Outline For Image Processing

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Outline For Image Processing

• A Digital Image Processing System
• Image Representation and Formats
• 1. Sensing, Sampling, Quantization
• 2. Gray level and Color Images
• 3. Raw, RGB, Tiff, BMP, JPG, GIF, (JP2)
• Image Transform and Filtering
• Histogram, Enhancement and Restoration
• Segmentation, Edge Detection, Thinning
• Image Data Compression
• Image Pattern Analysis (Recognition and
  Interpretation)
• 1 R.C. Gonzalez, R.E. Woods, S.L. Eddins,
  Digital Image Processing Using MATLAB, Pearson
  Prentice Hall, 2004
• 2 R.C. Gonzalez and R.E. Woods, Digital Image
  Processing, Prentice-Hall, 2002

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Examples of Digital Images
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Image Processing System
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Digital Image Analysis System

• A 2D image is nothing but a mapping from a region
  to a matrix
• A Digital Image Processing System consists of
• 1. Acquisition scanners, digital camera,
  ultrasound,
• X-ray, MRI, PMT
• 2. Storage HD (120GB), CD (700MB), DVD
  (4.7GB),
• Flash memory (512MB4GB), 3.5 floppy
  diskettes,
• i-pod,
• 3. Processing Unit PC, Workstation,
  PC-cluster
• 4. Communication telephone lines, cable,
  wireless,
• 5. Display LCD monitor, laser printer,
  laser-jet printer

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Gray Level and Color Images
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Pixels in a Gray Level Image
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A Gray Level Image is a Matrix

• f(0,0) f(0,1) f(0,2) . .
  f(0,n-1)
• f(1,0) f(1,1) f(1,2) . .
  f(1,n-1)
• . .
  .
• . .
  .
• . .
  .
• f(m-1,0) f(m-1,1) f(m-1,2) . f(m-1,n-1)
• An image of m rows, n columns, f(i,j) is in
  0,255

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Gray and Color Image Data

• 0, 64, 144, 196,
• 225, 169, 100, 36
• (R, G, B) for a color pixel
• Red (255, 0, 0)
• Green ( 0, 255, 0)
• Blue ( 0, 0, 255)
• Cyan ( 0,255, 255)
• Magenta (255, 0, 255)
• Yellow (255, 255, 0)
• Gray (128, 128, 128)

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Image Representation (Gray/Color)

• A gray level image is usually represented by an M
  by N matrix whose elements are all integers in
  0,1, , 255 corresponding to brightness scales
• A color image is usually represented by 3 M x N
  matrices whose elements are all integers in 0,1,
  , 255 corresponding to 3 primary primitives of
  colors such as Red, Green, Blue

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Red, Green, Blue, Color Images
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Sensing, Sampling, Quantization

• A 2D digital image is formed by a sensor which
  maps a region to a matrix
• Digitization of the spatial coordinates (x,y) in
  an image function f(x,y) is called Sampling
• Digitization of the amplitude of an image
  function f(x,y) is called Quantization

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Gray Level and Color Images
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Image File Formats (1/2)

• The American National Standards Institute (ANSI)
  sets standards for voluntary use in US. One of
  the most popular computer standards set by ANSI
  is the American Standard Code for Information
  Interchange (ASCII) which guarantees all
  computers can exchange text in ASCII format
• BMP Bitmap format from Microsoft uses
  Raster-based 124-bit colors (RGB) without
  compression or allows a run-length compression
  for 18-bit color depths
• GIF Graphics Interchange Format from CompuServe
  Inc. is Raster-based which uses 18-bit colors
  with resolutions up to 64,00064,000 LZW
  (Lempel-Ziv-Welch, 1984) lossless compression
  with the compression ratio up to 21

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Some Image File Formats (2/2)

• Raw Raw image format uses a 8-bit unsigned
  character to store a pixel value of 0255 for a
  Raster-scanned gray image without compression. An
  R by C raw image occupies RC bytes or 8RC bits
  of storage space
• TIFF Tagged Image File Format from Aldus and
  Microsoft was designed for importing image into
  desktop publishing programs and quickly became
  accepted by a variety of software developers as a
  standard. Its built-in flexibility is both a
  blessing and a curse, because it can be
  customized in a variety of ways to fit a
  programmers needs. However, the flexibility of
  the format resulted in many versions of TIFF,
  some of which are so different that they are
  incompatible with each other
• JPEG Joint Photographic Experts Group format is
  the most popular lossy method of compression, and
  the current standard whose file name ends with
  .jpg which allows Raster-based 8-bit grayscale
  or 24-bit color images with the compression ratio
  more than 161 and preserves the fidelity of the
  reconstructed image
• EPS Encapsulated PostScript language format
  from Adulus Systems uses Metafile of 124-bit
  colors with compression
• JPEG 2000

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Image Transforms and Filtering

• Feature Extraction find all ellipses in an
  image
• Bandwidth Reduction eliminate the low contrast
  coefficients
• Data Reduction eliminate insignificant
  coefficients of Discrete Cosine Transform (DCT),
  Wavelet Transform (WT)
• Smooth filtering can get rid of noisy signals

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Discrete Cosine Transform

• Partition an image into nonoverlapping 8 by 8
  blocks, and apply a 2d DCT on each block to get
  DC and AC coefficients.
• Most of the high frequency coefficients become
  insignificant, only the DC term and some low
  frequency AC coefficients are significant.
• Fundamental for JPEG Image Compression

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Discrete Cosine Transform (DCT)

• X a block of 8x8 pixels
• AQ8 8x8 DCT matrix as
• shown above
• YAXAt

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DCT on a 8x8 Block
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Quantized DCT Coefficients
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Wavelet Transform

• Haar, Daubechies Four, 9/7, 5/3 transforms
• 9/7, 5/3 transforms was selected as the lossy and
  lossless coding standards for JPEG2000
• A Comparison of JPEG and JPEG2000 shows that the
  latter is slightly better than the former,
  however, to replace the current image.jpg by
  image.jp2 needs time

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Daubechies 4 Wavelet Transform

• X an image
• W Haar transform shown above with ci 1/v2
• YPW(XWtQ), where
• P and Q are permutation matrices

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A Block and Its Daub4 Transform
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Mean and Median Filtering

• X1 X2 X3
• X4 X0 X5
• X6 X7 X8
• Replace the X0 by the
• mean of X0X8 is
• called mean filtering

• X1 X2 X3
• X4 X0 X5
• X6 X7 X8
• Replace the X0 by the
• median of X0X8 is
• called median filtering

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Example of Median Filtering
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Image and Its Histogram
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Enhancement and Restoration

• The goal of enhancement is to accentuate certain
  features for subsequent analysis or image
  display. The enhancement process is usually done
  interactively
• The restoration is a process that attempts to
  reconstruct or recover an image that has been
  degraded by using some unknown phenomenon

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Segmentation and Edge Detection

• Segmentation is basically a process of pixel
  classification the picture is segmented into
  subsets by assigning the individual pixels into
  classes
• Edge Detection is to find the pixels whose gray
  values or colors being abruptly changed

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Image, Histogram, Thresholding
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(No Transcript)
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Binarization by Thresholding
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Edge Detection

• -1 -2 -1
• 0 0 0 ? X
• 1 2 1
• -1 0 1
• -2 0 2 ? Y
• -1 0 1
• Large (XY) ? Edge

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Thinning and Contour Tracing

• Thinning is to find the skeleton of an image
  which is commonly used for Optical Character
  Recognition (OCR) and Fingerprint matching
• Contour tracing is usually used to locate the
  boundaries of an image which can be used in
  feature extraction for shape discrimination

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Image ?Edge, Skeleton, Contour
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Image Data Compression

• The purpose is to save storage space and to
  reduce the transmission time of information. Note
  that it requires 6 mega bits to store a 24-bit
  color image of size 512 by 512. It takes 6
  seconds to download such an image via an ADSL
  (Asymmetric Digital Subscriber Line) with the
  rate 1 mega bits per second and more than 12
  seconds to upload the same image
• Note that 1 byte 8 bits, 3 bytes 24 bits

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Lenna Image vs. Compressed Lenna