Segmentation of astronomical images
Result description
Object detection is one of the most important procedures in astronomical imaging. This paper deals with segmentation of astronomical images based on random forrest classifier. We consider astronomical image data acquired using a photometric system with B, V, R and I filters. Each image is acquired in more realizations. All image realizations are corrected using master dark frame and master flat field obtained as an average of hundreds of images. Then a profile photometry is applied to find possible position of stars. The classifier is trained by B, V, R and I image vectors. Training samples are defined by user using ellipsoidal regions (20 selections for both classes: object, background). A number of objects and their positions are compared with astronomical object catalogue using Euclidean distance. We can conclude that the performance of the presented technique is fully comparable to other SoA algorithms.
Keywords
The result's identifiers
Result code in IS VaVaI
Alternative codes found
RIV/60461373:22340/14:43897284
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Segmentation of astronomical images
Original language description
Object detection is one of the most important procedures in astronomical imaging. This paper deals with segmentation of astronomical images based on random forrest classifier. We consider astronomical image data acquired using a photometric system with B, V, R and I filters. Each image is acquired in more realizations. All image realizations are corrected using master dark frame and master flat field obtained as an average of hundreds of images. Then a profile photometry is applied to find possible position of stars. The classifier is trained by B, V, R and I image vectors. Training samples are defined by user using ellipsoidal regions (20 selections for both classes: object, background). A number of objects and their positions are compared with astronomical object catalogue using Euclidean distance. We can conclude that the performance of the presented technique is fully comparable to other SoA algorithms.
Czech name
—
Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JA - Electronics and optoelectronics
OECD FORD branch
—
Result continuities
Project
GA14-25251S: Nonlinear imaging systems with spatially variant point spread function
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Applications of Digital Image Processing XXXVII
ISBN
978-1-62841-244-4
ISSN
0277-786X
e-ISSN
—
Number of pages
6
Pages from-to
"921722-1"-"921722-6"
Publisher name
SPIE
Place of publication
Bellingham
Event location
San Diego, California
Event date
Aug 17, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000344014100057
Basic information
Result type
D - Article in proceedings
CEP
JA - Electronics and optoelectronics
Year of implementation
2014