Single View Research Articles

The shared project, but divergent views, of the Empiricist associationists

ABSTRACT Despite its long period of dominance, the details of associationism as developed by the British Empiricists in the 18th and 19th centuries are often ignored or forgotten today. Perhaps as a result, modern understandings of Empiricist associationism are often oversimplified. In fact, there is no single core view that can be viewed as definitional, or even weaker, as characteristic, of the tradition. The actual views of associationists in this tradition are much more diverse than any such view would allow, even on fundamental aspects of the concept of association. This paper presents some of the most striking theoretical diversity and most significant discussions within the Empiricist associationist tradition. Recognizing this diversity, Empiricist associationism is best viewed as a project with shared questions and methods rather than a shared substantive theory of mind. This discussion can inform our understanding of the historical tradition, and also inform our current use of association and associationism. The concept of association is more flexible than it is often treated, and even its basic nature and function remain up for debate.

Deep Dexterous Grasping of Novel Objects From a Single View

Dexterous grasping of a novel object given a single view is an open problem. This paper makes several contributions to its solution. First, we present a simulator for generating and testing dexterous grasps. Second, we present a dataset, generated by this simulator, of 2.4 million simulated dexterous grasps of variations of 294 base objects drawn from 20 categories. Third, we combine an existing approach to learn a grasp generation model with three different learned evaluative models employing ResNet-50 or VGG16 as their visual backbone. Fourth, we train, and evaluate 17 variants of the resulting generative-evaluative architectures on the simulated dataset, showing improvement from 69.53% grasp success rate to 90.49%. Fifth, we present a real robot implementation and evaluate the four most promising variants, executing 196 real robot grasps in total. We show that our best architectural variant achieves a grasp success rate of 87.8% on real novel objects seen from a single view, improving on a baseline of 57.1%. Finally, we explore the inner workings of our best evaluative model and perform an extensive analysis of its results on the simulated dataset.

MVFusFra: A Multi-View Dynamic Fusion Framework for Multimodal Brain Tumor Segmentation.

Medical practitioners generally rely on multimodal brain images, for example based on the information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis. Hence, to further utilize the 3D information embedded in such datasets, this paper proposes a multi-view dynamic fusion framework (hereafter, referred to as MVFusFra) to improve the performance of brain tumor segmentation. The proposed framework consists of three key building blocks. First, a multi-view deep neural network architecture, which represents multi learning networks for segmenting the brain tumor from different views and each deep neural network corresponds to multi-modal brain images from one single view. Second, the dynamic decision fusion method, which is mainly used to fuse segmentation results from multi-views into an integrated method. Then, two different fusion methods (i.e., voting and weighted averaging) are used to evaluate the fusing process. Third, the multi-view fusion loss (comprising segmentation loss, transition loss, and decision loss) is proposed to facilitate the training process of multi-view learning networks, so as to ensure consistency in appearance and space, for both fusing segmentation results and the training of the learning network. We evaluate the performance of MVFusFra on the BRATS 2015 and BRATS 2018 datasets. Findings from the evaluations suggest that fusion results from multi-views achieve better performance than segmentation results from the single view, and also implying effectiveness of the proposed multi-view fusion loss. A comparative summary also shows that MVFusFra achieves better segmentation performance, in terms of efficiency, in comparison to other competing approaches.

High-Precision Measurement of Height Differences from Shadows in Non-Stereo Imagery: New Methodology and Accuracy Assessment

The shadow-height method has been extensively used to extract the heights of buildings from the shadows they cast in non-stereo (single view) aerial and satellite imagery. However, the use of this method in Earth sciences has been limited, partially due to the relatively low accuracy reported, the fuzziness of shadow edges, the complexities of the scanning sensors, and a lack of software tools. In this paper, we present an enhanced shadow-height methodology offering significant accuracy improvement. These improvements are mainly the result of using a physical approach to model the illumination gradient through the edge of shadows and by leveraging meteorological data to precisely estimate atmospheric refraction. We validated 91 shadow-derived height estimations from images obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) at three sites with latitudes between 33 and 78°S: The Andes Mountains, Sentinel Range, and Abbot ice shelf. Reference measurements were obtained from Global Navigation Satellite System (GNSS) surveys and the Ice, Cloud, and land Elevation Satellite (ICESat). The observed errors fell below 6% for small height differences (∼20 m) and below 2% for larger height differences (≳300 m). Our validation data cover solar elevations ranging from 3.7 to 42.2°, and we observed smaller absolute errors at lower solar elevations. This novel information can be valuable for studying surface elevation changes in present and old imagery and extending glacier volume variation time-series.

Multi-View Large Population Gait Database With Human Meshes and Its Performance Evaluation

Existing model-based gait databases provide the 2D poses (i.e., joint locations) extracted by general pose estimators as the human model. However, these 2D poses suffer from information loss and are of relatively low quality. In this paper, we consider a more informative 3D human mesh model with parametric pose and shape features, and propose a multi-view training framework for accurate mesh estimation. Unlike existing methods, which estimate a mesh from a single view and suffer from the ill-posed estimation problem in 3D space, the proposed framework takes asynchronous multi-view gait sequences as input and uses both multi-view and single-view streams to learn consistent and accurate mesh models for both multi-view and single-view sequences. After applying the proposed framework to the existing OU-MVLP database, we establish a large-scale gait database with human meshes (i.e., OUMVLP-Mesh), containing over 10,000 subjects and up to 14 view angles. Experimental results show that the proposed framework estimates human mesh models more accurately than similar methods, providing models of sufficient quality to improve the recognition performance of a baseline model-based gait recognition approach.

Feature selection based on multiview entropy measures in multiperspective rough set

The performance of the neighborhood rough set model in feature selection is limited by nonobjective parameter selection method, the uncertainty measures considered only from a single view, and high time cost caused by processing high-dimensional data. To solve the above problems, this study first defines the interclass boundary to granulate the samples in different classes, and three types of neighborhood concepts—negative perspective, neutral perspective, and positive perspective—are put forward based on different cognitive perspectives. Then, the concept of the multiperspective rough set model is developed. The most prominent feature of this model is the discovery of differences between classes from the given data, without any parameters. Second, by integrating the information theory and algebraic views under the multiperspective rough set model, multiview entropy measures are proposed to effectively measure the uncertainty in data. Moreover, a nonmonotonic feature selection algorithm based on the mutual information in the multiview entropy measures under the neutral perspective as the evaluation function of feature importance is designed to resolve the disadvantages of the algorithms based on the monotone evaluation function. Finally, Information Gain is introduced to preliminarily decrease the dimension of high-dimensional data sets to promote classification accuracy and reduce time consumption. The experimental results confirm that the proposed algorithm is efficient in eliminating noise and increasing classification accuracy.

TCTAP A-073 Murray Law-Based Quantitative Flow Ratio for Assessment of Left Main Bifurcation Derived From a Single Fluoroscopic Angiographic View as Compared to FFRCT

TCTAP A-073 Murray Law-Based Quantitative Flow Ratio for Assessment of Left Main Bifurcation Derived From a Single Fluoroscopic Angiographic View as Compared to FFRCT

Pose Estimation and Non-Rigid Registration for Augmented Reality During Neurosurgery.

A craniotomy is the removal of a part of the skull to allow surgeons to have access to the brain and treat tumors. When accessing the brain, a tissue deformation occurs and can negatively influence the surgical procedure outcome. In this work, we present a novel Augmented Reality neurosurgical system to superimpose pre-operative 3D meshes derived from MRI onto a view of the brain surface acquired during surgery. Our method uses cortical vessels as main features to drive a rigid then non-rigid 3D/2D registration. We first use a feature extractor network to produce probability maps that are fed to a pose estimator network to infer the 6-DoF rigid pose. Then, to account for brain deformation, we add a non-rigid refinement step formulated as a Shape-from-Template problem using physics-based constraints that helps propagate the deformation to sub-cortical level and update tumor location. We tested our method retrospectively on 6 clinical datasets and obtained low pose error, and showed using synthetic dataset that considerable brain shift compensation and low TRE can be achieved at cortical and sub-cortical levels. The results show that our solution achieved accuracy below the actual clinical errors demonstrating the feasibility of practical use of our system. This work shows that we can provide coherent Augmented Reality visualization of 3D cortical vessels observed through the craniotomy using a single camera view and that cortical vessels provide strong features for performing both rigid and non-rigid registration.

Synthesis, spectral characterization, single crystal X-ray diffraction, and antimicrobial properties of 1-(4-iodo-1-hydroxynaphthalen-2-yl)ethan-1-one

This paper describe synthesis of 1-(4-iodo-1-hydroxynaphthalen-2-yl)ethan-1-one (3) from 2-acetyl-1-naphthol 2 by using I 2 /HIO 3 in ethanol under reflux condition. The structure of titled compound confirmed through spectral techniques. A well grown single crystal was developed using solvent evaporation technique for their X-ray diffraction analysis. The results showed that compound 3 crystallize in the monoclinic crystal system with Z value of four and space group symmetry P21/n. Whereas unit cell dimensions and angles are a; 7.5183 Å, b; 9.7529 Å, c; 14.8633 Å, α; 90°, β; 91.923°, γ; 90°. Moreover crystal data and packing view demonstrated that crystal was stabilized by one strong classical hydrogen bond interaction (O(2)-H(2)...O(1)) and two weak intermolecular hydrogen bonding. The synthesized compound was evaluated for antimicrobial activity against the Bacillus subtilis, Escherichia Coli, Pseudomonas Aeruginosa, Aspergillus flavus, Aspergillus Niger and Candida albicans organisms. Biological activity revealed that the compound 3 show significant to moderate activity. Single crystal packing view of 1-(4-iodo-1-hydroxynaphthalen-2-yl)ethan-1-one.

The Enchanting Swab Cleaner

Abstract: Cleaning residences and the environment has become more difficult due to today's hectic routine. There are cleaners in the market now that demand humans to bend and remove spots from the floor by sprinkling their liquid to clean it. As a result, there is a pressing need to implement a cleaner that is as efficient as possible. This project has used a cost-effective way for cleaning the desired region. Hazardous locations can be cleaned using this cleanser, reducing human risks. This is accomplished by incorporating a new concept into an existing one. A pipe is attached to the lip of the bottle by the cleaner's design. Which of the following will be Which will be aided by the motor at the end of the pipe, which will pressurise the liquid and sprinkle it where it is needed. When used with a detergent cleaner, the microfiber method removed more bacteria than cotton string mops. The addition of a disinfectant had no effect on the microfibre system's germ removal. When a cotton string mop was employed, however, the use of disinfectant greatly improved germ elimination (95 percent vs 68 percent , respectively). When compared to a traditional view system, the testing results utilising a scale model show that the proposed view system can reduce work time, stoppage time, and help operators swap views more frequently and relieve their concentration from a single view. These findings imply that the proposed view system has the potential to improve productivity. The goal of this study was to evaluate the upper limb's musculoskeletal risk variables (repetitions, posture, and forces) during domestic floor mopping duties.

Estimating the optimal entry point of the antegrade femoral nailing: Previous and novel morphometric measurements.

This anatomical study aimed to investigate the possible relationships between the proximal femur parameters and verify the optimal entry point in line with the medullary canal. Both in single image series and 3D-MPR views, 63 femur bones were evaluated. One-millimeter multidetector computed tomography scans were collected and assessed by OsiriX-Lite version 8 and Horos v3.3.5. Entry point locations, projected and true femoral neck-shaft, anteversion, and newly defined nail entrance angles were measured. The entry points of 16 femurs were placed at the tip of the greater trochanter, and the remaining 47 femurs were in the trochanteric fossa (i.e., piriformis fossa). Thirty-three of the entry points found in the trochanteric fossa were overlapped by the greater trochanter. When the parameters of the right and left sides were compared, it was found that; projected neck angle, nail entrance angle, and the differences between true neck angle and nail entrance angle were found significantly different. The relationship between nail entrance angle and entry point localization was assessed, and the probability of the entry point being at the tip of the greater trochanter is 63 times greater when the NEA is below 90. This study has demonstrated that the ideal entry point for straight nails, which is based on the anatomic axis of the femur, is found mainly at the trochanteric fossa, and the greater trochanter overlapped 70.21% of them. Level IV, Diagnostic Study.

Multi-view graph convolutional networks with attention mechanism

Recent advances in graph convolutional networks (GCNs), which mainly focus on how to exploit information from different hops of neighbors in an efficient way, have brought substantial improvement to many graph data modeling tasks. Most of the existing GCN-based models however are built on the basis of a fixed adjacency matrix, i.e., a single view topology of the underlying graph. That inherently limits the expressive power of the developed models especially when the raw graphs are often noisy or even incomplete due to the inevitably error-prone data measurement or collection. In this paper, we propose a novel framework, termed Multi-View Graph Convolutional Networks with Attention Mechanism (MAGCN), by incorporating multiple views of topology and an attention-based feature aggregation strategy into the computation of graph convolution. As an advanced variant of GCNs, MAGCN is fed with multiple “trustable” topologies, which already exist for a given task or are empirically generated by some classical graph construction methods, which has good potential to produce a better learning representation for downstream tasks. Furthermore, we present some theoretical analysis about the expressive power and flexibility of MAGCN, which provides a general explanation as to why multi-view based methods can potentially outperform those relying on a single view. Our experimental study demonstrates the state-of-the-art accuracies of MAGCN on Cora, Citeseer, and Pubmed datasets. Robustness analysis is also undertaken to show the advantage of MAGCN in handling some uncertainty issues in node classification tasks.

Automatic Building Detection for Multi-Aspect SAR Images Based on the Variation Features

Multi-aspect synthetic aperture radar (SAR) images contain more information available for automatic target recognition (ATR) than images from a single view. However, the sensitivity to aspect angles also makes it hard to extract and integrate information from multi-aspect images. In this paper, we propose a novel method based on the variations features to realize automatic building detection in the image level. First, to get a comprehensive description of target variation patterns, statistical characteristic variances are derived from three representative and complementary categories. Then, these obtained features are fused and put in the K-means classifier for prescreening, whose results are used as the training sets in supervised classification later to avoid manual labeling. Second, for more precise detection performance, finer features in vector forms are obtained by principal component analysis (PCA). The variation patterns of these feature vectors are explored in two different manners of correlation and fluctuation analyses and processed by separate support vector machines (SVMs) after fusion. Finally, the independent SVM detection results are fused according to a maximum probability rule. Experiments conducted on two different airborne data sets demonstrate the robustness and effectiveness of the proposed method, in spite of significant target signature variabilities and cluttered background.

Single-view calibration of a catadioptric camera based on a theodolite model.

The catadioptric panoramic imaging system may provide 360° panoramic imaging by employing the convex surface of a quadric surface with rotational symmetry as the reflector, which effectively compensates for the disadvantages of the narrow field of view in typical camera systems. First, this paper proposes a theodolite-based catadioptric camera image model based on the rotational symmetry of a catadioptric camera mirror, which simplifies the 2D modeling problem to a 1D problem. Simultaneously, the equivalence of the theodolite imaging model and the standard spherical imaging model also is demonstrated in this work. Second, this paper presents a method to calibrate the theodolite model parameters using only a single view and explains the calculation of model parameter initialization and iterative optimization steps in detail. Then, this paper demonstrates how to calibrate the theodolite model parameters using only a single view, as well as how to calculate the model parameter initialization and iterative optimization steps. Finally, simulation experiments and actual experiments have confirmed the correctness and stability of the method. The experimental results show that the average and standard deviation of the back-projection error are, respectively, 0.1983125 pixels and 0.0006153 pixels, in this model. We believe the theodolite model suggested in this paper offers a viable approach to catadioptric camera image modeling.

Dynamic guided metric representation learning for multi-view clustering.

Multi-view clustering (MVC) is a mainstream task that aims to divide objects into meaningful groups from different perspectives. The quality of data representation is the key issue in MVC. A comprehensive meaningful data representation should be with the discriminant characteristics in a single view and the correlation of multiple views. Considering this, a novel framework called Dynamic Guided Metric Representation Learning for Multi-View Clustering (DGMRL-MVC) is proposed in this paper, which can cluster multi-view data in a learned latent discriminated embedding space. Specifically, in the framework, the data representation can be enhanced by multi-steps. Firstly, the class separability is enforced with Fisher Discriminant Analysis (FDA) within each single view, while the consistence among different views is enhanced based on Hilbert-Schmidt independence criteria (HSIC). Then, the 1st enhanced representation is obtained. In the second step, a dynamic routing mechanism is introduced, in which the location or direction information is added to fulfil the expression. After that, a generalized canonical correlation analysis (GCCA) model is used to get the final ultimate common discriminated representation. The learned fusion representation can substantially improve multi-view clustering performance. Experiments validated the effectiveness of the proposed method for clustering tasks.

Transmission line fault-cause classification based on multi-view sparse feature selection

With the development of communication and monitoring techniques, multi-source information in form of multi-view data is expected to support fault-cause identification for transmission line maintenance and fault disposal. However, the combined used of multi-view data poses a great challenge of information fusion. In respond to this challenge, this paper proposes a multi-view sparse feature selection (MVSFS) method to combine contextual and waveform features for fault-cause classification. l2,1-norm sparsity regularization is adopted to select discriminative features across two views and an ɛ-dragging technique is integrated into the regression model to enhance its discriminant ability. Subsequently, an iteration optimization algorithm is devised to solve the model. Experiments on a real-life fault dataset demonstrate that contextual and waveform features can achieve higher accuracy than single view learning only through appropriate fusion, and the proposed MVSFS can overcome the drawback of conventional feature selection methods and make a great improvement of classification performance with multi-view data.

A Spatial Relationship Preserving Adversarial Network for 3D Reconstruction from a Single Depth View

Recovering the geometry of an object from a single depth image is an interesting yet challenging problem. While previous learning based approaches have demonstrated promising performance, they don’t fully explore spatial relationships of objects, which leads to unfaithful and incomplete 3D reconstruction. To address these issues, we propose a Spatial Relationship Preserving Adversarial Network (SRPAN) consisting of 3D Capsule Attention Generative Adversarial Network (3DCAGAN) and 2D Generative Adversarial Network (2DGAN) for coarse-to-fine 3D reconstruction from a single depth view of an object. Firstly, 3DCAGAN predicts the coarse geometry using an encoder-decoder based generator and a discriminator. The generator encodes the input as latent capsules represented as stacked activity vectors with local-to-global relationships (i.e., the contribution of components to the whole shape), and then decodes the capsules by modeling local-to-local relationships (i.e., the relationships among components) in an attention mechanism. Afterwards, 2DGAN refines the local geometry slice-by-slice, by using a generator learning a global structure prior as guidance, and stacked discriminators enforcing local geometric constraints. Experimental results show that SRPAN not only outperforms several state-of-the-art methods by a large margin on both synthetic datasets and real-world datasets, but also reconstructs unseen object categories with a higher accuracy.

Childhood Stuttering: Case-Based Examples of Multidimensional Treatment.

A well-known parable depicts six blind men investigating the nature of an elephant through touch.[1] Each observer, interfacing with the elephant from a single vantage point (i.e., side, trunk, tusk, leg, ear, tail), reaches an independent conclusion (i.e., wall, snake, spear, large cow, carpet, or rope), seemingly incompatible with those of his peers. In the field of stuttering, and many others, this tale serves to remind us that a single or narrow view of the complexities of human nature falls short, as the whole is greater than any one perspective or even than the sum of its parts.[2] [3] Given this understanding, in recent decades, speech-language pathologists (SLPs) have become increasingly well-versed in applying theories, models, and evidence from research to clinical service provision across client populations, including those who stutter.[4] [5] [6]

Vibration Measurement of a Metal Sheet Using Single-Camera Digital Image Correlation with Projection Components

Digital image correlation has emerged as a popular method for the dynamic performance measurement of metallic and polymer sheets, owing to the benefits of being a noncontact, full-field, and high-precision method. Two or more high-speed cameras are required for full-field vibration measurements with three-dimensional digital image correlation, which is generally costly. Perpendicular view to the specimen surface is conventional in two-dimensional digital image correlation, and the out-of-plane displacement is regarded as a part of systematic errors. In this study, a single view method was implemented with no complex optical settings. The full-field vibration displacement of the metal sheet was measured with projection components, and the first four orders of displacement modes were identified. Finite element analysis and traditional experimental modal analysis were then implemented to validate the effectiveness and accuracy of the proposed approach. The results show that the dynamic parameters, including the natural frequencies and mode shapes, were well consistent. Meanwhile, there is a significant difference in the length of mode shape vectors. The number of measurement points in the proposed method is 2016, which is far more than the number of measurement points in the traditional experimental modal analysis. This would be convenient and beneficial for damage identification towards thin-wall parts including turbine blade with the continuum hypothesis of mode shapes and a single-camera DIC system. It is worth noting that this is effective with conditions of small deformation vibration and no rigid-body rotation.

Soil governance: Who is governing who? A North American Indigenous perspective on Mother Earth

This article is presented as a single person's view of approaching the earth, soil, its complexities and simplicities, and its gifts. I am blessed to often be at the knee of many people who know that we need to live with her, we have responsibilities to her and that we are completely dependent on her. Period. Full stop. She is not dependent on us. Governing with soil from a traditional law perspective means creating a system of laws for humans to live by that understands this dependancy as a foundation for those laws. To maintain that lifeforce between the earth and humans we must see that all beings are connected, all related all interdependent. Here I offer a few things I know from my time with teachers in Indigenous communities to plant a few seeds to encourage you to find your teachers, human or otherwise.