confusion issue of nearly horizontal objects, the area ratio between the object and its minimal circumscribed rectangle is employed to guide the selection of horizontal or oriented detection for each object. Moreover, the rotated efficient intersection over union loss with horizontal bounding box and three area ratios are designed to optimize the bounding box regression for rotating objects. Experimental results on remote sensing datasets, including HRSC2016, DOTA, and UCAS-AOD, show that the proposed method achieves superior detection performance than many state-of-the-art approaches.

A Study On Effects of Flaw Shape Idealization On the Interaction of Co-Planar Surface Flaws Subjected to Tension Load

Abstract Engineering Critical Assessment (ECA) guidelines contain rules to assess flaw interaction. Major flaw dimensions (depth or height and length) are typically characterized assuming the flaws to be contained entirely within a bounding rectangle through a procedure known as flaw idealization. In fracture mechanics based calculations, flaws are often assumed to be (semi-)elliptical when evaluating possible interaction. This paper investigates the implication of this simplification for the specific case of two identical co-planar surface breaking flaws. Two flaw shapes are considered and compared: semi-elliptical and canoe-shaped (quarter-circular ends with constant depth elsewhere). Especially for long and shallow flaws, the canoe-shaped configuration best approximates the bounding rectangle, whereas the semi-elliptical shape only touches the bounding rectangle at three points (deepest point and two points at the surface). Several flaw dimensions and spacing distances are studied through an extensive parametric study comprising both linear elastic and elastic-plastic finite element simulations. The results, evaluated in terms of stress intensity factor (SIF) and J-integral, show that the flaw shape idealization, particularly for long and shallow flaws, can significantly affect the degree of interaction between identical co-planar flaws. The inconsistency between semi-elliptical and canoe-shaped flaw shapes is observed in a linear elastic analysis and becomes more pronounced at higher loading levels evaluated in elastic-plastic analyses