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X-ray computed tomography (CT) provides a three-dimensional (3D) volume image of the scanned object and can be applied for nondestructive inspection of industrial materials. The object surface extracted from the X-ray CT volume is examined by comparing it with the pre-obtained computer-aided design (CAD) mesh model of the scanned object. However, industrial materials often contain heavy metals that significantly attenuate X-rays and cause CT artifacts in CT images, preventing an accurate shape extraction. This study proposes a method that extracts the geometric features of two-dimensional (2D) X-ray projection images and matches them with pre-obtained 3D CAD meshes. The proposed method is based on the principle that most of the geometric features of the object, such as edges, in the X-ray 2D projection images are captured and can be directly compared with CAD meshes. We first extract the features in each of the 2D projection images as X-ray feature projections. Subsequently, CAD mesh contours are projected onto 2D mesh feature projections and matched with the X-ray feature projections. Finally, 3D feature shapes of the CAD mesh are obtained by back-projecting the matching results. The proposed method requires only a small number of projection images, thus reducing computational time while maintaining shape accuracy. • A new shape inspection method which use X-ray projections is proposed. • The proposed method extracts and evaluate shape features of the CAD model. • The accuracy of the method is analyzed by simulations and real X-ray scanning data. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to reduce CT (computed tomography) artifacts by using multiple X-ray CT scanning in which CT data fusion is combined with optimized selection of a combination of scan angles. Industrial X-ray CT has been widely used for investigating industrial parts including metals, which cause various CT artifacts. These CT artifacts reduce the quality of X-ray CT images, and prevent precise measurements. Our multiple X-ray CT scanning approach focuses on CT artifacts distribution estimation, which including scan angle combination optimization, multiple CT volume registration, and CT volume fusion. To demonstrate our method, we selected multiple optimal scan angles and conducted volume fusion on industrial parts included metals and resins. Our approach effectively reduced CT artifacts. Our experiments also showed that additional scans successively reduced metal artifacts in various regions, suggesting that our scan angle selection method successfully selected an optimal combination of scan angles. ∙ This paper proposes metal artifacts reduction method using multiple X-ray CT scanning. ∙CT artifacts distribution estimation enable us to find optimal combination of scan angles. ∙Multiple CT volumes are used to obtain optimal CT volumes. ∙Pros, and cons, of the method are presented and discussed. [ABSTRACT FROM AUTHOR]

Using a 3D scanner to inspect a 3D print is becoming more significant as the spectrum of applications for fused filament fabrication expands. We use the tool path, which describes the 3D printing process, as the nominal data to evaluate the manufacturing errors. However, since the 3D print's coordinate system in the scan data is unknown, the 3D print must be aligned with the tool path before the nominal-actual comparison. Therefore, we present a novel alignment based on the 3D print's microstructure. The proposed method first estimates the build direction and then aligns the 3D print with the tool path along the build direction. Finally, each layer of the 3D print is aligned with its corresponding layer of the tool path in the horizontal direction. The alignment result can be confirmed through the slice view. In addition, the performance, extension, and applications of the proposed method are discussed. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (CT) enables nondestructive evaluation and dimensional metrology using the reconstructed volumes of measured objects. Although the abundant applications of industrial X-ray CT exist, a high-quality reconstructed volume requires a long measurement time owing to sharp transmission images from dense views. Intensive X-rays with the exposure kept can be used to reduce the measurement time. However, the quality of the measured objects inevitably deteriorates as a result of the so-called penumbra effect caused by large focal spots in X-ray sources. This paper proposes rotational fine-tuning (RFT) for the acceleration of CT scanning with the quality kept. First, sharp transmission images from sparse views are obtained in appropriate imaging conditions. Subsequently, blurry transmission images from dense views are obtained using a larger focal spot size and a shorter exposure time. The acquired blurry images are deblurred by convolutional neural networks fine-tuned using several pairs of sharp and blurry images obtained at the corresponding projection angles, and linear interpolation integrates the deblurred images to generate the final RFT output. The proposed method indirectly reduces the measurement time because no lengthy acquisition time is required, and fine-tuning and blurring are rapid when using GPUs. In the experiments, the comparison of PSNRs between blurry and deblurred reconstructed volumes is shown. To reveal the effect of the proposed method on dimensional metrology, the improvement in the surface deviations of the stepped cylinder is also shown. Finally, the improvement of the pore spaces in a porous aluminum is shown to assess the aspect of nondestructive evaluation. Overall, experimental results demonstrate that the proposed method can perform the acceleration of X-ray CT scanning with the quality kept. • A new approach for the acceleration of high-quality CT scanning using machine learning. • Deblur the blurry X-ray transmission images with convolutional neural networks. • Empirical validation of the applicability of the proposed method for various materials. [ABSTRACT FROM AUTHOR]

Spectral X-ray computed tomography (SXCT) is able to provide richer spectral information than conventional X-ray CT, thereby improving the capability of discriminating materials in segmentation tasks. However, segmentation algorithms for conventional CT images often lose their performance in material segmentation with SXCT images due to the spectrum-dependent attenuation properties of X-rays. Recently, deep neural networks (DNNs) have demonstrated their effectiveness in segmentation tasks. However, processing SXCT with DNNs requires significant memory resources and may take significant computation time. To address these issues, this paper introduces a simulation study towards realising deep-learning-based SXCT image segmentation. The proposed method employs a convolutional neural network (CNN) that selectively utilises the significant spectrum channels for accurate material segmentation. The proposed method builds upon a prior study on adaptive CT reconstruction, but to further enhance the performance, we introduce a self-attention-based spectrum-selection module. During training, the selection module assigns importance weights to spectrum channels based on the attention scores. Once the network has been trained, the proposed method can obtain satisfactory segmentation results from projection images at one or a combination of several spectrum channels recommended by the selection module. The experimental results demonstrate that the proposed method achieves a significant reduction in computation time while maintaining the segmentation quality. [ABSTRACT FROM AUTHOR]

X-ray cone-beam computed tomography (CBCT) is a powerful tool for nondestructive testing and evaluation, yet the CT image quality can be compromised by artifact due to X-ray scattering within dense materials such as metals. This problem leads to the need for hardware- and software-based scatter artifact correction to enhance the image quality. Recently, deep learning techniques have merged as a promising approach to obtain scatter-free images efficiently. However, these deep learning techniques rely heavily on training data, often gathered through simulation. Simulated CT images, unfortunately, do not accurately reproduce the real properties of objects, and physically accurate X-ray simulation still requires significant computation time, hindering the collection of a large number of CT images. To address these problems, we propose a deep learning framework for scatter artifact correction using projections obtained solely by real CT scanning. To this end, we utilize a beam-hole array (BHA) to block the X-rays deviating from the primary beam path, thereby capturing scatter-free X-ray intensity at certain detector pixels. As the BHA shadows a large portion of detector pixels, we incorporate several regularization losses to enhance the training process. Furthermore, we introduce radiographic data augmentation to mitigate the need for long scanning time, which is a concern as CT devices equipped with BHA require two series of CT scans. Experimental validation showed that the proposed framework outperforms a baseline method that learns simulated projections where the entire image is visible and does not contain scattering artifacts. [ABSTRACT FROM AUTHOR]

Abstract The analysis of existing objects for developing new products is called reverse engineering and is a common process in industrial manufacturing. X-ray CT scanning is a powerful reverse-engineering tool that can acquire the entire geometry of an assembled object, including its inner structure, without disassembly. Before X-ray CT scanning can be used to accurately extract the parts of an assembled object from its CT volume, several challenges remain: CT artifacts, the non-uniqueness of the CT values, and limited resolution. Currently, an operator usually manually segments the target parts of the CT volume at voxel-level accuracy, which takes a long time. Hence, this paper proposes SegMo, a segmentation system which can perform segmentation of an assembled object to sub-voxel accuracy more quickly and with less effort than conventional methods. These advantages are achieved by suggesting the part boundary candidates that are coded as a hierarchical decomposition of the CT volume based on a Morse complex and polygonization at sub-voxel accuracy. SegMo consists of three steps: the generation of a hierarchical decomposition of a CT volume, segmentation using this hierarchy, and polygonization of the segmentation result. In this paper, we demonstrate how SegMo can achieve accurate and efficient segmentation on CT volumes of several complex assembled objects. Highlights • Practical segmentation system for X-ray CT volumes of assembles is proposed. • Accurate surface mesh of each segmented part can be obtained. • GUI assisting the efficiency of manual operation is proposed. • Examples of segmentation with CT volumes of complex object are shown. [ABSTRACT FROM AUTHOR]

The Feldkamp, Davis and Kress algorithm is a computationally efficient reconstruction method for three-dimensional cone-beam computed tomography. However, it suffers from severe artefacts when the number of projections is insufficient. Although recent deep learning–based methods have succeeded in reconstructing such sparse-view projections, it is still challenging to reconstruct a large 3D volume efficiently because of heavy memory consumption and difficulty in obtaining sufficient training data. Therefore, we propose a deep learning method to overcome these drawbacks. Our method consecutively reconstructs short bars in 3D CT volume using the intensities of pixels on the moving trajectory projected on the detector of these bars. Then, we feed such pixel intensities into a neural network and train it to simulate the filtering and back-projection processes of the FDK algorithm. Since the reconstruction volume is separated into bars, the neural network can be used with only a small amount of memory. Furthermore, the network can be trained sufficiently with only a few training samples because plenty of bar data can be extracted even from a single CT image. We experimentally demonstrate that our approach works efficiently for both simulated and actual sparse-view CBCT data using training data extracted from only a single CT image. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel interpolation scheme for surface extraction from volume fraction data stored on an octree. Here the volume fraction data is a set of octants each of which stores the volume ratio of the object included in the octant. Based on marching cubes on the dual grid of an octree, we modified the computation of the mesh vertex positions to be more appropriate for volume fraction data. The key point of the proposed interpolation is that we approximate the shapes of the octants as spheres rather than as cubes. This approximation has a very simple computation and can improve the accuracy of the vertex positions. We demonstrate the effectiveness of the algorithm using octree-compressed CT volumes that can be treated as volume fraction data. [ABSTRACT FROM AUTHOR]

Fiber composite materials have unique, advantageous mechanical properties that have made them highly desirable in a range of industries. In particular, 3D woven-fiber composites are highly resistant to delamination compared with laminated 2D woven-fiber composites and have been adopted in various advanced products. This paper focuses on the design of 3D woven-fiber composite products and proposes a flattening simulation method for designed 3D models with constant thickness. The proposed method estimates the shape of a flat material and the fiber directions in the 3D model design; deformation phenomena of 3D woven-fiber materials are also considered in order to improve the accuracy of the proposed method. CT images are used to compare the simulation results with the actual deformation of 3D woven-fiber materials and confirm the ability of our method to effectively design the fiber direction base on the 3D model and to estimate the shape of flat materials. [ABSTRACT FROM AUTHOR]

Inspired by computed tomography (CT), this paper presents a novel surface reconstruction algorithm, tomographic surface reconstruction , to reconstruct a surface mesh from a point cloud equipped with oriented normals. In the process of scanning a real object using an X-ray CT system, it generates a sinogram consisting of projection images that are maps of X-ray transmission lengths, and then, a tomogram (CT volume) is reconstructed from the sinogram. A hole-free surface mesh is then easily obtained by polygonizing an isosurface. To adopt this CT paradigm to surface reconstruction from a point cloud, only a scheme to generate a sinogram from a point cloud is required. The value of a sinogram for surface reconstruction can be defined as the sum of the distances between the intersecting points of a ray and the underlying surface, which are defined as the maxima of the point density. While ordinary CT scanning uses projection directions which share a single rotation axis, tomographic surface reconstruction adopts randomly selected projection directions and successfully improved the reconstruction robustness. By applying an iterative CT reconstruction to the sinogram, the algorithm generates a tomogram whose boundary between the foreground and background approximates the surface of the object. The effectiveness for a point cloud with a lack of sampling and outliers is demonstrated from experimental results. [ABSTRACT FROM AUTHOR]

In industrial manufacturing, reverse engineering, which can be applied to analyze or re-design, is an essential process, whereby scanned data of real objects are used to generate a CAD model. CAD data have various features such as fillets; therefore, robust and versatile reverse engineering is still difficult. Particularly, an adequate segmentation from real scanned data is challenging, and it is a major obstacle for reverse engineering. In this study, we propose an innovative segmentation from CT scanned data, wherein user-friendly segmentation is achieved by directly computing the curvature gradient from a CT sinogram. We used the proposed method to patch a B-spline surface onto each segmented region. The experimental results show that the proposed method offers robust, versatile, and user-friendly reverse engineering that is faster than conventional manual modeling. In addition, as it is supported by X-ray CT simulation, the proposed method can be applied to various surface mesh data without requiring any actual X-ray CT equipment. • Robust curvature gradients are computed directly from CT sinogram. • Flexible and powerful segmentation is performed using multi-level Morse complex. • B-spline surfaces are patched onto each segmented region as a demonstration. • X-ray CT simulations are conducted and used for the proposed method. [ABSTRACT FROM AUTHOR]

With a different speed function, Level Set Method has been widely applied to many applications. Generally speaking, speed function may depend on many factors, such as curvature, normal direction. In this paper, we discuss a novel speed function which is only determined by neighbors' support. With enough support, zero level set can move or stop. Otherwise, it must wait for a moment before a decision to move or stop is made. In addition, an algorithm based on normal diffusion is proposed to smooth the zero level set, which can preserve the sharp feature and round corner at the same time. Experimentally, the proposed method has been successfully used for interested objection segmentation and mesh segmentation. [ABSTRACT FROM AUTHOR]

Abstract: We introduce a method for segmentation of materials segmented in volumetric models of mechanical parts created by X-ray CT scanning for the purpose of generating their boundary surfaces. When the volumetric model is composed of two materials, one for the object and the other for the background (Air), these boundary surfaces can be extracted as isosurfaces using a surface contouring method. For a volumetric model composed of more than two materials, we need to classify the voxel types into segments by material and then use a surface contouring method that can deal with both CT values and material types. Here we propose a method for precisely classifying the volumetric model into its component materials using a modified and combined method of two well-known algorithms in image segmentation, region growing and Graph-cut. We then apply our non-manifold iso-contouring method to generate triangulated mesh surfaces. In addition, we demonstrate the effectiveness of our method by constructing high-quality triangular mesh models of the segmented parts. [Copyright &y& Elsevier]

We propose a novel method for smoothing partition of unity (PU) implicit surfaces consisting of sets of non-conforming linear functions with spherical supports. We derive new discrete differential operators and Laplacian smoothing using a spherical covering of PU as a grid-like data structure. These new differential operators are applied to the smoothing of PU implicit surfaces. First, Laplacian smoothing is performed for the vector field defined by the gradient of the PU implicit surface, which is then updated to reflect the smoothing of the gradient field. This process achieves a method for noise robust surface reconstruction from scattered points. [ABSTRACT FROM AUTHOR]

The level set method (LSM) is used in a wide variety of applications. In this paper, the authors propose an approach to detect and eliminate protruding and hollow features in triangular meshes using the technique. First, uniform grid points are generated based on a given mesh model, and these points are then clustered with respect to a user-defined threshold using LSM. Finally, the mesh model is segmented in consideration of the grouped grid points. The results of experiments performed using the proposed method indicate its suitability for the purpose at hand. [ABSTRACT FROM AUTHOR]

Conventionally, ultrasound (US) diagnosis is performed using hand-held rigid probes. Such devices are difficult to be used for long-term monitoring because they need to be continuously pressed against the body to remove the air between the probe and body. Flexible probes, which can deform and effectively adhere to the body, are a promising technology for long-term monitoring applications. However, owing to the flexible element array geometry, the reconstructed image becomes blurred and distorted. In this study, we propose a flexible probe U.S. imaging method based on element array geometry estimation from radio frequency (RF) data using a deep neural network (DNN). The input and output of the DNN are the RF data and parameters that determine the element array geometry, respectively. The DNN was first trained from scratch with simulation data and then fine-tuned with in vivo data. The DNN performance was evaluated according to the element position mean absolute error (MAE) and the reconstructed image quality. The reconstructed image quality was evaluated with peak-signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM). In the test conducted with simulation data, the average element position MAE was 0.86 mm, and the average reconstructed image PSNR and MSSIM were 20.6 and 0.791, respectively. In the test conducted with in vivo data, the average element position MAE was 1.11 mm, and the average reconstructed image PSNR and MSSIM were 19.4 and 0.798, respectively. The average estimation time was 0.045 s. These results demonstrate the feasibility of the proposed method for long-term real-time monitoring using flexible probes. [ABSTRACT FROM AUTHOR]

Visualising the complex assembly in a large CT volume is a difficult task due to the existence of components made of materials with similar X-ray attenuation factors. To resolve the difficulty, we propose a degree of local symmetry (DLS), a new voxel attribute to visualise specific parts based on their local geometric features. The DLS represents a dimensionality of the manifold around a voxel (e.g. the DLS for a local surface will be 2). For a given CT volume, we first extract an isosurface of the target assembly imaged by an X-ray CT device and compute the medial axes of the isosurface. We then compute the DLS values for voxels on the medial axes. Finally, the DLS values are propagated from the voxels on the medial axes to the entire voxels of the input volume. We demonstrate that the DLS works well to selectively visualise the parts in various assemblies, including a simple artificial assembly of several primitive shapes and more complicated vehicle models. [ABSTRACT FROM AUTHOR]

All terrain cranes often work in construction sites. Blind spots, limited information, and high mental workload are problems encountered by crane operators. A top-view camera mounted on the boom head offers a valuable perspective on the workspace that can help eliminate blind spots and provide the basis for assisting operation. In this study, a visual 2D map of a crane workspace is generated from images captured by a top-view camera. Various types of information can be overlaid on this visual 2D map to assist the operator, such as recording the operation and projecting the boom head's expected path through the workspace. Herein, the process of generating a visual map by stitching and locating the boom head trajectory in that visual map is described. Preliminary proof-of-concept tests show that a precise map and projected trajectories can be generated via image-processing techniques that discriminate foreground objects from the scene below the crane. The location error is analyzed and verified to confirm its applicability. These results show a way to help the operator make more precise operation easily and reduce the operator's mental workload. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) technology is a promising tool for field service applications such as assembly. AR can show computer-aided design (CAD) models of assembly parts at installation locations. This provides a means for delivering assembly instructions to an operator. Furthermore, evaluating the assembly process can be beneficial for preventing assembly errors. Field service engineers who perform assembly operations may choose to use a head-mounted display (HMD) for its good portability and hands-free operation. In this study, we propose a method for efficiently evaluating the misalignment between real and virtual objects, which is a mismatch between the two objects' poses, in an HMD-based AR assembly assistance system. By using a depth camera in the HMD, we evaluate the misalignment by comparing the depth maps of the real and virtual worlds. With our preliminary prototype, the system can evaluate misalignment with an accuracy of ±1 cm at a rate of 30 fps in real time. Moreover, we also propose a coordinate calibration method and establish a basic HMD-based AR assembly assistance system to demonstrate the methods. The efficient evaluation of misalignment could help to monitor operations to prevent assembly errors and decrease operation time. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for improvement of isosurface polygonizations. Given an initial polygonization of an isosurface, we introduce a mesh evolution process initialized by the polygonization. The evolving mesh converges quickly to its limit mesh which provides with a high quality approximation of the isosurface even if the isosurface has sharp features, boundary, complex topology. To analyze how close the evolving mesh approaches its destined isosurface, we introduce error estimators measuring the deviations of the mesh vertices from the isosurface and mesh normals from the isosurface normals. A new technique for mesh editing with isosurfaces is also proposed. In particular, it can be used for creating carving effects. [ABSTRACT FROM AUTHOR]

Measurement quality of industrial cone beam X-ray computed tomography (CT) is influenced by many types of artefacts, therefore, industrial-level accuracy is difficult to attain. In order to avoid the influences of these artefacts, this paper proposes a new method for measuring cylindrical surface that is a common geometric structure in mechanical parts. The proposed method fits cylindrical surface from a sinogram image directly. Compared with a standard way for dimensional CT metrology, higher measurement accuracy and less measurement time can be achieved. Mainly, the method consists of edge points detection of a sinogram image and cylindrical surface fitting. The performance of the method is evaluated by use of both simulation data and actual data. [ABSTRACT FROM AUTHOR]

Due to the complex working environment, limited vision, and complicated crane operations, the crane operator faces two major problems when constructing with a crane: safety and working efficiency. Every year, numerous accidents occur, the majority of which are the result of the crane operator's inability to acknowledge hidden hazards. Some of these accidents can be effectively avoided through a comprehensive and intuitive display of important visual information to the crane operator. In this research, one of the most important safety-related visual information, namely the working area limit line, is provided on the acquisition of a crane's 3D spatial map based on a top-view camera. The purpose is achieved firstly by a real-time scene reconstruction of a visual simultaneously location and mapping approach semi-direct visual odometry and regularized monocular depth estimation. Then the working area limit line is shown on the top-view image according to the reconstructed 3D spatial map. The working area limit line can be successfully and precisely shown in the top-view image in both simulation and field experiments. The augmented top-view image with a working area limit line can help the crane operator notice the hidden hazards of outreaching the crane's working limit and improve the operation efficiency. The hazard of crane overturn can be effectively avoided with the displayed result. Based on the proposed approach, much more safety and operation related visual information can be displayed according to the 3D spatial map and classified operation stages in our future work. • A novel two-stage system is proposed to visually assist crane operation. • In the pre-processing stage, the system performs a precise, dense, and real-time 3D reconstruction. • In the operation stage, the system generates accurate working area limit lines on the top-view images. • A variety of simulations and field experiments are conducted to evaluate the system. • Future directions on displaying much more safety- and operation-related information are figured out. [ABSTRACT FROM AUTHOR]

Among a wide range of fiber-reinforced composites, those with randomly oriented short fibers, which are also known as random-chopped fiber-reinforced composites (RaFCs), are the most common composites owing to its ease of manufacturing, flexibility of composite shapes, and good material properties, including light weight and high stiffness. These properties of RaFCs are involved with the lengths and distributions of fibers inside the composites. However, inspecting the fiber lengths and distribution remains a challenging problem, particularly when the lengths and locations of individual fibers need to be distinguished using only X-ray transmission images. The main difficulty arises from the variety of fiber widths and their frequent intersections. To address this problem, this paper proposes a comprehensive software system to localize fibers and measure their lengths. Our system is inspired by a previous work for tracing human hair strands. To adopt the previous method for RaFCs, our system extends classic Gabor filter to explore the locally best parameter sets to suit different fiber shapes. With this adaptive filter, we can extract the locations and orientations of local fibers more robustly for RaFCs. Then individual fibers are traced by solving an initial value problem of an ordinary differential equation. To avoid erroneous tracing which typically occurs at intersections, our method traces only the non-intersecting parts of the fibers initially. After that, we connect the fiber segments using the proximity of their endpoints and the orientations. Through experimental validations on different fiber samples, we demonstrate the stability of the fiber tracing and the robustness of the fiber length calculation. Our system works properly even for X-ray radiographic images of heavily tangled fibers in carbon-fiber-reinforced thermoplastic laminates taken by X-ray Talbot–Lau interferometer. [ABSTRACT FROM AUTHOR]

Recently, to meet the requirement of machine industry, there has been an increased focus on the development of porous metal as a high-strength material despite its low density. To evaluate its properties and quality, material testing is usually conducted. However, it is more efficient to perform computer simulation evaluations using finite element analysis. The X-ray computed-tomography scanning technique enables us to obtain the information regarding the internal structure of the metal. Furthermore, a reconstruction algorithm produces volume data of the test object. In general, conventional methods are utilized to generate mesh data from volume data for finite element analysis, but a key drawback is that they generate too many elements, resulting in high computational cost. We propose an approach to generate meshes for porous structures by modeling each pore using spheres from volume data. [ABSTRACT FROM AUTHOR]

Abstract: Thanks to recent improvements, computational methods can now be used to convert triangular meshes into quadrilateral meshes so that the quadrilateral elements capture well the principal curvature directional fields of surfaces and intrinsically have surface parametric values. In this study, a quadrilateral mesh generated using the mixed integer quadrangulation technique of Bommes et al. is used for input. We first segment a quadrilateral mesh into four-sided patches. The feature curves inside these patches are then detected and are constrained to act as the patch boundaries. Finally, the patch configuration is improved to generate large patches. The proposed method produces bi-monotone patches, which are appropriate for use in reverse engineering to capture the surface details of an object. A shape control parameter that can be adjusted by the user during the patch generation process is also provided to support the creation of patches with good bi-monotone shapes. This study mainly targets shape models of mechanical parts consisting of major smooth surfaces with feature curves between them. [Copyright &y& Elsevier]

Abstract: Today’s quad-meshing techniques generate high-quality quadrilateral meshes whose extraordinary vertices (i.e., not four-valence vertices except on the boundary) are generally located in highly curved regions. The motorcycle graph (MCG) algorithm of Eppstein et al. can be used to generate structured partitions of such quadrilateral meshes. However, it is not always possible for it to capture feature curves in the highly-curved parts of the model on the partition boundaries because model geometry is not taken into account. This study investigated feature-aware algorithms representing extensions of the MCG algorithm. Initial partitioning is first performed using a speed control algorithm identical to the MCG algorithm except that it assigns variable rather than constant speed to particles. Partition boundaries are then improved via local path flipping operations. The MCG algorithm and the speed control algorithm are intended to trace as many feature curves as possible, but do not necessarily trace all of them. For this reason, feature curves are extracted and integrated into the proposed framework by adding seeds located at ordinary vertices in addition to extraordinary seeds. The proposed algorithm generates partitions that are still structured, and has been tested with quadrilateral mesh models generated using the mixed integer quadrangulation technique of Bommes et al. [Copyright &y& Elsevier]

The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM), as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The method's implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery. [ABSTRACT FROM AUTHOR]