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Seminar I on Agricultural Process Engineering 農産 …...Seminar I on Agricultural Process...
Transcript of Seminar I on Agricultural Process Engineering 農産 …...Seminar I on Agricultural Process...
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Seminar I on Agricultural Process Engineering農産加工学演習 I
No.6
農学研究科 地域環境科学専攻農学研究科 地域環境科学専攻
近藤 直・清水 浩近藤 直・清水 浩
Naoshi Kondo, Hiroshi ShimizuDivision of Environmental Science & Technology,
Graduate School of Agriculture, Kyoto University
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KYOTO UNIVERSITY京都大学
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Applications as robotic eyes
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Strawberry harvesting robot
Color conversionPreprocessingPreprocessing
BinarizationBinarization
Processing ofProcessing ofbinary, gray levelbinary, gray levelimagesimages
Feature extractionFeature extraction
RecognitionRecognition
3 3 D understandingD understanding
Software of machine vision as robotic eyes
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Stereo imaging for Depth measurement for 3 D reconstruction
Active stereo visionPassive stereo vision
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Stereo geometryScene pointP
Z
Xf f
pL pR
b
xL xR
CL CR
bf
: Baseline: Focal length: disparity: P’s x coordinate on left camera: P’s x coordinate on right camera: Lateral offset: Distance from camera
XZ
d
Identical parallel cameras
xL
xR
d = ‐ =Z
f
Zf=
XxL
Zf=
xR +
,
d is proportional to f and binversely proportional to Z
Image plane
xLxR
X b
b
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Area-based stereo matching(use of larger image regions (or areas) that containenough information to yield unambiguous matches)Feature-based stereo matching(Feature extraction by color or edge detection and dealwith only points that can be matched unambiguoisly)
Correspondence problem
Occlusion problem, Ambiguous matching
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1) Kise M., et al. : A Stereovision-based Crop Row Detection Method for Tractor-automatedGuidance, Biosystems Engineering, 90(5) 357-367 (2005).
Stereo cameraStereo camera mounted tractor 1)
Area-based stereo vision
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Disparity image(darker pixel is farther)
Image size: 320 X 240Mask size m: 25 X 25d’= 0~32 (setting value)
E(d’) = Σ Σ |IL(x+i, y+j) - IR(x+i+d’, y+j)|
L R
i=-m/2
i=m/2 j=m/2
j=-m/2
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Feature-based stereo vision
Dx
Dy
b
P1
P2
Px d
Y
X
Dy=d b/(P2-P1) Dx=Px Dy/d Dz=Py Dy/d
Q1: Describe how we can getQ1: Describe how we can get Dy Dy==d bd b/(/(PP22-P-P11))
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Left Right
Strawberry fruit images from stereo vision
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Left Right
Matching criteria: Horizontal level and strawberry sizeshould be almost equal
Matching on binary images
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Cluster of Strawberry fruits
1
2
3
4
56
56
4
3
2
Left Right
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1
2
3
4
56
5
6
4
3
2
Left Right
No 1: out of view in right imageNo.3: occluded on right imageNo.4: different immature parts from different angle
Un-Matched fruits (No.1, 3, 4)
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Depth measurement by use of differential object size
Dy=P1 L/(P2-P1)Dx=P2 Dy/d
Dy=L√Na1/(√Na2‐√Na1)
Dy= Nl1L/(Nl2‐Nl1)
P1
DyL
Dx
P2
d
DyL
Na1
Nl1
Na2
Nl2
Nai: area of object on image Nli : length of object on image
Q2:Describe how we can getQ2:Describe how we can get Dy= Nl1L/(Nl2‐Nl1)
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Na1: pixel numberrecognizing fruit
at position P1 at position P2
Actual images from camera attached to manipulator end
Dy=L√Na1/(√Na2‐√Na1)
Na2: pixel numberrecognizing fruit
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3 D image from active range finder
Operating principleOperating principle(Time of flight)(Time of flight)
TransmitterReceiver
Rotating mirror
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3 dimensional shape recognition
Phyllotaxis
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Machine vision forfruit grading system
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Extracted features from images
SizesColorsShapeDefectsMaturity Dullness (Gloss)……
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An apple fruit with discoloration
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Color component images
R G B
G‐BR‐BR‐G
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FFT
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Internal defects detection by X-ray CT
Peach with
Split-pits
Moth suckeddefect
Rotten core
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Experimental result(sprit-pit of peach)
(a) Appearance (b) Cut sample (Sprit-pit)
(d) Top view(c) Side view
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Transparent ImageOutput Voltage: 50keV
Y=250
Intersection at Y=250
Gra
y Le
vel
CT Image
Split-pit
Peach with Split-pits
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X ray image
X ray GeneratorMonochromeTV camera
Scintillator
Rotten core onion
Hollow potatoes
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X-ray images of orange fruits
Empty portion
Top view Side view
Empty portion
Rind-puffingfruit
Normalfruit
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Ultra violet image
Visible camera
UV camera
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Review
Describe what was the phenomenon Describe what was the phenomenon ““fluorescencefluorescence””..
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Rotten part
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Fluorescence image
Fluorescence reaction
Rotten part
Color images fluorescence images
(365nm exciting light)
Rotten part
UV LEDs
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Infrared
Light
Rotten portion
Transmitted image
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Texture on bioproducts
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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 00 00 2
22 2
11 1
3Gray level image
0 1 2 30123
4 2 1 02 4 0 01 0 6 10 0 1 2
d=1, θ =0
Distance and direction
dθ
i j
jj
ASM:ΣΣ{p(i,j)} 2i=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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Measurement Box Power source, PC,
Spectroscopy,PLC, amp board
Antenna of DGPS
Chisel(sensorprobe
housing)
※ Date: 2004. 11. 21 Depth: 150mm, Speed: About 30cm/ secCooperation 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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Illumination fiber
EC Electrode
Laser Displacementsensor Color camera
CondensedfiberGround surfaceGround surface
Soil flattener
Arrangement of equipments in chisel
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Under-ground-Images by the soil sensor
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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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USAD-ARS Imaging Research TeamUSAD-ARS Imaging Research Team
RDA 2004 Symposium October 13, 2004
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Common ApertureCamera
Carcass
IntelligentBird Washer
Method & System for Intelligent Washing ofFecal & Ingesta Contaminants
RDA 2004 Symposium October 13, 2004
Multispectral imaging
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KYOTO UNIVERSITY京都大学
AgriculturalAgriculturalProcessProcessEngineeringEngineeringLaboratoryLaboratory
Machine vision for seedling production
Cutting sticking robot Transplanting robot
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KYOTO UNIVERSITY京都大学
AgriculturalAgriculturalProcessProcessEngineeringEngineeringLaboratoryLaboratory
A fruit grading robot with machine vision
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KYOTO UNIVERSITY京都大学
AgriculturalAgriculturalProcessProcessEngineeringEngineeringLaboratoryLaboratory
Assignment
Describe how you may apply the imagingDescribe how you may apply the imagingtechnologies to your own research project.technologies to your own research project.
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KYOTO UNIVERSITY京都大学
AgriculturalAgriculturalProcessProcessEngineeringEngineeringLaboratoryLaboratory
For improving my lecture
Tell me anything (teaching techniques) I canTell me anything (teaching techniques) I canimprove for your understanding in myimprove for your understanding in mylecture. (For example, small voice, too fastlecture. (For example, small voice, too fastPPT page changing, hard to see letters onPPT page changing, hard to see letters onblackboard, more explanation, use moreblackboard, more explanation, use moreblackboardblackboard……..)..)