Image processing


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Image processing

  1. 1. Vision system (image processing)By: karim ahmed abuamu
  2. 2. Image RepresentationA digital image is a representation of a two-dimensional image as a finite set of digital values, called picture elements or pixelsThe image is stored in computer memory as 2D array of integersDigital images can be created by a variety of input devices and techniques:  digital cameras,  scanners,  coordinate measuring machines etc.
  3. 3. Representation of Digital Images
  4. 4. Types of ImagesDigital images can be classified according to number and nature of those samples  Binary  Grayscale  Color
  5. 5. Binary ImagesA binary image is a digital image that has only two possible values for each pixelBinary images are also called bi-level or two-levelBinary images often arise in digital image processing as masks or as the result of certain operations such as segmentation, thresholding.
  6. 6. Grayscale Image Binary Image
  7. 7. Grayscale ImagesA grayscale digital image is an image in which the value of each pixel is a single sample.Displayed images of this sort are typically composed of shades of gray, varying from black at the weakest intensity to white at the strongest.The values of intensity image ranges from 0 to 255.
  8. 8. Grayscale Image
  9. 9. True color imagesA true color image is stored as an m-by-n-by-3 data array that defines red, green, and blue color components for each individual pixel.The RGB color space is commonly used in computer displays
  10. 10. True color Image
  11. 11. Image segmentationIn computer vision, image segmentation is the process of partitioning adigital image into multiple segments (sets of pixels, also known as superpixels).The goal of segmentation is to simplify and/or change the representation of an imageinto something that is more meaningful and easier to analyze.Image segmentation is typically used to locate objects and boundaries (lines, curves,etc.) in images. More precisely, image segmentation is the process of assigning alabel to every pixel in an image such that pixels with the same label share certainvisual characteristics.
  12. 12. Histograms o A tool that is used often in image analysis .o The (intensity or brightness) histogram shows how many times a particular grey level appears in an image. o For example, 0 - black, 255 – white. 7 0 1 1 2 4 6 5 2 1 0 0 2 4 3 5 2 0 0 4 2 1 1 1 2 4 1 0 0 1 2 3 4 5 6 image histogram
  13. 13. Histogram FeaturesAn image has low contrast when the complete range of possiblevalues is not used. Inspection of the histogram shows thislack of contrast.
  14. 14. Histogram Equalization
  15. 15. Thresholding (image processing)Threshold converts each pixel into black, white or unchanged depending onwhether the original color value is within the threshold range.Thresholding is usually the first step in any segmentationSingle value thresholding can be given mathematically as follows: 1 if f ( x, y ) > T g ( x, y ) =  0 if f ( x, y ) ≤ T
  16. 16. Imagine a poker playing robot that needs to visually interpret the cards inits hand Original Image Thresholded Image
  17. 17. If you get the threshold wrong the results can be disastrous Threshold Too Low Threshold Too High
  18. 18. Basic Global Thresholding Based on the histogram of an image Partition the image histogram using a single global threshold The success of this technique very strongly depends on how well the histogram can be partitionedThe basic global threshold, T, is calculated as follows: 1. Select an initial estimate for T (typically the average grey level in the image) 2. Segment the image using T to produce two groups of pixels: G1 consisting of pixels with grey levels >T and G2 consisting pixels with grey levels ≤ T 3. Compute the average grey levels of pixels in G1 to give μ1 and G2 to give μ2
  19. 19. 4. Compute a new threshold value: µ1 + µ 2 T= 2 5. Repeat steps 2 – 4 until the difference in T in successive iterations is less than a predefined limit T∞This algorithm works very well for finding thresholds when the histogram is suitable
  20. 20. Thresholding Example 1
  21. 21. Thresholding Example 2
  22. 22. Problems With Single Value ThresholdingSingle value thresholding only works for bimodal histogramsImages with other kinds of histograms need more than a single threshold
  23. 23. Single value thresholding only works for bimodal histogramsImages with other kinds of histograms need more than a single threshold
  24. 24. Thank You