Image Processing(IP)

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Image Processing(IP)

• 1. Introduction
• 2. Digital Image Fundamentals
• 3. Image Enhancement in the spatial Domain
• 4. Image Enhancement in the Frequency Domain
• 5. Image Restoration
• 6. Color Image Processing
• 7. Wavelets and Multiresolution Processing
• 8. Image Compression
• 9. Morphological Image Processing
• 10. Image Segmentation
• 11. Representation Description
• 12. Object Recognition

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Introduction

• 1.1 What is Digital Image Processing
• 1.2 The Origins of Digital Image Processing
• 1.3 Examples of Field that Use Digital Image
  Processing
• 1.4 Fundamental Steps in Digital Image Processing
• 1.5 Components of an Image Processing System
• 1.6 Importance Academic IP Journals Research
• 1.7 Course Requirements

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1.1 What is Digital Image Processing

• 1. Related Terminologies
• a. image ---- still
• b. picture --- image
• c. graph ----- conceptual
• d. pattern --- conceptual
• e. graphics -- drawings
• f. animation - dynamic graphics
• g. video ------ dynamic images

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1.1 What is Digital Image Processing

• 1. Image ( monochrome image )
• 2-D light intensity function f(x,y)
• where (x,y) spatial coordinates
• value of f brightness of gray level at (x,y)
• 2. Digital Image
• image discretized both in spatial and gray levels
• 3. Image Elements
• picture elements (pixels or pels)

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1.1 What is Digital Image Processing

• 4. Related Fields
• a. computer vision (CV) ----------- 3-D IP
• b. signal processing (SP) ---------- 1-D IP
• c. computer graphics (CG) -------- generation of
  drawings
• d. image synthesis (IS) ------------ generation
  of images (IP CG )
• e. pattern recognition (PR) ------- theory
• f. scientific visualization (SV) --- application
  of IS
• g. multimedia technologies ------- application of
  a thru f

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1.1 What is Digital Image Processing

• Three types of computerize processes
• Low-level processes
• Primitive operations such as image processing
  to reduce noise, contrast enhancement, and image
  sharpening.
• Both its inputs and outputs are images
• Mid-level processes
• Segmentation ( partitioning an image into regions
  or objects)
• Description of those objects to reduce them to a
  form suitable for computer processing,
• Classification ( recognition) of individual
  objects.
• Its inputs generally are images, but its outputs
  are attributes extracted form those image
• High-level processes
• Making sense of an ensemble of recognized
  objects

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1.2 The Origins of Digital Image Processing

• 1. Improving digitized newspaper in 1920s to 1950s

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1.2 The Origins of Digital Image Processing

• 2. Improving images from space programs from 1964

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1.2 The Origins of Digital Image Processing

• 3. From 1960s till now, the IP field has grown
  vigorously
• 4. Computer tomography(CT)
• an important achievement of in medicine ( has won
  a Nobel Prize)

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.1 Gamma Ray Imaging

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.2 X-ray imaging

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.3 Imaging in the Ultraviolet Band

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.4 Imaging in the Visible and Infrared Bands

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.4 Imaging in the Visible and Infrared Bands

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.4 Imaging in the Visible and Infrared Bands

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.4 Imaging in the Visible and Infrared Bands

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.4 Imaging in the Visible and Infrared Bands

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.4 Imaging in the Visible and Infrared Bands

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.4 Imaging in the Visible and Infrared Bands

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.4 Imaging in the Visible and Infrared Bands

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.5 Imaging in the Microwave Bands

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.6 Imaging in the Radio Bands

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.6 Imaging in the Radio Bands

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.7 Examples in which Other Imaging Modalities
  Are Used

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.7 Examples in which Other Imaging Modalities
  Are Used

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.7 Examples in which Other Imaging Modalities
  Are Used

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1.3 Examples of Field that Use Digital Image
Processing

• 1.3.7 Examples in which Other Imaging Modalities
  Are Used

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1.4 Fundamental Steps in Digital Image Processing
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1.5 Components of an Image Processing System
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1.6 Important Academic IP Journals

• 1. IEEE Transactions on Pattern Analysis. Mach.
  Intelligence
• 2. IEEE Transactions on Systems, Man, and
  Cybernetics
• 3. IEEE Transaction of Image Processing
• 4. Computer Vision, Graphics, and Image
  Processing
• 5. Pattern Recognition
• 6. Image and Vision Computing
• 7. International Journal of Computer Vision
• 8. Machine Vision and Applications
• 9. Pattern Recognition Letters

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1.7 Course Requirements

• 1. Textbook
• R.C.Gonzalez and R.E. Woods, Digital Image
  Processing, Addison-Wesley Pub. Co., Readings
  Massachusetts, USA,
• 2. Grade Evaluation
• b. one or two exams
• a. about 34 homeworks
• 3. Pre-requistes
• Ability of programing or Experience of IP
  Software ( MATLAB).

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2.4 Some Basic Relations Between Pixels

• 2.4.1 Neighborhood of a Pixel
• 1. Given a pixel p in the center of 9
• pixels
• a b c
• d p e
• f g h
• then
• 4-neighbors of p b, d, e, g
• 8-neighbors of p a, b, c, d, e, f, g, h
• diagonal neighbors of p a, c, f, h

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2.4 Some Basic Relations Between Pixels

• 2.4.2 Connectivity
• 1. Terms 4-connected , 8-connected , 4-adjacent
  , 8-adjacent , path connected.
• 2. Connected component (c. c.)
• for any pixel p in a set of pixels S , the set of
  pixels MS that are connected to p is a c.c. of p
  

S
c.c.
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2.4 Some Basic Relations Between Pixels

• 2.4.3 Labeling of Connected Components
• 1.Gives a pixel p with r and t as its upper and
  left-hand neighbors as follows
• r
• t p
• then the following algorithm labels all c.c. in
  an binary image ( This algorithm for 4-connected)
• a. Scan the image from left to right and from top
  to bottom
• b. If p 0 , continue the scan
• c. If p 1 , exam r and t
• if r t 0 assign a new label to p

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2.4 Some Basic Relations Between Pixels

• if r 1 t 0 , assign the label of r to p
• if r 0 t 1 , assign the label of t to p
• if r t 1 labels of r t identical, then
  assign that label to p
• if r t 1 labels of r t different, then
  assign one of the labels to p and make the two
  labels equivalent
• d. Sort all the equivalent label pairs into
  equivalent classes, and assign a distinct label
  to each class.
• 2.Do a second scan thru the image and replace
  each label by the label assigned to its
  equivalent class.
• 3. For sorting of equivalent labels ,see Section
  2.4.4
• P.S. The above is for 4-connectivity, another
  algorithm in textbook for 8-connectivity

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2.4 Some Basic Relations Between Pixels

• 2.4.4 Relations , Equivalence , and Transitive
  Closures
• 1. A property
• If R is an equivalent relation on a set A ,
  then A can be divided into a group of disjoint
  subsets , called equivalent classes , such that
  aRb iff a and b are in the same subset.

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2.4 Some Basic Relations Between Pixels

• 2.4.5 Distance Measures
• 1. Given three pixels p, q, and z with
  coordinates (x, y), (s, t), (u, v), respectively,
  we have the following three types of distances
• a. Euclidean distance
• De(p, q) (x-s)2(y-t)21/2
• b. City-block distance
• D4(p, q) x - s y - t
• The pixels with D41 to a pixel p are the
  4-neighbors of p
• c. Chessboard distance
• D8(p, q) max ( x - s , y - t )
• The pixels with D81 to a pixel p are the
  8-neighbors of p

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2.4 Some Basic Relations Between Pixels

• 2.4.6 Arithmetic/Logic Operations
• 1. Mask operations
• are arithmetic/logic operation applied to the
  neighborhood ( with g. l. z1, z2, .., z9) of a
  pixel with g. l. z5, e.g.,
• z5 z(z1z2z3.z9)/9
• 2.Notes
• a. g. l. gray level
• b. Masks are also called templates, windows,
  filters, etc.
• 2.5 Image Geometry ( see the textbook)
• 2.6 Photographic Films ( see the textbook).