bean counting hemina patel sai-ming law themis toache tony girardi
TRANSCRIPT
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bean counting
Hemina PatelSai-Ming LawThemis ToacheTony Girardi
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image processing
• an image is a 2 dimensional function, eg. f(x,y)
• image processing is the analysis, interpretation, and manipulation of images
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the problem
• picture acquisition
• filtering/thresholding algorithms
• shrinking/separating algorithms
• counting algorithms
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Why????• http://www.wellcome.ac.uk/en/wia/gallery.html?image=20• http://www.visualsunlimited.com/images/watermarked/899/899108.jpg• http://faculty.mc3.edu/jearl/ML/ml-5-2.htm• http://www.bone-net.de/textgut/ecoli.htm• http://www.nirgal.net/graphics/e_coli.jpg
sarcina lutea bacteria
e.coli
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first try
rgb gray scaled image
after thresholding after shrinking
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problems with our first try
problems with double counting
problems with connected beans
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more problems with our first try
Blurry images
Fragmented beans
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new counting algorithm
- check the waiting list for elements. -traverse the image until a certain pixel is found (in the waiting list)
- find the first pixel A and add it to the waiting list.
- Once A is in the waiting list we check its neighborhood for more elements and add them to the waiting list.
- After adding the elements of A’s neighborhood to the waiting list, we remove A from the waiting list, change its color to white and add it to the visited list.
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new counting algorithm cont.5. Since B is the first element in the Waiting List, we add
the neighbors of B are not in the list.
6. After that, we take B out from the list, and add it to the Visited List.
7. We follow the same procedures until the Waiting list is empty.
8. Then we add the size of the bean, i.e, total number
of elements in Visited List to the Size List.
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• works by finding the edge of each bean, and then repeatedly subtracting the outer edge from the bean
• Shrinking/separating algorithm needs good thresholding
new shrinking/separating algorithm
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new filtering and thresholding algorithm
• We used the difference in the red green and blue images to achieve separation of the beans
Grayscale Blue filtered image
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lentils1 2
3 4
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lentil results
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450400350300250200
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actual
avg. L3
&L1
pre
dic
ted
S 12.7646R-Sq 97.9%R-Sq(adj) 97.8%
Fitted Line Plotavg. L3&L1predicted = 8.150 + 0.9655 actual
450400350300250200
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5
0
actual
%ER
ROR
S 2.85340R-Sq 1.6%R-Sq(adj) 0.0%
Fitted Line Plot%ERROR = 4.485 - 0.004108 actual
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m&ms
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counting by colorBlue
Yellow
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m&m results
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500400300200100
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Actual _
Pre
dic
ted _
S 4.84658R-Sq 99.9%R-Sq(adj) 99.9%
Fitted Line PlotPredicted _ = 1.669 + 1.004 Actual _
500400300200100
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Actual _
%ER
ROR
S 0.973127R-Sq 1.2%R-Sq(adj) 0.0%
Fitted Line Plot%ERROR = 1.129 + 0.000823 Actual _
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rice1 2
3
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rice results
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900800700600500400300200
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actual
pre
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ted 2
255255
S 16.6006R-Sq 99.5%R-Sq(adj) 99.5%
Fitted Line Plotpredicted 2255255 = 3.98 + 0.9993 actual
900800700600500400300200
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actual
%ER
ROR
S 1.37320R-Sq 0.0%R-Sq(adj) 0.0%
Fitted Line Plot%ERROR = 2.057 - 0.000085 actual
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for the future• more testing of our algorithms• apply new filtering & separating techniques• apply our algorithms to new objects
counting red blood cells