Topological Approximation Reconstruction of Sharp Feature Surfaces

AbstractIn quantum magnets, magnetic moments fluctuate heavily and are strongly entangled with each other, a fundamental distinction from classical magnetism. Here, with inelastic neutron scattering measurements, we probe the spin correlations of the honeycomb lattice quantum magnet YbCl3. A linear spin wave theory with a single Heisenberg interaction on the honeycomb lattice, including both transverse and longitudinal channels of the neutron response, reproduces all of the key features in the spectrum. In particular, we identify a Van Hove singularity, a clearly observable sharp feature within a continuum response. The demonstration of such a Van Hove singularity in a two-magnon continuum is important as a confirmation of broadly held notions of continua in quantum magnetism and additionally because analogous features in two-spinon continua could be used to distinguish quantum spin liquids from merely disordered systems. These results establish YbCl3 as a benchmark material for quantum magnetism on the honeycomb lattice.

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A New Triangle Mesh Simplification Method with Sharp Feature

2018 7th International Conference on Digital Home (ICDH) ◽

10.1109/icdh.2018.00063 ◽

2018 ◽

Cited By ~ 2

Author(s):

Zhuo Shi ◽

Kong Qian ◽

Ke Yu ◽

Xiaonan Luo

Keyword(s):

Mesh Simplification ◽

Triangle Mesh ◽

Sharp Feature

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Guided point cloud denoising via sharp feature skeletons

The Visual Computer ◽

10.1007/s00371-017-1391-8 ◽

2017 ◽

Vol 33 (6-8) ◽

pp. 857-867 ◽

Cited By ~ 10

Author(s):

Yinglong Zheng ◽

Guiqing Li ◽

Shihao Wu ◽

Yuxin Liu ◽

Yuefang Gao

Keyword(s):

Point Cloud ◽

Sharp Feature

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Optimizing B-Spline Surface Reconstruction for Sharp Feature Preservation

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) ◽

10.1109/ccwc47524.2020.9031263 ◽

2020 ◽

Author(s):

Daniel Lee ◽

Isabella Quan ◽

Chris Wu ◽

Jason Wu ◽

Dan Tamir ◽

...

Keyword(s):

Surface Reconstruction ◽

B Spline ◽

Sharp Feature ◽

Feature Preservation ◽

Spline Surface ◽

Sharp Feature Preservation

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Fine grained medical image fusion using type-2 fuzzy logic

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v14.i2.pp999-1011 ◽

2019 ◽

Vol 14 (2) ◽

pp. 999

Author(s):

Ramya H.R ◽

B K Sujatha

Keyword(s):

Fuzzy Logic ◽

Image Fusion ◽

Medical Image ◽

Objective Evaluation ◽

Medical Image Fusion ◽

Fine Grained ◽

Sharp Feature ◽

Sugeno Model ◽

Image Fusion Technique

<p>In recent years, many fast-growing technologies coupled with wide volume of medical data for the digitalization of that data. Thus, researchers have shown their immense interest on Multi-sensor image fusion technologies which convey image information based on data from various sensor modalities into a single image. The image fusion technique is a widespread technique for the diagnosis of medical instrumentation and measurement. Therefore, in this paper we have introduced a novel multimodal sensor medical image fusion method based on type-2 fuzzy logic is proposed using Sugeno model. Moreover, a Gaussian smoothen filter is introduced to extract the detailed information of an image using sharp feature points.Type-2 fuzzy algorithm is used to achieve highly efficient feature points from both the b images to provide high visually classified resultant image. The experimental results demonstrate that the proposed method can achieve better performance than the state-of-the- art methods in terms of visual quality and objective evaluation.</p>

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NEIGHBOR SUPPORT BASED SPEED FUNCTION FOR LEVEL SET METHOD

Journal of Biological System ◽

10.1142/s0218339010003676 ◽

2010 ◽

Vol 18 (spec01) ◽

pp. 149-158 ◽

Cited By ~ 1

Author(s):

XUESHU LIU ◽

YUTAKA OHTAKE ◽

HIROMASA SUZUKI

Keyword(s):

Level Set Method ◽

Level Set ◽

Normal Direction ◽

Mesh Segmentation ◽

Zero Level ◽

Sharp Feature ◽

Normal Diffusion ◽

Speed Function ◽

Made In

With a different speed function, Level Set Method has been widely applied to many applications. Generally speaking, speed function may depend on many factors, such as curvature, normal direction. In this paper, we discuss a novel speed function which is only determined by neighbors' support. With enough support, zero level set can move or stop. Otherwise, it must wait for a moment before a decision to move or stop is made. In addition, an algorithm based on normal diffusion is proposed to smooth the zero level set, which can preserve the sharp feature and round corner at the same time. Experimentally, the proposed method has been successfully used for interested objection segmentation and mesh segmentation.

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Recognizing Sharp Features of 2-D Shapes

International Journal of Electronics and Telecommunications ◽

10.2478/v10177-010-0020-5 ◽

2010 ◽

Vol 56 (2) ◽

pp. 153-156

Author(s):

Laxmi Gewali ◽

Joseph Scanlan

Keyword(s):

Feature Extraction ◽

Efficient Algorithm ◽

Shape Similarity ◽

Sharp Feature ◽

Processing Step

Recognizing Sharp Features of 2-D Shapes We present an efficient algorithm for recognizing and extracting sharp-features from complex polygonal shapes. The algorithm executes in O(n2) time, where n is the number of vertices in the polygon. Sharp-feature extraction algorithms can be useful as a pre-processing step for measuring shape-similarity between polygonal shapes.

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SHARP FEATURES EXTRACTION FROM POINT CLOUDS

International Journal of Image and Graphics ◽

10.1142/s0219467812500295 ◽

2012 ◽

Vol 12 (04) ◽

pp. 1250029

Author(s):

WUSHOUR SLAMU ◽

JUMING CAO ◽

XINHUI YAO

Keyword(s):

Mean Curvature ◽

Mean Curvature Flow ◽

Point Clouds ◽

Curvature Flow ◽

General Purpose ◽

Features Extraction ◽

Feature Points ◽

Sharp Feature ◽

Local Extreme ◽

Original Point

As sharp feature manipulation plays an important role in point clouds processing, a novel mean curvature flow-based framework for sharp feature extraction from point clouds is presented in this paper. That is, for input point clouds, a general purpose mean curvature flow-based point clouds smoothing operator is applied on them, thereby, obtaining a smoothing version of the original point clouds. The sharp feature points are labeled as points whose displacements between original point clouds and their smoothing version get local extreme. Implementation of our method on both synthesized and real scanned point clouds show that our methods are effective and robust for the purpose of sharp features extraction tasks.

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