Skip to main navigation
Skip to search
Skip to main content
VTT's Research Information Portal Home
Search content at VTT's Research Information Portal
Home
Profiles
Research output
Projects
Datasets
Research units
Research Infrastructures
Activities
Prizes
Press/Media
Impacts
Automated star-galaxy discrimination for large surveys
Filippo Cortiglioni
, Petri Mähönen
, P. Hakala
, Tapio Frantti
VTT Technical Research Centre of Finland
University of Oulu
University of Turku
Research output
:
Contribution to journal
›
Article
›
Scientific
›
peer-review
17
Link opens in a new tab
Citations (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Automated star-galaxy discrimination for large surveys'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Large-scale Survey
100%
Galaxies
100%
Good Enough
50%
Automatic Classification
50%
Classification Accuracy
50%
Survey Data
50%
Neural Network
50%
Training Set
50%
Self-organizing Map
50%
Fuzzy Classifier
50%
Hybrid Algorithm
50%
Data Selection
50%
Function Fitting
50%
Point Spread Function
50%
Object Classification
50%
Real-time Data Analysis
50%
Learning Vector Quantization
50%
Back-propagation Artificial Neural Network (BP-ANN)
50%
Algorithmic Complexity
50%
Self-paced Learning
50%
Neural Network Method
50%
Computer Science
Automatic Classification
100%
Neural Network Approach
100%
Hybrid Algorithm
100%
Vector Quantization
100%
back-propagation neural network
100%
point-spread function
100%
Classification Accuracy
100%
Neural Network
100%
INIS
surveys
100%
classification
100%
stars
100%
galaxies
100%
neural networks
75%
data
25%
hybrids
25%
size
25%
learning
25%
data analysis
25%
increasing
25%
accuracy
25%
availability
25%
algorithms
25%
fuzzy logic
25%
maps
25%
vectors
25%
quantization
25%
Physics
Neural Network
100%
Fuzzy System
33%
Vector Quantization
33%