TH-D-213A-07: A Novel Inverse-Consistent Feature-Based Non-Rigid Registration Method That Improves the Mapping of Organs with Large-Scale Deformations

2009 ◽  
Vol 36 (6Part28) ◽  
pp. 2822-2822
Author(s):  
M Bondar ◽  
M Hoogeman ◽  
E Vasquez Osorio ◽  
G Dhawtal ◽  
J Mens ◽  
...  
Keyword(s):  
Large Scale ◽  
Registration Method ◽  
Rigid Registration ◽  
Feature Based ◽  
Consistent Feature

scholarly journals A High-Precision Registration Technology Based on Bundle Adjustment in Structured Light Scanning System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jianying Yuan ◽  
Qiong Wang ◽  
Xiaoliang Jiang ◽  
Bailin Li
Keyword(s):  
High Precision ◽  
Large Scale ◽  
Structured Light ◽  
Bundle Adjustment ◽  
Scanning System ◽  
Control Points ◽  
Registration Method ◽  
Global Control ◽  
3D Data ◽  
Structured Light Scanning

The multiview 3D data registration precision will decrease with the increasing number of registrations when measuring a large scale object using structured light scanning. In this paper, we propose a high-precision registration method based on multiple view geometry theory in order to solve this problem. First, a multiview network is constructed during the scanning process. The bundle adjustment method from digital close range photogrammetry is used to optimize the multiview network to obtain high-precision global control points. After that, the 3D data under each local coordinate of each scan are registered with the global control points. The method overcomes the error accumulation in the traditional registration process and reduces the time consumption of the following 3D data global optimization. The multiview 3D scan registration precision and efficiency are increased. Experiments verify the effectiveness of the proposed algorithm.

An Error-Adaptive, Non-Rigid Registration Method for the Analysis of Springback in Sheet Metal Forming

2015 ◽  
Vol 651-653 ◽  
pp. 1015-1020 ◽  
Author(s):  
Matthias Schweinoch ◽  
Alexei Sacharow ◽  
Dirk Biermann ◽  
Christoph Buchheim
Keyword(s):  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Simulation Software ◽  
Registration Method ◽  
Rigid Registration ◽  
Reference Shape ◽  
Geometric Deviations ◽  
Error Metric ◽  
Test Shape

Springback effects, as occuring in sheet metal forming processes, pose a challenge to manufacturingplanning: the as-built part may deviate from the desired shape rendering it unusable forits intended purpose. A compensation can be achieved by modifying the forming tools to counteractthe shape deviations. A prerequisite to compensation is the knowledge of correspondences (ui; vj),between points ui on the desired and vj on the actual shape. FEM-based simulation software providesmeans to both virtually predict springback and directly obtain correspondences. In case of experimentalprototyping and validation, however, finding correspondences requires solving a registrationproblem: given a test shape Q (scan points of the as-built geometry) and a reference shape R (CADdata of the desired geometry), a transformation S has to be found to fit both objects. Correspondencesbetween S(Q) and R may then be computed based on a metric.If S is restricted to Euclidean transformations, then S(Q) results in a rigid transformation, whereevery point of Q is subject to the same translation and rotation. Local geometric deviations due tospringback are not considered, often resulting in invalid correspondences. In this contribution, a nonrigidregistration method for the efficient analysis of springback is therefore presented. The test shape Q is iteratively partitioned into segments with respect to an error metric. The segments are locally registeredusing rigid registration subject to regulatory conditions. Resulting discontinuities are addressedby minimization of the deformation energy. The error metric uses information about the deviationscomputed based on the correspondences of the previous iteration, e.g. maximum errors or changes ofthe sign. This adaptive per-segment registration allows appropriate correspondences to be determinedeven under local geometric deviations.

Consistency Management in Feature-Based Parametric Design

1995 ◽  
Author(s):  
Ana Sofia Vieira
Keyword(s):  
Parametric Design ◽  
Automatic Generation ◽  
Fundamental Principle ◽  
Consistency Management ◽  
Semantic Constraints ◽  
Evaluation Time ◽  
Feature Based ◽  
Consistent Feature ◽  
Semantic Correctness ◽  
Definition Of

Abstract One of the main problems to be solved in design-by-features is to preserve the semantic correctness of feature-based models. Currently, feature-based parametric design (FbPD) is being used as one of the most powerful approaches for solving this problem. In this paper, a fundamental principle of this approach is introduced. Three aspects stated, are: FbPD deals with functional design primitives, it solves the automatic generation of model variations, and it offers the basis for the development of a mechanism to check the semantic correctness of feature-based models. Several concepts for the definition of semantic constraints are presented. They instigate the classification of semantic constraints in four different categories, based on the constraint evaluation-time, purpose, behaviour, and representation. Sinfonia, a system for feature-based parametric design, is presented as a testbed environment for design-by-features applications. One of its modules, the Consistency Handler, uses the constraint concepts introduced in order to preserve the semantic consistency of the models. Several examples illustrate the different types of constraints. In addition, an algorithm applied for the process of a consistent feature modification is presented.

Feature-based multibody rigid registration of CT and ultrasound images of lumbar spine

2012 ◽  
Vol 39 (6Part1) ◽  
pp. 3154-3166 ◽  
Author(s):  
Abtin Rasoulian ◽  
Purang Abolmaesumi ◽  
Parvin Mousavi
Keyword(s):  
Lumbar Spine ◽  
Ultrasound Images ◽  
Rigid Registration ◽  
Feature Based

scholarly journals Syntax Role for Neural Semantic Role Labeling

2021 ◽  
pp. 1-48
Author(s):  
Zuchao Li ◽  
Hai Zhao ◽  
Shexia He ◽  
Jiaxun Cai
Keyword(s):  
Argument Structure ◽  
Large Scale ◽  
Language Models ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Empirical Survey ◽  
Learning Framework ◽  
Syntactic Information ◽  
Feature Based ◽  
Predicate Argument Structure

Abstract Semantic role labeling (SRL) is dedicated to recognizing the semantic predicate-argument structure of a sentence. Previous studies in terms of traditional models have shown syntactic information can make remarkable contributions to SRL performance; however, the necessity of syntactic information was challenged by a few recent neural SRL studies that demonstrate impressive performance without syntactic backbones and suggest that syntax information becomes much less important for neural semantic role labeling, especially when paired with recent deep neural network and large-scale pre-trained language models. Despite this notion, the neural SRL field still lacks a systematic and full investigation on the relevance of syntactic information in SRL, for both dependency and both monolingual and multilingual settings. This paper intends to quantify the importance of syntactic information for neural SRL in the deep learning framework. We introduce three typical SRL frameworks (baselines), sequence-based, tree-based, and graph-based, which are accompanied by two categories of exploiting syntactic information: syntax pruningbased and syntax feature-based. Experiments are conducted on the CoNLL-2005, 2009, and 2012 benchmarks for all languages available, and results show that neural SRL models can still benefit from syntactic information under certain conditions. Furthermore, we show the quantitative significance of syntax to neural SRL models together with a thorough empirical survey using existing models.

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

Virtual Gas Turbines Part I: A Top-Down Geometry Modelling Environment for Turbomachinery Application

2021 ◽  
Author(s):  
Davendu Y. Kulkarni ◽  
Gan Lu ◽  
Feng Wang ◽  
Luca di Mare
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Large Scale ◽  
Logical Structure ◽  
System Level ◽  
Top Down ◽  
Design Intent ◽  
Geometry Model ◽  
Feature Based ◽  
Modelling Environment

Abstract The gas turbine engine design involves multi-disciplinary, multi-fidelity iterative design-analysis processes. These highly intertwined processes are nowadays incorporated in automated design frameworks to facilitate high-fidelity, fully coupled, large-scale simulations. The most tedious and time-consuming step in such simulations is the construction of a common geometry database that ensures geometry consistency at every step of the design iteration, is accessible to multi-disciplinary solvers and allows system-level analysis. This paper presents a novel design-intent-driven geometry modelling environment that is based on a top-down feature-based geometry model generation method. In the proposed object-oriented environment, each feature entity possesses a separate identity, denotes an abstract geometry, and carries a set of characteristics. These geometry features are organised in a turbomachinery feature taxonomy. The engine geometry is represented by a tree-like logical structure of geometry features, wherein abstract features outline the engine architecture, while the detailed geometry is defined by lower-level features. This top-down flexible arrangement of feature-tree enables the design intent to be preserved throughout the design process, allows the design to be modified freely and supports the design intent variations to be propagated throughout the geometry automatically. The application of the proposed feature-based geometry modelling environment is demonstrated by generating a whole-engine computational geometry. This geometry modelling environment provides an efficient means of rapidly populating complex turbomachinery assemblies. The generated engine geometry is fully scalable, easily modifiable and is re-usable for generating the geometry models of new engines or their derivatives. This capability also enables fast multi-fidelity simulation and optimisation of various gas turbine systems.

Is your document novel? Let attention guide you. An attention-based model for document-level novelty detection

2020 ◽  
pp. 1-28
Author(s):  
Tirthankar Ghosal ◽  
Vignesh Edithal ◽  
Asif Ekbal ◽  
Pushpak Bhattacharyya ◽  
Srinivasa Satya Sameer Kumar Chivukula ◽  
...  
Keyword(s):  
Large Scale ◽  
Novelty Detection ◽  
Detection Task ◽  
Feature Engineering ◽  
Rule Based ◽  
Semantic Level ◽  
Neural Architecture ◽  
New Information ◽  
Feature Based ◽  
Document Level

Abstract Detecting, whether a document contains sufficient new information to be deemed as novel, is of immense significance in this age of data duplication. Existing techniques for document-level novelty detection mostly perform at the lexical level and are unable to address the semantic-level redundancy. These techniques usually rely on handcrafted features extracted from the documents in a rule-based or traditional feature-based machine learning setup. Here, we present an effective approach based on neural attention mechanism to detect document-level novelty without any manual feature engineering. We contend that the simple alignment of texts between the source and target document(s) could identify the state of novelty of a target document. Our deep neural architecture elicits inference knowledge from a large-scale natural language inference dataset, which proves crucial to the novelty detection task. Our approach is effective and outperforms the standard baselines and recent work on document-level novelty detection by a margin of $\sim$ 3% in terms of accuracy.

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