Image Processing Apllied to Agroindustry

8/13/2019 Image Processing Apllied to Agroindustry

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IMAGE PROCESSING APLLIEDTO AGROINDUSTRY


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OBJECTIIVES

Research techniques applied to agro industrial sector,supported by theories of artificial vision.

The development of technology oriented to an

improvement in the selection of fruits, in order to keepthe required standards of quality and add value to theproducts of the region.

Apply this techniques using MATLAB IMAGE

TOOLBOX.Achieve communication between the camera and

MATLAB in order to capture images and process its.


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BACKGROUND

Digital image:

It is a representation of a bi dimensional image using afinite number of point, called pels or pixeles, which are

represented by one or more numeric values. For monochromatic image, just one value represents the

pixel intensity (0 255).

For color image, uses 3 values, which represents Red,

Green and Blue colors (RGB) components.


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Digital image processing:

It is the manipulation of images, supported by acomputer, generally made in an automatic way andthose are based in algorithm designed carefully.

There are three levels of operation in image processing:

Low level: Noise reduction, contrast enhancement. Inputsand outputs are images.

Medium level: Edges, perimeter, areas, etc. Of images.

High level: analysis and interpretation of the content of the

scenes


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Color Analysis

Color images are represented by RGB that indicates aMxNx3 color pixels matrix. They can be seen as a stackof three grey scale images.

RGB models are based on Cartesian coordinates systemswhose axes represent Red, Green and Blue colors.


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Digital images representation

Bitmap Uses one or more bi dimensional pixel matrix

Vector

Uses drawing commands to represent a image

Binary Image

2D pixel matrix. . 0 means totally black and 1 meanstotally white.

Gray Scale image

2D pixel matrix. 8 bits per pixel. [0 255]. 0 means totally black.255 means totally white. Values between 0 and 255 mean lot ofgrey tones.


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Color Image

RGB:for each color channel (R G B), corresponds a 2D matrix,which each element keeps a 8 bit value, that indicates thequantity of Red, Green and Blue in that determined point, in a[0 255] scale.

Indexed color:for antique hardware that isnt able to show 16millions of colors simultaneously.


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Algorithms used

Size algorithm Calculates the width and height of the fruit.

We proceed binarizing the image in order to find the heightand width counting valid pixels in rows and columns.

Shape algorithm

Qualify the roundness of the fruit, independently of thephysics features.


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Shape algorithm f lowchart


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Color algorithm

Set up an histogram of the R G B components to analyze thequality of the fruit

Color algorithm flow

chrat


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Testing

At first we proceed to obtain a controlled environment,to isolate the fruit from external noise. In addition, weset up a light source in order to have a good image.

The ground might be white color


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Distance testing:

The camera is set up at 52.5 cm high from ground.

Capture image.


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Permetro Altura Ancho Permetro Altura Ancho Permetro Altura Ancho

1 33 11,855 8,687 1627,6 564 400 49,32121 47,57486 46,04582

2 32,8 11,237 8,73 1584,1 543 400 48,29573 48,32251 45,81901

3 34 12,826 9,026 1704,5 609 408 50,13235 47,48168 45,202754 31,9 11,307 8,288 1540 526 385 48,27586 46,51985 46,4527

5 37 13,929 9,529 1833,5 654 449 49,55405 46,9524 47,11932

6 26 9,004 7,412 1270 416 344 48,84615 46,20169 46,41123

7 31,5 11,117 8,641 1528,6 516 398 48,52698 46,4154 46,059488 32,5 11,625 8,622 1608,1 552 403 49,48 47,48387 46,7409

49,08368 47,21704 46,23535

Medida computarizada [pxeles]Medida real [cm] Relacin

PROMEDIO

PROMEDIO GENERAL

47,21703965


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y = -0.0037x + 49.071R = 0.0002

48

48.5

49

49.5

50

50.5

0 1 2 3 4 5 6 7 8 9

Relacin Permetro


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y = -0.1617x + 47.847R = 0.3012

45

45.5

46

46.5

47

47.5

48

48.5

1 2 3 4 5 6 7 8

Relacin Altura


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y = 0.1233x + 45.676R = 0.2638

44

44.5

45

45.5

46

46.5

47

47.5

1 2 3 4 5 6 7 8

Relacin Ancho


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Color testing


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Configuracin de la cmara imaqhwinfo('winvideo')

ans =

AdaptorDllName: [1x81 char]

AdaptorDllVersion: '4.5 (R2013a)'

AdaptorName: 'winvideo'

DeviceIDs: {[1] [2]}

DeviceInfo: [1x2 struct]


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info=imaqhwinfo('winvideo',1)

info =

DefaultFormat: 'M420_1280x720'

DeviceFileSupported: 0 DeviceName: 'Microsoft LifeCam Studio'

DeviceID: 1

VideoInputConstructor: 'videoinput('winvideo', 1)'

VideoDeviceConstructor: 'imaq.VideoDevice('winvideo',1)'

SupportedFormats: {1x36 cell}


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>> info.SupportedFormats

ans =

Columns 1 through 5

'M420_1280x720' 'M420_160x120' 'M420_176x144' 'M420_1920x1080' 'M420_320x240'

Columns 6 through 10

'M420_352x288' 'M420_424x240' 'M420_640x360' 'M420_640x480' 'M420_800x448'


'M420_800x600' 'M420_960x544' 'MJPG_1280x720' 'MJPG_160x120' 'MJPG_176x144'


'MJPG_1920x1080' 'MJPG_320x240' 'MJPG_352x288' 'MJPG_432x240' 'MJPG_640x360'


'MJPG_640x480' 'MJPG_800x448' 'MJPG_800x600' 'MJPG_960x544' 'YUY2_1280x720'


'YUY2_160x120' 'YUY2_176x144' 'YUY2_1920x1080' 'YUY2_320x240' 'YUY2_352x288'


'YUY2_424x240' 'YUY2_640x360' 'YUY2_640x480' 'YUY2_800x448' 'YUY2_800x600'

Column 36

'YUY2_960x544'


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Image Processing Apllied to Agroindustry

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