Machine VisionAcquisition of image data, followed by the processing and interpretation of these data by computer for some useful application like inspection, counting etc.

2D system◦ Most commonly using system.◦ For measuring dimensions of parts.

Verifying presence of components. Checking features of Flat or semi flat surfaces.

3D system◦ Only for special purpose

Application include 3D analysis of scenes.

Types of Machine vision System

Image acquisition and digitization Image processing and analysis Interpretation

Operational Functions of Machine Vision:-

What the hell is this?◦ It is nothing but capture the images or video

using a video camera (image acquisition is over now) then digitize the image using an ADC( Analog to digital converter) and store the image data for subsequent analysis.

Take ok….Camera ready….Action….

Image Acquisition and Digitization

Of course there is a camera for capturing video

Light sources for providing light

Analog to digital converter (ADC)

Components of Image Acquisition and Digitization

There are mainly two types of vision system they are:-

Binary System Gray scale system

Vision Systems

Vidicon Cameras

Solid-State Cameras

Types of 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 lighting◦ Back lighting◦ Side lighting◦ Structured lighting◦ Strobe lighting.

Illumination (Light source)

Front lighting.◦ Light source is located at the same side of the

camera.◦ Produces a reflected light from the object that

allow inspection of surface features.

Back lighting.◦ Light source is placed between behind the object

being viewed by the camera.◦ This create dark silhouette of the object that

contrasts sharply with the light background.◦ This type is used for inspect parts dimension and

distinguish between part outlines.

Silhouette

Back Lighting

Side lighting◦ Light source is placed at the side of the surface to

be illuminated.◦ For finding out surface irregularities, flaws,

defects on the surface.

Structured lighting◦ Projection of special light pattern onto the object.◦ Usually planer sheet of highly focused light are

used.

The above elevation differences are calculated by trigonometric relation

Strobe Lighting.◦ The scene is illuminated by short pulse of high

intensity light which causes moving object appear to be stationary.

◦ This is dangerous causing migraine, fizz to the operator…

Different techniques for image processing and analysis the image data in machine vision system.

Segmentation( consist of two different technique)

◦ Thresholding◦ Edge detection

Feature extraction

Image Processing and Analysis

Segmentation:- Indented to define separate region of interest within the image.◦ The two common segmentation techniques.

Thresholding Conversion of each pixel intensity level into a binary

value, representing black or white. There is a threshold value of intensity If the value of the pixel of the image is less than the

threshold value then the pixel value is Zero(Black) otherwise One( White).

Monalisa after thresholding

Edge detection Determines the location of boundaries between an

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

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

Monolisa 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 the number of squares in it.

Pattern recognition. Two common pattern recognition technique

are:-◦ Template matching◦ Feature weighting.

Interpretation

Pattern recognition◦ Recognizing the object◦ Comparing the image with predefined models or

standard values.◦ Template matching:-

Compare one or more feature of an image with the corresponding feature of model or template stored in computer memory.

Image is compared pixel by pixel. Disadvantage : very difficult to aligning the part in the

same position and orientation in front of the camera, to allow the comparison to be made with out complication in the image processing.

◦ Feature Weighting. Several features like area, length and perimeter are

combined into a single measure by assigning a weight to each feature according to the relative importance in the identifying the object.

The score of the object in the image is compared with the score of the image in the computer memory to achieve proper identification.

Inspection

Identification

Visual guidance and control

Application of Machine vision

Machine vision in inspection◦ 80% of inspection works in industries are done by

machine vision◦ Save lot’s of time

Dimensional measurement Dimensional gaging. Verification of the presence of components. Verification of hole location and number of holes. Detection of surface flaws and defects. Detection of flaws in a printed label.

Automation, Production system and computer integrated manufacturing by Mikell P Groover.

Reference