Machine Vision3

8/7/2019 Machine Vision3

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Acquisition of image data, followed by the processingand interpretation of these data by computer for

some useful application like inspection, counting etc.



` 2D systemM ost commonly using system.For measuring dimensions of parts.x Verifying presence of components.x Checking features of Flat or semi flat surfaces.

` 3 D systemO nly for special purposex Application include 3 D analysis of scenes.



` Image acquisition and digitization` Image processing and analysis` Interpretation



` W hat the hell is this?It is nothing but capture the images or video using avideo camera (image acquisition is over now) thendigitize the image using an A D C( Analog to digitalconverter) and store the image data for subsequentanalysis.

Take ok«.Camera ready«.Action«.



` O f course there is a camera for capturing video

` L ight sources for providing light

` Analog to digital converter (A D C)



There are mainly two types of vision system theyare:-

` Binary System` Gray scale system



` V idicon Cameras

` Solid-State Cameras



The scene captured by the vision cameramust be well illuminated and the illumination must be constant over time` There are mainly five categories of lighting

systems.Front lightingBack lighting

Side lightingStructured lightingStrobe lighting.



` Front lighting.L ight source is located at the same side of the camera.Produces a reflected light from the object that allowinspection of surface features.



` Structured lightingProjection of special light pattern onto the object.

Usually planer sheet of highly focused light are used.

The above elevation differences are calculated bytrigonometric relation



` Strobe L ighting.The scene is illuminated by short pulse of high intensitylight which causes moving object appear to be stationary.This is dangerous causing migraine, fizz to theoperator«



D ifferent techniques for image processing andanalysis the image data in machine vision system.

` Segmentation( consist of two different

technique)ThresholdingEdge detection

`

Feature extraction



` Segmentation:- Indented to define separate regionof interest within the image.

The two common segmentation techniques.x Thresholding

x Conversion of each pixel intensity level into a binary value,representing black or white.

x There is a threshold value of intensityx I

f the value of the pixel of the image is less than the thresholdvalue then the pixel value is Zero(Black) otherwise O ne( W hite).

M onalisa after thresholding



Edge detectionx D etermines the location of boundaries between an object

and its surroundings in an image.x This is accomplished by identifying the contrast in light

intensity that exists between adjecent pixels at the border of the objects.

M onolisa after edge detection



` Feature extraction.Used for extracting features like area, length, width,diameter, perimeter from the image.

The area of the leaf can be calculated by counting thenumber of squares in it.



` Pattern recognition.` Two common pattern recognition technique are:-

Template matching

Feature weighting.



` Pattern recognitionR ecognizing the objectComparing the image with predefined models or standardvalues.

Template matching:-x Compare one or more feature of an image with the

corresponding feature of model or template stored incomputer memory.

x Image is compared pixel by pixel.x D

isadvantage : very difficult to aligning the part in the sameposition and orientation in front of the camera, to allow thecomparison to be made with out complication in the imageprocessing.



` Inspection

` Identification

` V isual guidance and control



` M achine vision in inspection80% of inspection works in industries are done bymachine visionSave lot¶s of timex D imensional measurementx D imensional gaging.x Verification of the presence of components.x Verification of hole location and number of holes.x

D etection of surface flaws and defects.x D etection of flaws in a printed label.



` Automation, Production system and computer integrated manufacturing by M ikell P Groover.

Machine Vision3

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