1 / 23

Machine Vision

Machine Vision. Acquisition of image data, followed by the processing and interpretation of these data by computer for some useful application like inspection, counting etc. Types of Machine vision System. 2D system Most commonly using system. For measuring dimensions of parts.

Télécharger la présentation

Machine Vision

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.


Presentation Transcript

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

  2. Types of Machine vision System • 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.

  3. Operational Functions of Machine Vision:- • Image acquisition and digitization • Image processing and analysis • Interpretation

  4. Image Acquisition and Digitization • 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….

  5. Components of Image Acquisition and Digitization • Of course there is a camera for capturing video • Light sources for providing light • Analog to digital converter (ADC)

  6. Vision Systems There are mainly two types of vision system they are:- • Binary System • Gray scale system

  7. Types of CAMERAS • Vidicon Cameras • Solid-State Cameras

  8. Illumination (Light source) The scene captured by the vision camera must be well illuminated and the illum ination must be constant over time • There are mainly five categories of lighting systems. • Front lighting • Back lighting • Side lighting • Structured lighting • Strobe lighting.

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. 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… 

  14. Image Processing and Analysis 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

  15. 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

  16. 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

  17. 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. 

  18. Interpretation • Pattern recognition. • Two common pattern recognition technique are:- • Template matching • Feature weighting.

  19. 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.

  20. 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.

  21. Application of Machine vision • Inspection • Identification • Visual guidance and control

  22. 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.

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

More Related