robotic vision report

8/3/2019 Robotic Vision Report

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Vision

Introduction

The aim of this report is to use a piece of machine vision software 'NeuroCheck'

(version 6.0.38.0) to analysis a pattern of six sweets which are three different colours.

The software was needed to be set up so it showed up each of the three differentcolours.

Machine vision is the analysis of a digital image, which has been produced from the

light reflect from a physical object. Machine vision has been developed to get as

close to replicating human vision as possible.

A few examples of its advantage over using a human is:

y A lot faster at basic visual task's in a certain confined area

y Can be used 24 hours a day, 7 days a week.

y Can be used in an environment which is dangerous

Equipment

The equipment used is:

y NeuroCheck 6.0 - This is the software used, it is a powerful application

software for machine vision.

y Camera - This uses CCD (charge coupled devices), which the light energy to

charge the CCD and produce a signal.

y Computer with the following minimum specification: S

y

y Operating System:y Windows 7 (32-bit/64-bit), Vista (32-bit), or XP SP2 or higher (32-bit)

y Processor: 1.5 GHz (2.0 GHz multi-core recommended)

y System memory: 1 GB RAM (4 GB recommended)y Free space on hard drive: 2 GB on system partition

y Screen resolution: 1,024768

y Optical drive: CD-ROM or DVD

y Interface: USB or Parallel Port

Method

To be able to use the software properly a suitable image is needed. Using different

apparatus, focus, aperture and lighting the best image was extracted. The softwareconverts the colour image into a black and white one, the different colours in each

pixel are then placed onto the grey scale. The software recognises the greyscale as

black = 0 and white = 255.

The following images are screenshot of the images received:

Type Acceptable?Yes/No

Reason Picture


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Ring lightonly

No Picture too dark,Lens cannot focus,Red and black cannot easily bedistinguished.

SpotLightsOnly

No To much shadowin all directions,although colourscan clearly bedistinguished

LampsOnly

No Dark shadows

Ring lightsandLamps

No Dark shadows andshine from bottomleft sweet


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Ring lightanddiffuser

No Close to therequired picture,but the two darksweets are tooclosely matched.

Ring light,spot lightanddiffuser

Yes Picture is clear,colours are easilydistinguishableand there are noshadows present.Any small defects

e.g. shining, canbe removed by thesoftware.

Reasons for not choosing images

y Shadows - Corrupts image as: shadows are all the same colour, computer

can recognises it as part of sweet.

y Colours too similar - Computer may not be able to diguish between the

different sweets.

Settings and apparatus affecting image

y Diffuser - a paper tube which went over the sweets. It is used to

diffuse/spread out the light (soften the light). Regarding the sweets it had

eliminated the shadows.

y Aperture - A device which is located inside the camera behind the lens but infront of the CMOS image sensor. It is used to control the amount of light

entering the camera. It is controlled from outside the camera's body and has

a scale in increasing f-numbers where f3 lets in the most light and f16 lets in

the least amount of light. Basically It allows the picture to be lighter or darker.

y Focus - Adjusting the lens via an external rod to produce maximum clarity of

the image.


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The selected image chosen for processing was when the sweets which had the use

of the ring light, spot light and diffuser. This produced the best image, with no

shadows, good clarity and excellent the three shades of grey were easily

distinguishable. The only points which would need reprocessing were because the

sweets were embossed on the top this produced some shining and dark areas.


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After an image was taken it is then inserted into the vision software, it can then be

further processed to enhance it. The form of reprocessing we had chosen was to add

a filter to the image.

Image Filter

First the image is filtered, this simplifies the image by increasing/decreasing the lightand dark anomaly parts of the image.

This is a screenshot of the original (above) and after filter image (below). There are

several options for filtering but the one which gave the best results was 'erosion 3x3',

erosion works on the lighter end of the scale and causes the lighter areas of the

image to become darker. Which in this case made the light area's from the shiny

parts merge with the rest of the sweets grey level. The filtered image has been

simplified which the light shining of the embossed sweets has been dulled and

slightly more merged into the rest of the sweet.


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ROI

The last procedure which needs to be performed before each of the colours of

sweets can be processed is the selection of the ROI (regions of interest). This is

easily done (a simple drag and drop), but is very important to do so the software

knows where on the image to focus the processing.

Colour Processing

Now the setup of the image has been performed, the three different pair's of

coloured sweets can be processed. At the end of each of the processing the

computer will count the amount of each of the coloured sweets which is a quantity of

two, this will confirm that everything has been done correct.


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Black

To determine the black sweets, first the threshold is determined below is the

screenshot of this process. The top of the screenshot is a histogram and the lower

image is the real-time results of the threshold.

Determine threshold

Create ROIs by Thresholding

This is the next step and shows the software picking up the two black sweets, which

are highlighted in blue.


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Yellow

This procedure is the same as above.

Determine threshold


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Create ROI by Theshold

Compute Features

When the yellow sweets image has had the features computed, it shows there are

several parts with the same colour. The table in the screen shot shows the areas


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with the grey level. Because there are several areas highlighted the image needs to

be screened.

Screen ROI

This is an extra step to which allows a range of areas to be selected, in this case the

range was 7500 to 9500 pixles. This screenshot shows the updated processed

image.


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Count ROI

The the ROI now counted it confirms the quanity of yellow sweets as two.


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Red

This procedure is exactly the same for the yellow sweets

Determine threshold

Create ROI by Theshold


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Compute Featuers

Screen ROI


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Count ROI

This confirms that the quantity of red sweets is two.

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