scholarly journals A parallel dual marching cubes approach to quad only surface reconstruction

2021 ◽  
Author(s):  
Roberto Grosso ◽  
Daniel Zint
Keyword(s):  
Data Structure ◽  
Surface Reconstruction ◽  
Synthetic Data ◽  
Data Sets ◽  
Marching Cubes ◽  
Volume Data ◽  
Almost Everywhere ◽  
Reconstruction Methods ◽  
Novel Method ◽  
Reconstructed Surface

AbstractWe present a novel method that reconstructs surfaces from volume data using a dual marching cubes approach without lookup tables. The method generates quad only meshes which are consistent across cell borders, i.e., they are manifold and watertight. Vertices are positioned exactly on the reconstructed surface almost everywhere, leading to higher accuracy than other reconstruction methods. A halfedge data structure is used for storing the meshes which is convenient for further processing. The method processes elements in parallel and therefore runs efficiently on GPU. Due to the transition between layers in volume data, meshes have numerous vertices with valence three. We use simplification patterns for eliminating quads containing these vertices wherever possible which reduces the number of elements and increases quality. We briefly describe a CUDA implementation of our method, which allows processing huge amounts of data on GPU at almost interactive time rates. Finally, we present runtime and quality results of our method on medical and synthetic data sets.

Hardware Accelerated Segmentation of Complex Volumetric Filament Networks

2009 ◽  
Vol 15 (4) ◽  
pp. 670-681 ◽  
Author(s):  
David Mayerich ◽  
John Keyser
Keyword(s):  
High Throughput ◽  
Large Data ◽  
Graphics Hardware ◽  
Data Sets ◽  
Volume Data ◽  
Data Set ◽  
Thin Filaments ◽  
Novel Method ◽  
Filament Networks ◽  
Microscopy Data

We present a framework for segmenting and storing filament networks from scalar volume data. Filament networks are encountered more and more commonly in biomedical imaging due to advances in high-throughput microscopy. These data sets are characterized by a complex volumetric network of thin filaments embedded in a scalar volume field. High-throughput microscopy volumes are also difficult to manage since they can require several terabytes of storage, even though the total volume of the embedded structure is much smaller. Filaments in microscopy data sets are difficult to segment because their diameter is often near the sampling resolution of the microscope, yet these networks can span large regions of the data set. We describe a novel method to trace filaments through scalar volume data sets that is robust to both noisy and undersampled data. We use graphics hardware to accelerate the tracing algorithm, making it more useful for large data sets. After the initial network is traced, we use an efficient encoding scheme to store volumetric data pertaining to the network.

scholarly journals Mining Periodic Traces of an Entity on Web

2015 ◽  
Vol 10 (5) ◽  
pp. 654
Author(s):  
Xinyan Huang ◽  
Xinjun Wang ◽  
Yan Zhang ◽  
Jinxin Zhao
Keyword(s):  
Large Scale ◽  
High Efficiency ◽  
Experimental Studies ◽  
Synthetic Data ◽  
Graph Model ◽  
Data Sets ◽  
Analysis Method ◽  
Cluster Analysis Method ◽  
Novel Method ◽  
Small Periodic

<p class="Abstract">A trace of an entity is a behavior trajectory of the entity. Periodicity is a frequent phenomenon for the traces of an entity. Finding periodic traces for an entity is essential to understanding the entity behaviors. However, mining periodic traces is of complexity procedure, involving the unfixed period of a trace, the existence of multiple periodic traces, the large-scale events of an entity and the complexity of the model to represent all the events. However, the existing methods can’t offer the desirable efficiency for periodic traces mining. In this paper, Firstly, a graph model(an event relationship graph) is adopted to represent all the events about an entity, then a novel and efficient algorithm, TracesMining, is proposed to mine all the periodic traces. In our algorithm, firstly, the cluster analysis method is adopted according to the similarity of the activity attribute of an event and each cluster gets a different label, and secondly a novel method is proposed to mine all the Star patterns from the event relationship graph. Finally, an efficient method is proposed to merge all the Stars to get all the periodic traces. High efficiency is achieved by our algorithm through deviating from the existing edge-by-edge pattern-growth framework and reducing the heavy cost of the calculation of the support of a pattern and avoiding the production of lots of redundant patterns. In addition, our algorithm could mine all the large periodic traces and most small periodic traces. Extensive experimental studies on synthetic data sets demonstrate the effectiveness of our method.</p>

CPU-GPU Rendering of CT Scan Images for Vertebra Reconstruction from CT Scan Images with a Calibration Policy

2020 ◽  
pp. 1-5
Author(s):  
Usman Khan ◽  
Usman Khan ◽  
AmanUllah Yasin ◽  
Imran Shafi ◽  
Muhammad Abid
Keyword(s):  
3D Reconstruction ◽  
Ct Scan ◽  
Surface Reconstruction ◽  
3D Visualization ◽  
Cervical Vertebra ◽  
Marching Cubes ◽  
Affine Transform ◽  
Visualization Techniques ◽  
Visualization Algorithms ◽  
Gpu Implementation

In this work GPU implementation of classic 3D visualization algorithms namely Marching Cubes and Raycasting has been carried for cervical vertebra using VTK libraries. A proposed framework has been introduced for efficient and duly calibrated 3D reconstruction using Dicom Affine transform and Python Mayavi framework to address the limitation of benchmark visualization techniques i.e. lack of calibration, surface reconstruction artifacts and latency.

scholarly journals Framework for Reconstruction of Pseudo Quad Polarimetric Imagery from General Compact Polarimetry

2021 ◽  
Vol 13 (3) ◽  
pp. 530
Author(s):  
Junjun Yin ◽  
Jian Yang
Keyword(s):  
Linear Equations ◽  
Estimation Method ◽  
Data Sets ◽  
Reconstruction Procedure ◽  
Component Decomposition ◽  
Reconstruction Methods ◽  
Sar Imagery ◽  
Key Aspects ◽  
Non Linear Equations ◽  
Reconstruction Model

Pseudo quad polarimetric (quad-pol) image reconstruction from the hybrid dual-pol (or compact polarimetric (CP)) synthetic aperture radar (SAR) imagery is a category of important techniques for radar polarimetric applications. There are three key aspects concerned in the literature for the reconstruction methods, i.e., the scattering symmetric assumption, the reconstruction model, and the solving approach of the unknowns. Since CP measurements depend on the CP mode configurations, different reconstruction procedures were designed when the transmit wave varies, which means the reconstruction procedures were not unified. In this study, we propose a unified reconstruction framework for the general CP mode, which is applicable to the mode with an arbitrary transmitted ellipse wave. The unified reconstruction procedure is based on the formalized CP descriptors. The general CP symmetric scattering model-based three-component decomposition method is also employed to fit the reconstruction model parameter. Finally, a least squares (LS) estimation method, which was proposed for the linear π/4 CP data, is extended for the arbitrary CP mode to estimate the solution of the system of non-linear equations. Validation is carried out based on polarimetric data sets from both RADARSAT-2 (C-band) and ALOS-2/PALSAR (L-band), to compare the performances of reconstruction models, methods, and CP modes.

scholarly journals Computational Geometry-Based Surface Reconstruction for Volume Estimation: A Case Study on Magnitude-Frequency Relations for a LiDAR-Derived Rockfall Inventory

2021 ◽  
Vol 10 (3) ◽  
pp. 157
Author(s):  
Paul-Mark DiFrancesco ◽  
David A. Bonneau ◽  
D. Jean Hutchinson
Keyword(s):  
Computational Geometry ◽  
Surface Reconstruction ◽  
Power Law ◽  
Point Cloud ◽  
Point Clouds ◽  
Volume Estimation ◽  
Estimation Methods ◽  
Small Scale ◽  
Reconstruction Methods ◽  
3D Volume

Key to the quantification of rockfall hazard is an understanding of its magnitude-frequency behaviour. Remote sensing has allowed for the accurate observation of rockfall activity, with methods being developed for digitally assembling the monitored occurrences into a rockfall database. A prevalent challenge is the quantification of rockfall volume, whilst fully considering the 3D information stored in each of the extracted rockfall point clouds. Surface reconstruction is utilized to construct a 3D digital surface representation, allowing for an estimation of the volume of space that a point cloud occupies. Given various point cloud imperfections, it is difficult for methods to generate digital surface representations of rockfall with detailed geometry and correct topology. In this study, we tested four different computational geometry-based surface reconstruction methods on a database comprised of 3668 rockfalls. The database was derived from a 5-year LiDAR monitoring campaign of an active rock slope in interior British Columbia, Canada. Each method resulted in a different magnitude-frequency distribution of rockfall. The implications of 3D volume estimation were demonstrated utilizing surface mesh visualization, cumulative magnitude-frequency plots, power-law fitting, and projected annual frequencies of rockfall occurrence. The 3D volume estimation methods caused a notable shift in the magnitude-frequency relations, while the power-law scaling parameters remained relatively similar. We determined that the optimal 3D volume calculation approach is a hybrid methodology comprised of the Power Crust reconstruction and the Alpha Solid reconstruction. The Alpha Solid approach is to be used on small-scale point clouds, characterized with high curvatures relative to their sampling density, which challenge the Power Crust sampling assumptions.

scholarly journals Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

2021 ◽  
Vol 13 (9) ◽  
pp. 4648
Author(s):  
Rana Muhammad Adnan ◽  
Kulwinder Singh Parmar ◽  
Salim Heddam ◽  
Shamsuddin Shahid ◽  
Ozgur Kisi
Keyword(s):  
Clustering Algorithm ◽  
Suspended Sediments ◽  
Ecological Impacts ◽  
Accurate Estimation ◽  
Data Sets ◽  
The Novel ◽  
Neuro Fuzzy ◽  
Adaptive Regression ◽  
Novel Method ◽  
Model Training

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.

scholarly journals Methods for estimating uncertainty in factor analytic solutions

2014 ◽  
Vol 7 (3) ◽  
pp. 781-797 ◽  
Author(s):  
P. Paatero ◽  
S. Eberly ◽  
S. G. Brown ◽  
G. A. Norris
Keyword(s):  
Environmental Protection Agency ◽  
Synthetic Data ◽  
Analytic Solutions ◽  
Data Sets ◽  
Random Errors ◽  
Data Set ◽  
Factor Analytic ◽  
Uncertainty Estimates ◽  
Multilinear Engine ◽  
Analytic Models

Abstract. The EPA PMF (Environmental Protection Agency positive matrix factorization) version 5.0 and the underlying multilinear engine-executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement of factor elements (BS-DISP). The goal of these methods is to capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. It is shown that the three methods complement each other: depending on characteristics of the data set, one method may provide better results than the other two. Results are presented using synthetic data sets, including interpretation of diagnostics, and recommendations are given for parameters to report when documenting uncertainty estimates from EPA PMF or ME-2 applications.

Reverse time migration

Geophysics ◽  
1983 ◽  
Vol 48 (11) ◽  
pp. 1514-1524 ◽  
Author(s):  
Edip Baysal ◽  
Dan D. Kosloff ◽  
John W. C. Sherwood
Keyword(s):  
Wave Amplitude ◽  
Synthetic Data ◽  
Data Sets ◽  
Field Observations ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Migration Method ◽  
Zero Offset ◽  
Time Extrapolation

Migration of stacked or zero‐offset sections is based on deriving the wave amplitude in space from wave field observations at the surface. Conventionally this calculation has been carried out through a depth extrapolation. We examine the alternative of carrying out the migration through a reverse time extrapolation. This approach may offer improvements over existing migration methods, especially in cases of steeply dipping structures with strong velocity contrasts. This migration method is tested using appropriate synthetic data sets.

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