Correction of topological errors in automated traces of neurites

The purpose of this paper is to analyze how optical pre-processing with polarizing filters and digital pre-processing with HDR imaging, may improve the automated 3D modeling pipeline based on SFM and Image Matching, with special emphasis on optically non-cooperative surfaces of shiny or dark materials. Because of the automatic detection of homologous points, the presence of highlights due to shiny materials, or nearly uniform dark patches produced by low reflectance materials, may produce erroneous matching involving wrong 3D point estimations, and consequently holes and topological errors on the mesh originated by the associated dense 3D cloud. This is due to the limited dynamic range of the 8 bit digital images that are matched each other for generating 3D data. The same 256 levels can be more usefully employed if the actual dynamic range is compressed, avoiding luminance clipping on the darker and lighter image areas. Such approach is here considered both using optical filtering and HDR processing with tone mapping, with experimental evaluation on different Cultural Heritage objects characterized by non-cooperative optical behavior. Three test images of each object have been captured from different positions, changing the shooting conditions (filter/no-filter) and the image processing (no processing/HDR processing), in order to have the same 3 camera orientations with different optical and digital pre-processing, and applying the same automated process to each photo set.

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Real-Time 3D Reconstruction of Thin Surface Based on Laser Line Scanner

Sensors ◽

10.3390/s20020534 ◽

2020 ◽

Vol 20 (2) ◽

pp. 534 ◽

Cited By ~ 1

Author(s):

Yuan He ◽

Shunyi Zheng ◽

Fengbo Zhu ◽

Xia Huang

Keyword(s):

3D Reconstruction ◽

Real Time ◽

Laser Line ◽

Frame Rate ◽

Processing Unit ◽

Memory Usage ◽

Central Processing ◽

Time Performance ◽

Topological Errors ◽

Line Scanner

The truncated signed distance field (TSDF) has been applied as a fast, accurate, and flexible geometric fusion method in 3D reconstruction of industrial products based on a hand-held laser line scanner. However, this method has some problems for the surface reconstruction of thin products. The surface mesh will collapse to the interior of the model, resulting in some topological errors, such as overlap, intersections, or gaps. Meanwhile, the existing TSDF method ensures real-time performance through significant graphics processing unit (GPU) memory usage, which limits the scale of reconstruction scene. In this work, we propose three improvements to the existing TSDF methods, including: (i) a thin surface attribution judgment method in real-time processing that solves the problem of interference between the opposite sides of the thin surface; we distinguish measurements originating from different parts of a thin surface by the angle between the surface normal and the observation line of sight; (ii) a post-processing method to automatically detect and repair the topological errors in some areas where misjudgment of thin-surface attribution may occur; (iii) a framework that integrates the central processing unit (CPU) and GPU resources to implement our 3D reconstruction approach, which ensures real-time performance and reduces GPU memory usage. The proposed results show that this method can provide more accurate 3D reconstruction of a thin surface, which is similar to the state-of-the-art laser line scanners with 0.02 mm accuracy. In terms of performance, the algorithm can guarantee a frame rate of more than 60 frames per second (FPS) with the GPU memory footprint under 500 MB. In total, the proposed method can achieve a real-time and high-precision 3D reconstruction of a thin surface.

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Analysing OpenStreetMap data for topological errors

International Journal of Spatial Temporal and Multimedia Information Systems ◽

10.1504/ijstmis.2016.076800 ◽

2016 ◽

Vol 1 (1) ◽

pp. 87 ◽

Cited By ~ 4

Author(s):

Sukhjit Singh Sehra ◽

Jaiteg Singh ◽

Hardeep Singh Rai

Keyword(s):

Topological Errors

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Voluminator—Approximating the Volume of 3D Buildings to Overcome Topological Errors

Lecture Notes in Geoinformation and Cartography - AGILE 2015 ◽

10.1007/978-3-319-16787-9_20 ◽

2015 ◽

pp. 343-362 ◽

Cited By ~ 1

Author(s):

Horst Steuer ◽

Thomas Machl ◽

Maximilian Sindram ◽

Lukas Liebel ◽

Thomas H. Kolbe

Keyword(s):

Topological Errors

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Automatical Reconstruction of Deficient CAD Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.186.241 ◽

2011 ◽

Vol 186 ◽

pp. 241-245 ◽

Cited By ~ 1

Author(s):

Gui Ping Qian ◽

Ruo Feng Tong

Keyword(s):

Finite Element ◽

Complex Surface ◽

Finite Element Mesh ◽

Surface Model ◽

Reconstruction Method ◽

Cad Model ◽

Element Mesh ◽

Surface Patches ◽

Topological Errors ◽

Surface Representations

This paper presents a new CAD model reconstruction method for finite element mesh analysis. It has been accepted by many researchers that modification of a model is often a necessity as a precursor to effective mesh generation. We design an IGES surface model transformation and repairing method based on trimmed B-spline surface patches, and give an algorithm for reconstructing Brep model from surface model without correct topology information. In processing Brep model for numerical simulation, the critical issues involves the rectification of geometrical and topological errors, clearing up sharp edges and cracks, geometry healing will be emphasized. Our model-healing algorithm essentially simplifies the problems of the imperfect models and allows one to deal with simple surface model rather than complex surface representations for finite element mesh.

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Robinson-Foulds Reticulation Networks

10.1101/642793 ◽

2019 ◽

Cited By ~ 1

Author(s):

Alexey Markin ◽

Tavis K. Anderson ◽

Venkata SKT Vadali ◽

Oliver Eulenstein

Keyword(s):

Local Search ◽

Influenza A ◽

Introgressive Hybridization ◽

Biological Data ◽

Phylogenetic Networks ◽

Np Hard ◽

Influenza A Viruses ◽

Network Problem ◽

Topological Errors ◽

Search Approach

AbstractPhylogenetic (hybridization) networks allow investigation of evolutionary species histories that involve complex phylogenetic events other than speciation, such as reassortment in virus evolution or introgressive hybridization in invertebrates and mammals. Reticulation networks can be inferred by solving thereticulation network problem, typically known as thehybridization network problem. Given a collection of phylogenetic input trees, this problem seeks aminimum reticulation networkwith the smallest number of reticulation vertices into which the input trees can be embedded exactly. Unfortunately, this problem is limited in practice, since minimum reticulation networks can be easily obfuscated by even small topological errors that typically occur in input trees inferred from biological data. We adapt the reticulation network problem to address erroneous input trees using the classic Robinson-Foulds distance. TheRF embedding costallows trees to be embedded into reticulation networksinexactly, but up to a measurable error. The adapted problem, called theRobinson-Foulds reticulation network (RF-Network) problemis, as we show and like many other problems applied in molecular biology, NP-hard. To address this, we employ local search strategies that have been successfully applied in other NP-hard phylogenetic problems. Our local search method benefits from recent theoretical advancements in this area. Further, we introduce inpractice effective algorithms for the computational challenges involved in our local search approach. Using simulations we experimentally validate the ability of our method,RF-Net, to reconstruct correct phylogenetic networks in the presence of error in input data. Finally, we demonstrate how RF-networks can help identify reassortment in influenza A viruses, and provide insight into the evolutionary history of these viruses. RF-Net was able to estimate a large and credible reassortment network with 164 taxa.

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Drainage Network Analysis and Structuring of Topologically Noisy Vector Stream Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8090422 ◽

2019 ◽

Vol 8 (9) ◽

pp. 422

Author(s):

John B. Lindsay ◽

Wanhong Yang ◽

Duncan D. Hornby

Keyword(s):

Network Analysis ◽

Channel Length ◽

Drainage Network ◽

Stream Networks ◽

Stream Data ◽

Suitable Alternative ◽

Analysis Technique ◽

Digital Elevation ◽

Topological Errors ◽

Fine Resolution

Drainage network analysis includes several operations that quantify the topological organization of stream networks. Network analysis operations are frequently performed on streams that are derived from digital elevation models (DEMs). While these methods are suited to application with fine-resolution DEM data, this is not the case for coarse DEMs or low-relief landscapes. In these cases, network analysis that is based on mapped vector streams is an alternative. This study presents a novel vector drainage network analysis technique for performing stream ordering, basin tagging, the identification of main stems and tributaries, and the calculation of total upstream channel length and distance to outlet. The algorithm uses a method for automatically identifying outlet nodes and for determining the upstream-downstream connections among links within vector stream networks while using the priority-flood method. The new algorithm was applied to test stream datasets in two Canadian study areas. The tests demonstrated that the new algorithm could efficiently process large hydrographic layers containing a variety of topological errors. The approach handled topological errors in the hydrography data that have challenged previous methods, including disjoint links, conjoined channels, and heterogeneity in the digitized direction of links. The method can provide a suitable alternative to DEM-based approaches to drainage network analysis, particularly in applications where stream burning would otherwise be necessary.

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