A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing

2019 ◽  
Vol 30 (2) ◽  
pp. 311-326
Author(s):  
Faruk Serin ◽  
Metin Erturkler
Keyword(s):  
Proximity Graph ◽  
Cell Graph

Approximate Top-k Inner Product Join with a Proximity Graph

2021 ◽  
Author(s):  
Hayato Nakama ◽  
Daichi Amagata ◽  
Takahiro Hara
Keyword(s):  
Inner Product ◽  
Proximity Graph

scholarly journals Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers

2021 ◽  
Vol 68 ◽  
pp. 101903
Author(s):  
Cheng Lu ◽  
Can Koyuncu ◽  
German Corredor ◽  
Prateek Prasanna ◽  
Patrick Leo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Hpv Status ◽  
Local Cell ◽  
Computational Pathology ◽  
Cell Graph

Lightweight preprocessing and fast query of geodesic distance via proximity graph

2018 ◽  
Vol 102 ◽  
pp. 128-138
Author(s):  
Shiqing Xin ◽  
Wenping Wang ◽  
Ying He ◽  
Yuanfeng Zhou ◽  
Shuangmin Chen ◽  
...  
Keyword(s):  
Geodesic Distance ◽  
Proximity Graph

Learning Hyperparameters in Efficient Spatial Model by Robotic Sensors

2019 ◽  
Author(s):  
Jinho Jeong ◽  
Soo Jeon ◽  
Jongeun Choi
Keyword(s):  
Mobile Robots ◽  
Learning Strategies ◽  
Spatial Model ◽  
Markov Random Fields ◽  
Spatial Models ◽  
Future Research ◽  
Gaussian Markov Random Fields ◽  
New Class ◽  
Future Research Directions ◽  
Proximity Graph

Abstract Recently, a new class of spatial models over a continuum domain that builds on hidden Gaussian Markov Random Fields (GMRFs) was proposed for resource-constrained networked mobile robots dealing with non-stationary physical processes. The hidden GMRF was realized with respect to a proximity graph over a surveillance region. In this paper, we investigate learning strategies based on the maximum likelihood (ML) and the maximum a posteriori (MAP) estimators to find the locational generating points for the spatial model so that mobile robots can efficiently make the prediction. Some promising simulation results and future research directions are discussed.

On the size of a random sphere of influence graph

1999 ◽  
Vol 31 (3) ◽  
pp. 596-609 ◽  
Author(s):  
T. K. Chalker ◽  
A. P. Godbole ◽  
P. Hitczenko ◽  
J. Radcliff ◽  
O. G. Ruehr
Keyword(s):  
Upper And Lower Bounds ◽  
Exponential Inequality ◽  
Nearest Neighbour ◽  
Expected Number ◽  
Spheres Of Influence ◽  
Influence Graph ◽  
Influence Graphs ◽  
Sphere Of Influence ◽  
Proximity Graph ◽  
Sharp Concentration

We approach sphere of influence graphs (SIGs) from a probabilistic perspective. Ordinary SIGs were first introduced by Toussaint as a type of proximity graph for use in pattern recognition, computer vision and other low-level vision tasks. A random sphere of influence graph (RSIG) is constructed as follows. Consider n points uniformly and independently distributed within the unit square in d dimensions. Around each point, Xi, draw an open ball (‘sphere of influence’) with radius equal to the distance to Xi's nearest neighbour. Finally, draw an edge between two points if their spheres of influence intersect. Asymptotically exact values for the expected number of edges in a RSIG are determined for all values of d; previously, just upper and lower bounds were known for this quantity. A modification of the Azuma-Hoeffding exponential inequality is employed to exhibit the sharp concentration of the number of edges around its expected value.

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