Structure modeling and estimation of multiple resolvable group targets via graph theory and multi-Bernoulli filter

Automatica ◽  
2018 ◽  
Vol 89 ◽  
pp. 274-289 ◽  
Author(s):  
Weifeng Liu ◽  
Shujun Zhu ◽  
Chenglin Wen ◽  
Yongsheng Yu
Keyword(s):  
Graph Theory ◽  
Structure Modeling ◽  
Bernoulli Filter ◽  
Resolvable Group

scholarly journals Resolvable Group State Estimation with Maneuver Based on Labeled RFS and Graph Theory

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1307
Author(s):  
Weifeng Liu ◽  
Yudong Chi
Keyword(s):  
Graph Theory ◽  
State Estimation ◽  
Target Tracking ◽  
Finite Sets ◽  
Target Model ◽  
Group Members ◽  
Group Target ◽  
Random Finite Sets ◽  
Resolvable Group

In this paper, multiple resolvable group target tracking was considered in the frame of random finite sets. In particular, a group target model was introduced by combining graph theory with the labeled random finite sets (RFS). This accounted for dependence between group members. Simulations were presented to verify the proposed algorithm.

Graph Theory, Coding Theory and Block Designs

1975 ◽  
Author(s):  
P. J. Cameron ◽  
J. H. van Lint
Keyword(s):  
Graph Theory ◽  
Coding Theory ◽  
Block Designs

Topics in Chromatic Graph Theory

2009 ◽  
Keyword(s):  
Graph Theory

Emergence of Stimulus Equivalence Explained by Graph Theory

2009 ◽  
Author(s):  
Celso S. Oliveira
Keyword(s):  
Graph Theory ◽  
Stimulus Equivalence

APL and graph theory in dynamic systems analysis

1981 ◽  
Vol 128 (3) ◽  
pp. 85 ◽  
Author(s):  
C. Schizas ◽  
F.J. Evans
Keyword(s):  
Graph Theory ◽  
Dynamic Systems ◽  
Systems Analysis

Pattern Recognition in Histo-Pathology: Basic Considerations

1982 ◽  
Vol 21 (01) ◽  
pp. 15-22 ◽  
Author(s):  
W. Schlegel ◽  
K. Kayser
Keyword(s):  
Pattern Recognition ◽  
Graph Theory ◽  
Normal Tissue ◽  
Diagnostic Procedures ◽  
Minimal Distance ◽  
Malignant Growth ◽  
Colon Tissue ◽  
First Results ◽  
Tissue Structures ◽  
The Given

A basic concept for the automatic diagnosis of histo-pathological specimen is presented. The algorithm is based on tissue structures of the original organ. Low power magnification was used to inspect the specimens. The form of the given tissue structures, e. g. diameter, distance, shape factor and number of neighbours, is measured. Graph theory is applied by using the center of structures as vertices and the shortest connection of neighbours as edges. The algorithm leads to two independent sets of parameters which can be used for diagnostic procedures. First results with colon tissue show significant differences between normal tissue, benign and malignant growth. Polyps form glands that are twice as wide as normal and carcinomatous tissue. Carcinomas can be separated by the minimal distance of the glands formed. First results of pattern recognition using graph theory are discussed.

Sign in / Sign up

Export Citation Format

Share Document