A Study of Feature Line Extraction and Closed Frame Structure of a Stone Tool from Measured Point Cloud

Yuan, Lin; Matsuyama, Katsutsugu; Chiba, Fumito; Konno, Kouichi

doi:10.1109/nicoint.2016.8

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…In our method, feature line extraction approach combines a Mahalanobis distance metric algorithm. Feature line construction is not an easy task for stone tools and many approaches have been proposed [13,14,15]. However, base drawings cannot be completely connected by using previous works.…”

Section: Extraction Of Feature Linesmentioning

confidence: 99%

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Feature Line Extraction of Stone Tools Based on Mahalanobis Distance Metric

Erdenebayar

Konno

2019

The Journal of the Society for Art and Science

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Point-cloud-based technique plays a very significant role in 3D model restoration. In the archaeological application of stone tools, the scale drawing, which is hand-drawn from measured stone tools, is traditionally used. In the scale drawing creation, a base drawing which consists outline and ridge lines is initially drawn from geometric features of shape. After that other lines are extracted from knowledge of making stone tools and are added to the base drawing. It requires special knowledge to extract feature lines from stone tools so that scale drawing is time-consuming. Therefore, if the base drawing is automatically extracted, the working hours are reduced. To overcome this issue, this paper proposes a feature line extraction method using the Mahalanobis distance metric. First, the points on outline are extracted from a point cloud. Then, the surface variation is calculated with a various number of neighbors and thus the potential feature points are detected by the analysis of its surface variation. After that, the potential feature points are thinned towards the highest variation points by using Laplacian smoothing. Then, the thinned feature points are shrunk to the potential feature points. Finally, a feature line is extracted by connecting the nearest thinned feature points locating in the Mahalanobis distance field. To verify our method, the extracted feature lines are compared to the ground truth of base drawing drawn by archaeological illustrators. Our method is applied to stone tools, and we confirm the effectiveness of our method.

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Section: Extraction Of Feature Linesmentioning

confidence: 99%

“…To construct feature lines, [13,14,15] connect the nearest feature points one by one. As suggested in [3], the feature lines are initialized at the seed points, and arbitrary points obtained by thinning feature points can be chosen as the new seed points.…”

Section: Extraction Of Feature Linesmentioning

confidence: 99%

Feature Line Extraction of Stone Tools Based on Mahalanobis Distance Metric

Erdenebayar

Konno

2019

The Journal of the Society for Art and Science

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“…Adaptive tracking algorithms avoid exhaustive model or trajectory searching by promoting candidate displacements consistent with a window of recent observations, thereby decreasing the overall computational complexity while maintaining accuracy and adaptability . Point clouds are the suitable unit of measure for particle position in an adaptive tracking method because point clouds contain multiple weighted data points, , adapting to a localization confidence gradient rather than a single representative point. Achieving accurate particle linking in a cellular environment with an adaptive tracker could lead to fundamental discoveries about endosomal transport mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Untying the Gordian KNOT: Unbiased Single Particle Tracking Using Point Clouds and Adaptive Motion Analysis

Bishop

Dutta

et al. 2021

J. Phys. Chem. A

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Achieving mechanistic understanding of transport in complex environments such as inside cells or at polymer interfaces is challenging. We need better ways to image transport in 3-D and better single particle tracking algorithms to determine transport that are not systemically biased toward any classical motion model. Here we present an unbiased single particle tracking algorithm: Knowing Nothing Outside Tracking (KNOT). KNOT uses point clouds provided by iterative deconvolution to educate individual particle localizations and link particle positions between frames to achieve 2-D and 3-D tracking. Information from prior point clouds fuels an independent adaptive motion model for each particle to avoid global models that could introduce biases. KNOT competes with or surpasses other 2-D methods from the 2012 particle tracking challenge while accurately tracking adsorption dynamics of proteins on polymer surfaces and early endosome transport in live cells in 3-D. We apply KNOT to study 3-D endosome transport to reveal new physical insight into locally directed and diffusive transport in live cells. Our analysis demonstrates better accuracy in classifying local motion and its direction compared to previous methods, revealing intricate intracellular transport heterogeneities.

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A Method of Feature Lines Generation of Cultural Relics Based on Point Clouds

Wang

Shen

et al. 2019

2019 Nicograph International (NicoInt)

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A Study of Feature Line Extraction and Closed Frame Structure of a Stone Tool from Measured Point Cloud

Cited by 3 publications

References 10 publications

Feature Line Extraction of Stone Tools Based on Mahalanobis Distance Metric

Feature Line Extraction of Stone Tools Based on Mahalanobis Distance Metric

Untying the Gordian KNOT: Unbiased Single Particle Tracking Using Point Clouds and Adaptive Motion Analysis

A Method of Feature Lines Generation of Cultural Relics Based on Point Clouds

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