Transcript Automated detection of filaments from Hα full disk images

Automated detection of filaments
on full disk H images
EGSO WP5
Nicolas Fuller and Jean Aboudarham
Meudon Observatory / LESIA
October 2003
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Automated Detection of Filaments / NF & JA 2003
Original image
Standardization
Cleaning
Seeds detection
Region growing
Shape description
Catalogue parameters
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Automated Detection of Filaments / NF & JA 2003
Cleaning Process
Darkening
Dust lines
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Automated Detection of Filaments / NF & JA 2003
Cleaning process / Intensity normalization 1
• Need to compute the slow variations of the background
• Use of a large median filter on a resized image
• The first approximation is influenced by large bright plages and large filaments
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Resize the image I to a smaller scale -> Is
Apply a median filter with a large window to Is
Resize to original scale (-> B) and subtract from I -> I’
From I’define 2 thresholds to roughly locate filaments and bright plages
Replace their value with the corresponding values in B -> I”
Apply step 1 to 3 to I” and get the final background and the normalize image
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Cleaning process / Intensity normalization 2
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Cleaning process / Dust lines removal 1
Need to compute a binary image with most of the
line points set to 1 and the background to 0 :
• Threshold
• Thinning morphological operator
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Automated Detection of Filaments / NF & JA 2003
Cleaning process / Dust lines removal 2
Original
Threshold
Thinning
Hough
transform
Threshold
Hough
backprojection
Line pixels
locations
Pixels values
correction
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Image enhancement
To enhance the image sharpness we use a Laplacian filter
• Filaments contours are better defined
• Allows to detect the thinnest parts of the filaments more efficiently
g(x,y) = f(x,y) –2f(x,y) where 2f = 2f/x2 + 2f/y2
Digital implementation:
Before and after enhancement
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Automated Detection of Filaments / NF & JA 2003
Region Growing
Definition: “procedure that groups pixels into larger regions based on
predefined criteria. It starts with a set of ‘seed’ points and from these grow
regions by appending to each seed those neighboring pixels that have
properties similar to the seed”
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Region Growing / Seeds detection
To find the seed points we apply a windowed threshold:
The pixels statistics in each window (200*200) are computed and the
threshold is given by: Twin = Mwin –  x win
M : Mean
 : constant
 : standard deviation
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Region Growing
For each seed we define an intensity range which is a criteria to append
connected pixels to the seed: [ 0, Tbr ]
where Tbr = Mbr –  x br
( br stands for Bounding Rectangle )
A minimum region size is also defined
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Region growing / BBSO
The process has been tested on other H full disk observations :
 Big Bear Solar Observatory example
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Shape analysis / Morphological operators
Morphological closing
Morphological thinning / pruning
Chain code
direction numbers
EGSO
Skeleton  Length/centre/Chain Code…
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Parameters : examples
GRAV_C_CAR_LAT
GRAV_C_CAR_LON
BRPIX_X_LL
BRPIX_Y_LL
BRPIX_X_UR
BRPIX_Y_UR
SAMPLECOUNT
AREA
SKE_LEN_DEG
ELONG
MEAN_INT_RATIO
FEAT_MAX_INT
FEAT_MIN_INT
FEAT_MEAN_INT
ENC_MET
COD_PIX_X
COD_PIX_Y
COD_SKE_PIX_X
COD_SKE_PIX_Y
SKE_CHAIN
BND_CHAIN
DOUBLE
DOUBLE
DOUBLE
DOUBLE
DOUBLE
DOUBLE
LONG
DOUBLE
DOUBLE
DOUBLE
DOUBLE
DOUBLE
DOUBLE
DOUBLE
STRING
DOUBLE
DOUBLE
DOUBLE
DOUBLE
STRING
STRING
-18.297280
337.64961
562.00000
418.00000
574.00000
430.00000
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2.8334533
2.254509
0.90625000
0.83272228
978.00000
689.00000
869.36206
'CHAINCODE'
572.00000
417.00000
574.00000
418.00000
'33332233334343'
'00123322222234433443444566707777677076'
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