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We construct a novel descriptor called anisotropic wavelet energy decomposition descriptor (AWEDD) for non-rigid shape analysis, based on anisotropic diffusion geometry. We first extend the Dirichlet energy of the vertex coordinate function to an anisotropic version, then use multiscale anisotropic spectral manifold wavelets to decompose the Dirichlet energy to all vertices and collect local energy at each vertex to form AWEDD. AWEDD simultaneously encodes multiscale extrinsic and intrinsic shape features, which are more informative and robust than purely intrinsic or extrinsic descriptors. And the introduction of anisotropy endows AWEDD with stronger abilities of feature discrimination and intrinsic symmetry identification. Our results demonstrate that AWEDD is more discriminative than current state-of-the-art descriptors. In addition, we show that AWEDD is an excellent choice of the initial inputs for various shape analysis approaches, such as functional map pipelines and deep convolutional architectures. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Increasingly complex 3D CAD models are essential during different life-cycle stages of modern engineering projects. Even though these models contain several repeated geometries, instancing information is often not available, resulting in increased requirements for storage, transmission, and rendering. Previous research have successfully applied shape matching techniques to identify repeated geometries and thus reduce memory requirements and improve rendering performance. However, these approaches require consistent vertex topology, prior knowledge about the scene, and/or the laborious creation of labeled datasets. In this paper, we present an unsupervised deep-learning method that overcomes these limitations and is capable of identifying repeated geometries and computing their instancing transformations. The method also guarantees a maximum visual error and preserves intrinsic characteristics of surfaces. Results on real-world 3D CAD models demonstrate the effectiveness of our approach: the datasets are reduced by up to 83.93% in size. Our approach achieves better results than previous work that does not rely on supervised learning. The proposed method is applicable to any kind of 3D scene and geometry. [Display omitted] • Unsupervised deep learning method for 3D shape registration. • Does not require any previous knowledge of the 3D geometries. • Does not require a labeled dataset for any supervised training. • Guarantees an upper bound on any visual errors. • Generalizes for any 3D scene and geometry. [ABSTRACT FROM AUTHOR]

A jigsaw puzzle is a recreational activity that involves assembling a certain number of pieces into a combined and well-fitting unit without creating gaps between adjacent pieces. Two-dimensional puzzles are divided into two main categories, the "apictorial" in which the only information available is the shape of the pieces and the "pictorial" which may take into account not only the shape of the pieces, but also their content. Jigsaw puzzles are considered as one of the most popular category of puzzles. The majority of them are accompanied by a guiding image and there is only one "counterpart" for each side of each piece (pictorial jigsaw puzzles), although some more difficult variants have blank pieces, the so-called apictorial jigsaw puzzles. In this paper, we will examine the open problem of solving pictorial and apictorial jigsaw puzzles, and their various applications, such as the reconstruction of two-dimensional fragmented objects, the restoration of fragmented wall-paintings and the repair of shredded documents. We will also present an evaluation of the state-of-the- art jigsaw puzzle reassembly techniques in pictorial and apictorial puzzles. [ABSTRACT FROM AUTHOR]

Shape descriptors have recently gained popularity in shape matching, statistical shape modeling, etc. Their discriminative ability and efficiency play a decisive role in these tasks. In this paper, we first propose a novel handcrafted anisotropic spectral descriptor using Chebyshev polynomials, called the anisotropic Chebyshev descriptor (ACD); it can effectively capture shape features in multiple directions. The ACD inherits many good characteristics of spectral descriptors, such as being intrinsic, robust to changes in surface discretization, etc. Furthermore, due to the orthogonality of Chebyshev polynomials, the ACD is compact and can disambiguate intrinsic symmetry since several directions are considered. To improve the ACD's discrimination ability, we construct a Chebyshev spectral manifold convolutional neural network (CSMCNN) that optimizes the ACD and produces a learned ACD. Our experimental results show that the ACD outperforms existing state-of-the-art handcrafted descriptors. The combination of the ACD and the CSMCNN is better than other state-of-the-art learned descriptors in terms of discrimination, efficiency, and robustness to changes in shape resolution and discretization. [ABSTRACT FROM AUTHOR]

Abstract Shape descriptors have recently gained popularity in shape matching, statistical shape modeling, etc. Their discriminative ability and efficiency play a decisive role in these tasks. In this paper, we first propose a novel handcrafted anisotropic spectral descriptor using Chebyshev polynomials, called the anisotropic Chebyshev descriptor (ACD); it can effectively capture shape features in multiple directions. The ACD inherits many good characteristics of spectral descriptors, such as being intrinsic, robust to changes in surface discretization, etc. Furthermore, due to the orthogonality of Chebyshev polynomials, the ACD is compact and can disambiguate intrinsic symmetry since several directions are considered. To improve the ACD’s discrimination ability, we construct a Chebyshev spectral manifold convolutional neural network (CSMCNN) that optimizes the ACD and produces a learned ACD. Our experimental results show that the ACD outperforms existing state-of-the-art handcrafted descriptors. The combination of the ACD and the CSMCNN is better than other state-of-the-art learned descriptors in terms of discrimination, efficiency, and robustness to changes in shape resolution and discretization.

We propose a novel learning-based approach for robust 3D shape matching. Our method builds upon deep functional maps and can be trained in a fully unsupervised manner. Previous deep functional map methods mainly focus on predicting optimised functional maps alone, and then rely on off-the-shelf post-processing to obtain accurate point-wise maps during inference. However, this two-stage procedure for obtaining point-wise maps often yields sub-optimal performance. In contrast, building upon recent insights about the relation between functional maps and point-wise maps, we propose a novel unsupervised loss to couple the functional maps and point-wise maps, and thereby directly obtain point-wise maps without any post-processing. Our approach obtains accurate correspondences not only for near-isometric shapes, but also for more challenging non-isometric shapes and partial shapes, as well as shapes with different discretisation or topological noise. Using a total of nine diverse datasets, we extensively evaluate the performance and demonstrate that our method substantially outperforms previous state-of-the-art methods, even compared to recent supervised methods. Our code is available at https://github.com/dongliangcao/Unsupervised-Learning-of-Robust-Spectral-Shape-Matching. [ABSTRACT FROM AUTHOR]

A new algorithm is presented to compute nonrigid, possibly partial comparisons of shapes defined by unstructured triangulations of their surfaces. The algorithm takes as input a pair of surfaces with each surface given by a distinct and unrelated triangulation. Its goal is to define a possibly partial correspondence between the vertices of the two triangulations, with a cost associated with this correspondence that can serve as a measure of the similarity of the two shapes. To find this correspondence, the vertices in each triangulation are characterized by a signature vector of features. We tested both the LD-SIFT signatures, based on the concept of spin images, and the wave kernel signatures obtained by solving the Shrödinger equation on the triangulation. A cost matrix C is constructed such that C (k , l) is the norm of the difference of the signature vectors of vertices k and l. The correspondence between the triangulations is then computed as the transport plan that solves the optimal transport or optimal partial transport problem between their sets of vertices. We use a statistical physics approach to solve these problems. The presentation of the proposed algorithm is complemented with examples that illustrate its effectiveness and manageable computing cost. [ABSTRACT FROM AUTHOR]

Shape matching is a fundamental problem in computer graphics with many applications. Functional maps translate the point-wise shape-matching problem into its functional counterpart and have inspired numerous solutions over the last decade. Nearly all the solutions based on functional maps rely on the eigenfunctions of the Laplace–Beltrami Operator (LB) to describe the functional spaces defined on the surfaces and then convert the functional correspondences into point-wise correspondences. However, this final step is often error-prone and inaccurate in tiny regions and protrusions, where the energy of LB does not uniformly cover the surface. We propose a new functional basis Principal Components of a Dictionary (PC D) to address such intrinsic limitation. PC D constructs an orthonormal basis from the Principal Component Analysis (PCA) of a dictionary of functions defined over the shape. These dictionaries can target specific properties of the final basis, such as achieving an even spreading of energy. Our experimental evaluation compares seven different dictionaries on established benchmarks, showing that PC D is suited to target different shape-matching scenarios, resulting in more accurate point-wise maps than the LB basis when used in the same pipeline. This evidence provides a promising alternative for improving correspondence estimation, confirming the power and flexibility of functional maps. [Display omitted] • A procedure to adaptively define basis for functional spaces over discrete 3D meshes • A general method that applies to different redundant dictionaries of functions • Achieve state-of-the-art shape matching accuracy in a functional map framework • Disclose the impact of more even distribution of the energy of a basis in matching • Quantitative assessment of seven bases in comparison to existing alternatives [ABSTRACT FROM AUTHOR]

It has been well known for decades that prone positioning (PP) improves oxygenation. However, it has gained widespread acceptance only in the last few years since studies have shown significant survival benefit. Many centers have established prone ventilation in their treatment algorithm for mechanically ventilated patients with severe acute respiratory distress syndrome (ARDS). Physiologically, PP should also benefit awake, non-intubated patients with acute hypoxemic respiratory failure. However, proning in non-intubated (PINI) patients did not gain any momentum until a few months ago when the Coronavirus disease 2019 (COVID-19) pandemic surged. A large number of sick patients overwhelmed the health care system, and many centers faced a dearth of ventilators. In addition, outcomes of patients placed on mechanical ventilation because of COVID-19 infection have been highly variable and often dismal. Hence, increased focus has shifted to using various strategies to prevent intubation, such as PINI. There is accumulating evidence that PINI is a low-risk intervention that can be performed even outside intensive care unit with minimal assistance and may prevent intubation in certain patients with ARDS. It can also be performed safely at smaller centers and, therefore, may reduce the patient transfer to larger institutions that are overwhelmed in the current crisis. We present a case series of 2 patients with acute hypoxemic respiratory failure who experienced significant improvements in oxygenation with PP. In addition, the physiology of PP is described, and concerns such as proning in obese and patient's anxiety are addressed; an educational pamphlet that may be useful for both patients and health care providers is provided.

As a linear actuator, accurate dimension measurement is crucial to the transmission reliability and interchangeability of ball screws. However, most of the current approaches are ineligible for rapid ball screw in-situ inspections due to the installation condition requirement of the production line. In this research, a machine vision method is presented to achieve highly accurate measurements of crucial parameters (the center distance and raceway arcs) in ball screws using a curvature edge detection algorithm. To capture images of the immediate area surrounding the area of interest, a telecentric lens is used. Thereafter, the curvature-based edge detection algorithm is employed to extract the contours. The measurement location on the object is automatically chosen by using a shape-matching algorithm. Additionally, random noise is suppressed by using the multiple-measurement averaging technique. Based on the results of the experiments, it is concluded that the center distance and the two raceway arcs computed absolute errors are 0.0019 mm, 0.0055 mm, and 0.0059 mm, respectively.

An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67  mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.

We propose a principled approach for non‐isometric landmark‐preserving non‐rigid shape matching. Our method is based on the functional map framework, but rather than promoting isometries we focus on near‐conformal maps that preserve landmarks exactly. We achieve this, first, by introducing a novel landmark‐adapted basis using an intrinsic Dirichlet‐Steklov eigenproblem. Second, we establish the functional decomposition of conformal maps expressed in this basis. Finally, we formulate a conformally‐invariant energy that promotes high‐quality landmark‐preserving maps, and show how it can be optimized via a variant of the recently proposed ZoomOut method that we extend to our setting. Our method is descriptor‐free, efficient and robust to significant mesh variability. We evaluate our approach on a range of benchmark datasets and demonstrate state‐of‐the‐art performance on non‐isometric benchmarks and near state‐of‐the‐art performance on isometric ones. [ABSTRACT FROM AUTHOR]

A new algorithm is presented to compute nonrigid, possibly partial comparisons of shapes defined by unstructured triangulations of their surfaces. The algorithm takes as input a pair of surfaces with each surface given by a distinct and unrelated triangulation. Its goal is to define a possibly partial correspondence between the vertices of the two triangulations, with a cost associated with this correspondence that can serve as a measure of the similarity of the two shapes. To find this correspondence, the vertices in each triangulation are characterized by a signature vector of features. We tested both the LD-SIFT signatures, based on the concept of spin images, and the wave kernel signatures obtained by solving the Shrödinger equation on the triangulation. A cost matrix C is constructed such that C(k,l) is the norm of the difference of the signature vectors of vertices k and l. The correspondence between the triangulations is then computed as the transport plan that solves the optimal transport or optimal partial transport problem between their sets of vertices. We use a statistical physics approach to solve these problems. The presentation of the proposed algorithm is complemented with examples that illustrate its effectiveness and manageable computing cost.

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Two-dimensional (2D) irregular packing problems are widespread in manufacturing industries such as shipbuilding, metalworking, automotive production, aerospace, clothing and furniture manufacturing. Research on 2D irregular packing problems is essential for improving material utilization and industrial automation. Much research has been conducted on this problem with significant research results and certain algorithms. The work has made important contributions to solving practical problems. This paper reviews recent advances in the domain of 2D irregular packing problems based on a variety of research papers. We first introduce the basic concept and research background of 2D irregular packing problems and then summarize algorithms and strategies that have been proposed for the problems in recent years. Conclusion summarize development trends and research hotspots of typical 2D irregular shape packing problems. We hope that this review could provide guidance for researchers in the field of 2D irregular packing.

Salp swarm algorithm (SSA) is one of the efficient recent meta‐heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields. In the research process, it is found that it is very difficult to maintain the balance between the exploration and exploitation capabilities of a certain algorithm. Therefore, one of the main purposes of this article is to provide an algorithm that can intelligently balance between exploration and exploitation, so that it can balance exploration and exploitation capabilities. Later, in the research process, it was found that the sine and cosine function and the salp foraging trajectory have a high mathematical similarity, which greatly improves the optimization ability of the algorithm. In addition, the variable neighbourhood strategy can appropriately expand the optimization range of the algorithm. So in this paper, a novel golden sine cosine salp swarm algorithm with variable neighbourhood search scheme (GSCSSA‐VNS) is proposed, the another objective of proposing this algorithm is as a new optimization method for shape matching. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost effective in the computation process. Experimental results of four realistic examples show that GSCSSA‐VNS is able to provide very competitive results and outperforms the other algorithms. [ABSTRACT FROM AUTHOR]

A clinically applicable approach to estimate the in vivo mechanical material properties of the heart wall is presented. This optimization‐based inverse estimation approach applies a shape‐based objective functional combined with rigid body registration and incremental parameterization of heterogeneity to use standard clinical imaging data along with simplified representations of cardiac function to provide consistent and physically meaningful solution estimates. The capability of the inverse estimation algorithm is evaluated through application to two clinically obtained human datasets to estimate the passive elastic mechanical properties of the heart wall, with an emphasis on the right ventricle. One dataset corresponded to a subject with normal heart function, while the other corresponded to a subject with severe pulmonary hypertension, and therefore expected to have a substantially stiffer right ventricle. Patient‐specific pressure‐driven bi‐ventricle finite element analysis was used as the forward model and the endocardial surface of the right ventricle was used as the target data for the inverse problem. By using the right ventricle alone as the target of the inverse problem the relative sensitivity of the objective function to the right ventricle properties is increased. The method was able to identify material properties to accurately match the corresponding shape of the simplified forward model to the clinically obtained target data, and the properties obtained for the example cases are consistent with the clinical expectation for the right ventricle. Additionally, the material property estimates indicate significant heterogeneity in the heart wall for both subjects, and more so for the subject with pulmonary hypertension. [ABSTRACT FROM AUTHOR]

This article introduces a novel approach to generate visually promising skeletons automatically without any manual tuning. In practice, it is challenging to extract promising skeletons directly using existing approaches. This is because they either cannot fully preserve shape features, or require manual intervention, such as boundary smoothing and skeleton pruning, to justify the eye-level view assumption. We propose an approach here that generates backbone and dense skeletons by shape input, and then extends the backbone branches via skeleton grafting from the dense skeleton to ensure a well-integrated output. Based on our evaluation, the generated skeletons best depict the shapes at levels that are similar to human perception. To evaluate and fully express the properties of the extracted skeletons, we introduce two potential functions within the high-order matching protocol to improve the accuracy of skeleton-based matching. These two functions fuse the similarities between skeleton graphs and geometrical relations characterized by multiple skeleton endpoints. Experiments on three high-order matching protocols show that the proposed potential functions can effectively reduce the number of incorrect matches. [ABSTRACT FROM AUTHOR]

Road markings are of great significance to road inventory management, intelligent transportation systems, high-definition maps (HD Maps), and autonomous driving. Most existing methods focus on extracting and classifying the road markings from mobile laser scanning (MLS) point clouds. Nevertheless, the performance suffers from the wear and incompleteness of road markings. Converting the extracted road marking points into a consistent representation with a sparse set of parameters needs extensive study as well. This paper presents a two-stage coarse-to-fine object detection and localization approach for automatically extracting and modeling road markings from mobile laser scanning (MLS) point clouds, which is robust to variations in reflective intensity, various point density, and partial occlusion. The first step is to use a general object detection network to detect bounding boxes with semantic labels of road markings on feature maps, which consists of information about intensity, elevation, and distance to the scanner. Next, accurate positions, orientations, and scales of candidate road markings are determined in the raw point clouds coordinate system through a shape matching operator that leverages the standard geometric structure and radiometric appearance of road markings. Finally, a re-ranking operator combining the coarse detection confidence and fine localization score is used to acquire the final road marking models. Comprehensive experiments revealed that the proposed method achieved an overall performance of 92.3% in recall and 95.1% in precision for extracting 12 types of road markings from urban scene point cloud datasets, even with worn and incomplete road markings. The modeling performance was 0.504 using the mIoU metric. [ABSTRACT FROM AUTHOR]

We present a dynamical systems approach for geometric matchings in an ensemble of polytopes along rigid-body motions. Each polytope can be characterized by a vertex set and edge or faces determined by vertices, and polygons and simplexes correspond to a polytope. For a geometric matching, we propose a system of dynamical system for the evolution of centroids and rotations of polytopes to match the vertices under rigid-body motions which can be decomposed as a composition of translation and rotations. Our proposed dynamical system acts on the product space (ℝd x SO(d))N. The evolution of centroids can be described by the coupled linear second-order dynamical system with diffusive linear couplings, whereas rotations for the matching of vertices are described by the Lohe matrix model on SO(d)N. In particular, the Lohe matrix model has been derived from some set of physical principles compared to previous works in which the Lohe matrix model were employed as a system dynamics. This is a contrasted difference between earlier works on the Lohe matrix model which has been adopted a priori for an aggregate modeling of matrices. We also provide an analytical result leading to the complete shape matchings for an ensemble of congruent polytopes, and several numerical examples to illustrate analytical results visually. [ABSTRACT FROM AUTHOR]

Most research on 3D model retrieval focus on shape as the main feature used for search and retrieval. In this paper, we explore augmenting 3D shape-based similarity measures by combining shape with color features. There are already numerous techniques for content-based 3D model retrieval utilizing shape, some use global shape features, shape distributions, or view-based methods. This paper uses a view-based approach using 2D projections of 3D models taken from different viewpoints. The main problem with view-based techniques is how to normalize the rotation of the mesh in 3D space to properly align the model to the projection planes. In our work, we use Normal-vectors PCA (NPCA). The 3D model is first projected onto three planes using their normal vectors and gray-level images (inner elevations) that describe the depth information are generated. The angular radial transform (ART) from MPEG-7 is then used on these inner elevations to extract a feature descriptor. These descriptors are called the inner elevation descriptor (IED) and are used to match the query with the database object. This shape feature is then augmented using color histograms considering perceptual color similarity. It is then tested on the Princeton Shape Benchmark and compared to well-known approaches. [ABSTRACT FROM AUTHOR]

Shape matching is a fundamental operation in digital geometry processing and computer graphics. Challenges in shape matching include finding correspondences of partial shapes with deformations, as well as topological noise and ambiguities. This paper presents a partial shape correspondence algorithm based on the concept of the functional map. An iterative dense matching algorithm, incorporating sparse and guided dense matching, is proposed along with a new objective function including both descriptor matching error and transformation error. Rank estimation with the rank direction is proposed to achieve more accurate slope approximation of the functional map. The slope is beneficial because it directly influences the matching efficiency. The experimental results obtained using FAUST and SHREC′16 datasets demonstrate the effectiveness of our proposed algorithm for matching the shapes of different human subjects and shapes with large missing parts compared with state-of-the art algorithms. The proposed algorithm provides an average geodesic distance of

Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision. While a number of correspondence selection methods have been proposed in recent years, their advantages and shortcomings remain unclear regarding different applications and perturbations. To fill this gap, this paper gives a comprehensive evaluation of nine state-of-the-art 3D correspondence grouping methods. A good correspondence grouping algorithm is expected to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall as well as facilitating accurate transformation estimation. Toward this rule, we deploy experiments on three benchmarks with different application contexts, including shape retrieval, 3D object recognition, and point cloud registration. We also investigate various perturbations such as noise, point density variation, clutter, occlusion, partial overlap, different scales of initial correspondences, and different combinations of keypoint detectors and descriptors. The rich variety of application scenarios and nuisances result in different spatial distributions and inlier ratios of initial feature correspondences, thus enabling a thorough evaluation. Based on the outcomes, we give a summary of the traits, merits, and demerits of evaluated approaches and indicate some potential future research directions. [ABSTRACT FROM AUTHOR]

Plant species recognition using leaf images is a highly important and challenging issue in botany and pattern recognition. A center problem of this task is how to accurately extract leaf image characteristics and quickly calculate the similarity between them. This article presents a new shape description approach called triangle-distance representation (TDR) for plant leaf recognition. The TDR descriptor is represented by two matrices: a sign matrix and a triangle center distance matrix. The sign matrix is used to characterize the convex/concave property of a shape contour, while the triangle center distance matrix is used to represent the bending degree and spatial information of a shape contour. This method can effectively capture the detailed and global characteristics of a leaf shape while keeping the similarity transformations (translation, rotation, and scaling) unchanged. In addition, this method is quite compact and has low computational complexity. We tested our method on four standard plant leaf datasets, including the famous Swedish, Smithsonian, Flavia, and ImageCLEF2012 datasets. The results confirm that our approach exceeds the prior state-of-the-art shape-based plant leaf recognition approaches. An extra experiment on the MPEG-7 shape dataset further shows that our method can be applied to general shape recognition.

Although the total number of ureteroscopy interventions during the past years has significantly increased, current flexible ureteroscopy procedures still present some limitations to urologic surgeons. However, nowadays different robotic systems have been developed in order to reduce those limitations. Flexible ureteroscopy robots provide a technological alternative which combines the benefits that this type of procedures offers to the patients, and solutions to the problems encountered from the surgeons perspective. In this paper, a virtual reality training platform for robot-assisted flexible ureterorenoscopy interventions is presented. A position based model for the virtual flexible endoscope is detailed and a standard user interface for the training platform is designed. Moreover, a comparative analysis of the performance of the training platform in different scenarios, including the navigation through a three-dimensional ureterorenal model, is presented. The obtained results determine that the training platform presents different computational rates depending on the complexity of the implemented environment and on the number of collisions and constraints that have to be handled. Nevertheless, the virtual model is visually plausible, effective for real-time user interaction and suitable for training.

Shape matching has been extensively used in various fields. The local feature-based or global feature-based algorithms can hardly describe the shape comprehensively due to their inherent defects. Combining the local and global feature to describe the shape has become a trend. In this paper, an improved discrete curve evolution algorithm is proposed which combines the discrete curve evolution with the uniform sampling and achieves a better description of the shape contour. Three simple and intuitive multi-scale features which represent both the global and local features of shapes are designed from aspects of the spatial relationship of contour points, structural information of contour sequence, and shape geometry feature. A cyclic Smith-Waterman algorithm is introduced to solve local contour matching and starting point selection. Experimental results demonstrate that our proposed features are translation, rotation, and scaling invariant, and have good robustness to deformation. Retrieval accuracies of Kimia99, Kimai216, and MPEG-7 indicate that our method can bring out a better performance.

Linking arbitrary shapes (e.g., circles, squares, and triangles) to personal labels (e.g., self, friend, or stranger) or reward values (e.g., £18, £6, or £2) results in immediate processing benefits for those stimuli that happen to be associated with the self or high rewards in perceptual matching tasks. Here we further explored how social and reward associations interact with multisensory stimuli by pairing labels and objects with tones (low, medium, and high tones). We also investigated whether self and reward biases persist for multisensory stimuli with the label removed after an association had been made. Both high reward stimuli and those associated with the self, resulted in faster responses and improved discriminability (i.e., higher d’), which persisted for multisensory stimuli even when the labels were removed. However, these self- and reward-biases partly depended on the specific alignment between the physical tones (low, medium, and high) and the conceptual (social or reward) order. Performance for reward associations improved when the endpoints of low or high rewards were paired with low or high tones; meanwhile, for personal associations, there was a benefit when the self was paired with either low or high tones, but there was no effect when the stranger was associated with either endpoint. These results indicate that, unlike reward, social personal associations are not represented along a continuum with two marked endpoints (i.e., self and stranger) but rather with a single reference point (the self vs. other).

Linking arbitrary shapes (e.g., circles, squares, and triangles) to personal labels (e.g., self, friend, or stranger) or reward values (e.g., £18, £6, or £2) results in immediate processing benefits for those stimuli that happen to be associated with the self or high rewards in perceptual matching tasks. Here we further explored how social and reward associations interact with multisensory stimuli by pairing labels and objects with tones (low, medium, and high tones). We also investigated whether self and reward biases persist for multisensory stimuli with the label removed after an association had been made. Both high reward stimuli and those associated with the self, resulted in faster responses and improved discriminability (i.e., higher d'), which persisted for multisensory stimuli even when the labels were removed. However, these self- and reward-biases partly depended on the specific alignment between the physical tones (low, medium, and high) and the conceptual (social or reward) order. Performance for reward associations improved when the endpoints of low or high rewards were paired with low or high tones; meanwhile, for personal associations, there was a benefit when the self was paired with either low or high tones, but there was no effect when the stranger was associated with either endpoint. These results indicate that, unlike reward, social personal associations are not represented along a continuum with two marked endpoints (i.e., self and stranger) but rather with a single reference point (the self vs. other). [ABSTRACT FROM AUTHOR]

Among the structures that make up a smart city, tunnels are recognized as an important facility in the US and Canada as an infrastructure for the construction of an eco-friendly city. Total station surveying takes a long time to acquire data and has the disadvantage that the Global Navigation Satellite System (GNSS) needs to be able to receive satellites in tunnel surveying. Recently, 3D laser scanners have been attracting attention as a new technology to compensate for the disadvantages of a small amount of data and a long measurement time of the total station. Three-dimensional laser scanners can be used in various fields. In civil engineering, research and application methods often use 3D laser scanners. Because tunnel surveying is difficult in a GNSS survey due to site conditions, total station surveying has been mainly used. As total station surveying takes a lot of time to acquire data, the use of 3D laser scanners for tunnel surveying has recently been increasing. However, when using a 3D laser scanner, a large number of reference points must be installed inside the tunnel for registration and geo-referencing of acquired data. As a new technology, a shape matching method has been introduced in the field of scanning sensors, and its use is expected. In this study, a tunnel survey using a 3D laser scanner was performed and its utility was evaluated. Also, the accuracy of the shape registration method for recent registration was analyzed. The scanning data were acquired by configuring a traverse using the total station function and compared with 10 checkpoints already installed for accuracy verification. As a result of the accuracy evaluation by comparison with the checkpoints, the maximum error was within 6 cm in the N, E, and H directions, and these results indicate the plane and elevation allowable accuracy of a 1:1000 digital map, suggesting the applicability of the method using the reference point performance and the laser scanning data. By using scan data, not only analysis of the cross section but also continuous analysis of the shape of the scanning section is possible. An additional evaluation of the accuracy of shape matching of the 3D laser scanner data was performed. By using the scanning data acquired five times, geo-referencing of the data acquired at the first station was performed, and then the data obtained were registered by the shape matching method. A maximum of error of 1 m occurred at the end of the data. This indicates that the accuracy of the shape registration can be degraded if the same shape is repeated, such as in a tunnel. Therefore, in tunnel surveying, it is judged that the construction of the data only by shape matching may lower the accuracy. Further research can improve the accuracy of the shape registration method, which will improve the efficiency of tunnel surveying. [ABSTRACT FROM AUTHOR]