Automatic Eggplant Grading Machine -Present & Future- · PLC, amp board Antenna of DGPS Chisel...
Transcript of Automatic Eggplant Grading Machine -Present & Future- · PLC, amp board Antenna of DGPS Chisel...
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2013/10/9
Texture Ananlysis
Naoshi Kondo
Division of Environmental Science & Technology,
Graduate School of Agriculture, Kyoto University
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KYOTO UNIVERSITY 京都大学 2013/10/9
An energy flow from light source to
output of TV camera
Image grabber and/or PC
Illumination
TV camera
Object
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KYOTO UNIVERSITY 京都大学 2013/10/9
Cooccurrence matrix and textural features
Haralick, R.M., K. Shanmugam and Its'hak Dinstein. Textural features for image classification,
IEEE Transactions on systems, man, and cybernetics, Vol.SMC-3, No.6, 610-621.1973.
1
2 3
0 0
0 0
0 2
2
2 2
1
1 1
3
Gray level image
0 1 2 3
0
1
2
3
4 2 1 0
2 4 0 0
1 0 6 1
0 0 1 2
d=1, q =0
Distance and direction
d
q i j
j j
ASM: ΣΣ{p(i,j)} 2 i=0 j=0
n n
ΣΣ p(i,j)/{1+(i-j)2} IDM: i=0 j=0
n n
ΣΣ (i-j)2p(i,j) CON: i=0 j=0
n n
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KYOTO UNIVERSITY 京都大学
Measurement Box
Power source, PC,
Spectroscopy,
PLC, amp board
Antenna of DGPS
Chisel
(sensor
probe
housing)
※ Date: 2004. 11. 21 Depth: 150mm, Speed: About 30cm/ sec Cooperation with SHIBUYA MACHINERY CO.,LTD., TUAT
SAS1000 – Outline (Real time soil sensor)
Ready to sell!
Measurement
Nitrogen
MC
SOM
EC
pH
Compaction
+
Image data
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KYOTO UNIVERSITY 京都大学 2013/10/9
Illumination fiber
EC Electrode
Laser Displacement
sensor Color camera Condensed
fiber Ground surface
Soil flattener
Arrangement of equipments in chisel
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KYOTO UNIVERSITY 京都大学 2013/10/9
Under-ground-Images by the soil sensor
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KYOTO UNIVERSITY 京都大学
Samples of soil images under ground
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KYOTO UNIVERSITY 京都大学
a b c
d e f
g h i
(max{a,b,c,d,e,f,g,h,i}-min{a,b,c,d,e,f,g,h,i})×k
(a+b+c+d+e+f+g+h+I)/9
Textural analysis
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KYOTO UNIVERSITY 京都大学 2013/10/9
Machine vision for
fruit grading system
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KYOTO UNIVERSITY 京都大学 2013/10/9
Extracted features from images
Sizes Colors Shape Defects Maturity Dullness (Gloss) ……
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KYOTO UNIVERSITY 京都大学 2013/10/9
Texture on bioproducts
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KYOTO UNIVERSITY 京都大学
Orange fruit sweetness prediction
by human eyes
Color
Size
Shape
Texture
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KYOTO UNIVERSITY 京都大学
Assignment
Describe an image in which textural feature
extraction can be effective process for
discrimination, recognition or
understanding of objects in a page of PPT.
(except images you learnt in this class)
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