| 000 -LEADER |
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| fixed length control field |
01340nam a2200169Ia 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20200308073525.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
190927s9999 xx 000 0 und d |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
T N352 2013 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Preferred name for the person |
Nepomuceno, Richie Mae G. |
| 245 #0 - TITLE STATEMENT |
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| Title |
Meat classification through back propagation |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of Publication |
Cebu City |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of Publisher |
CIT-U |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Date of Publication |
2013 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
This paper presented a method on how to classify the meat of the pig, cow and chicken through machine vision. This is useful especially to the children who are still learning the kinds of meat. And this is also useful in supermarkets when the meats are mixed together. The system will make the tedious work easier and faster. The method used in the system is neural network which is back propagation and image processing. I used back propagation in determining the image of the meat, by using the image pixels as input and setting a series of 0?s and 1?s as output. And then, after checking that the image is meat I used image processing to get the average intensity of red colour in the meat area. And after that I get the percentage of fat in the meat area using the HSI value. I compare the values of the two variables and classify them according to the value of the data?s. HSI value is also used in getting the region of the meat in the frame. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| -- |
000-099 |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Item type |
RESERVED BOOKS |
| Source of classification or shelving scheme |
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