bean counting hemina patel sai-ming law themis toache tony girardi

bean counting

Hemina PatelSai-Ming LawThemis ToacheTony Girardi

image processing

• an image is a 2 dimensional function, eg. f(x,y)

• image processing is the analysis, interpretation, and manipulation of images

the problem

• picture acquisition

• filtering/thresholding algorithms

• shrinking/separating algorithms

• counting algorithms

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

first try

rgb gray scaled image

after thresholding after shrinking

problems with our first try

problems with double counting

problems with connected beans

more problems with our first try

Blurry images

Fragmented beans

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.

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.

• 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

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

lentils1 2

3 4

lentil results

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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

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%ER

ROR

S 2.85340R-Sq 1.6%R-Sq(adj) 0.0%

Fitted Line Plot%ERROR = 4.485 - 0.004108 actual

m&ms

counting by colorBlue

Yellow

m&m results

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Actual _

Pre

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ted _

S 4.84658R-Sq 99.9%R-Sq(adj) 99.9%

Fitted Line PlotPredicted _ = 1.669 + 1.004 Actual _

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Actual _

%ER

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S 0.973127R-Sq 1.2%R-Sq(adj) 0.0%

Fitted Line Plot%ERROR = 1.129 + 0.000823 Actual _

rice1 2

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rice results

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actual

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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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%ER

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S 1.37320R-Sq 0.0%R-Sq(adj) 0.0%

Fitted Line Plot%ERROR = 2.057 - 0.000085 actual

for the future• more testing of our algorithms• apply new filtering & separating techniques• apply our algorithms to new objects

counting red blood cells