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A Tutorial On Ip 1
 

A Tutorial On Ip 1

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A Tutorial On Ip 1 A Tutorial On Ip 1 Presentation Transcript

  • February 1st – 4th ,2007
  • What is Image Processing
    • Image Processing is a tool for analyzing image data in all areas of natural science.
    • It is concerned with extracting data from real-world images.
    • Differences from computer graphics is that computer graphics makes extensive use of primitives like lines, triangles & points. However no such primitives exist in a real world images.
  • Typical Applications of IP
    • Automated visual inspection system. Checking of objects for defects visually.
    • Satellite Image Processing.
    • Classification (OCR), identification (Handwriting, finger prints) etc.
  • Components of IP system
    • Camera, Scanner, other image acquisition software.
    • PC or workstation or DSP kit.
    • Software to run on the hardware platform.
    • To process an image, a representation or model is necessary.
    • An image is a spatial distribution of irradiance in a plane.
  • Possible representation of Images
    • Matrix
    • Quadtrees
    • Chains
    • Pyramid
    • Of the four, matrix is the most general. The other three are used for special purposes. All these representations must provide for spatial relationships.
  • Matrix Representation
    • Computers cannot handle continuous images but only arrays of digital numbers.
    • So images are represented as 2-D arrays of points (2-D matrix)
    • A point on this 2-D grid (corresponding to the image matrix element) is called PIXEL (picture element)
    • It represents the average irradiance over the area of the pixel.
  • Types of Images
    • Color Images :
    • Each pixel value has three components. Red, Green, Blue. Each component can take a value of 0-255. In computer memory it is stored as a 32 bit value with bits 0-7 storing Blue, 8-15 storing Green, 16-23 storing Red, 24-31 storing alpha or left blank.
  • Types of Images(contd..1)
    • Grey Scale Images :
    • Each Pixel value has only one component, Grayscale value. This represents the brightness on a 0-255 scale. It is also stored in memory as 32 bit with
    • R=G=B. But for our purposes we can use a single uchar and convert it to ulong on the fly.
  • Types of Images(contd..2)
    • Binary Images :
    • Each pixel can take one of the two values 0 and 1. so a pixel is either ON or OFF. Images which are easiest to work with
    • We recommend using these Images.
  • Concept of Neighbourhoods
    • A Neighbourhood N of a pixel is a subset of the image which satisfies some distance relation with the pixel.
    • Euclidean distance
    • L euclidean = √(x 1 -y 1 ) 2 +(x 2 -y 2 ) 2
    • City Block distance
    • L city = |(x 1 -y 1 )|+|(x 2 -y 2 )|
    • Chess board distance
    • L chessboard = min (|x 1 -y 1 |, |x 2 -y 2 |)
  • Concept of Neighbourhoods(contd…)
    • Von Neumann Neighbourhood
    • Nvon(x) = { y : Lcity (y,x) =1)
    • Moore Neighbourhood
    • Nmoore(x) = {y : Lchess(y,x) =1 )
  • Image Preprocessing
    • First step in most IP applications.
    • Used to remove noise in the input image
    • Examples are median filtering, averaging, contrast enhancement etc.
  • Image Preprocessing(contd…)
    • Median Filtering
    • F is an image.
    • F’ is an blank image.
    • F’(x) = Median {F(y)| y € N(x) }
    • x=(x1,x2) , y= (y1,y2)
    • F’(x) = Median(F(x,y+1),F(x,y-1),F(x-1,y),F(x,y-1))
    • Mean Filtering
    • F’(x) = Mean {F(y)| y € N(x) }
    • F’(x) = ¼ (F(x,y+1),F(x,y-1),F(x-1,y),F(x,y-1))
  • Image Preprocessing(contd…)
    • Edge Detection
    • Almost all edge detection algorithms are based on some form of differentiation.
    • Simplest Algorithm:
    • F(x 1 ,x 2 ) = F(x 1 +1,x 2 ) – F(x 1 ,x 2 ) Horizontal Gradient
  • Image Preprocessing(contd…)
    • F(x 1 ,x 2 ) = F(x 1 ,x 2 +1) – F(x 1 ,x 2 ) Vertical Gradient
    • F(x 1 ,x 2 ) = |F(x 1 +1,x 2 ) –F(x 1 ,x 2 )|+
    • |F(x 1 ,x 2 +1) – F(x 1 ,x 2 )|
    • Other Possible algorithms: Sobel, canny, Frei-Chen, Crack, Roberts, Prewitt etc.
  • Segmentation:
    • The process of checking each individual pixel to see whether it belongs to an object of interest or not.
    • After segmentation, it is known which pixel belongs to which object.
    • The next step is image analysis & recognition, using the shape of the image.
  • Segmentation(contd…)
    • Simplest Segmentation: Pixel Based
    • if (F(x)>T)
    • F’(x)=1 else
    • F’(x)=0.
    • Where F(x) is a gray scale image and F’(x) is a binary image of same dimensions.
  • Segmentation(contd…)
    • Connected Components segmentation.
    • Let a be a binary image of mxn.
    • Let b, c, d be normal real valued images.
    • Algo: Let d (i, j) = i*n+j 0 ≤ I ≤ m-1, 0 ≤ j ≤ n
    • Let b=0
    • c=d.a
    • While b!=c
    • b=c
    • c=(b ^ N).a
    • end
  • Segmentation(contd…)
    • Selecting the threshold
    • Usually selected interactively.
    • If is the mean & σ is the variance of all the pixel values.
    • Then μ +k σ (1 ≤ k ≤ 3) gives good results. Where the background is much larger than the object.
    • Other methods of dynamic thresholding exist.
  • Morphology
    • Operations on the size and shape of the object.
    • Performed on binary images
  • Basic Operations
    • Erosion
    • G-M ={ p: M p c= G }
    • G: Set of all pixels of the matrix which are non zero.
    • M p : Neighbourhood operator centered at point P (here called a structuring element)
  • Basic Operations(contd…)
    • Dilation
    • G+M ={ p: M p ∩G ≠ Φ }
    • Elementary operators from which other more complex operators can be built.
    • Although they belong to the family of the image analysis operators, it can also be used as low operator to remove noise.
  • Basic Operations(contd…)
    • Composite Morphological Operators
    • Opening
    • G ° M = (G – M ) + M
    • Closing
    • G • M = (G + M) – M
    • Salt and Pepper Noise Removal
    • (G ° M) • M.
    • Opening removes all objects which at no point completely enclose the structure element, but it does not shrink the objects which are larger than the dia of the structuring element.
    • Ideal for removing thin lines.
    • Closing operator closes small holes and cracks without general enlargement.
    Basic Operations(contd…)
    • Thank you…