National DongHwa University

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National Dong-Hwa University Short
Course Class Notes July 5 8,
2004 College of Computing Sciences Computer
Vision Laboratory New Jersey Institute of
Technology Newark, NJ 07102 (http//www.cs.njit.ed
u/shih)
Current Research in Pattern Recognition and
Image Analysis
Professor Frank Y. Shih
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Course Outline

• Chapter 1 Introduction
• Chapter 2 Image Transforms
• Chapter 3 Image Enhancement
• Chapter 4 Matching
• Chapter 5 Segmentation
• Chapter 6 Representation
• Chapter 7 Minimum Distance Classifier
• Chapter 8 Neural Networks Classifier
• Chapter 9 Syntactic Pattern Classifier
• Chapter 10 Fuzzy Classifier
• Chapter 11 Mathematical Morphology
• Chapter 12 Fast Euclidean Distance Transformation
• Chapter 13 Shortest Path Planning
• Chapter 14 Decomposition of Binary and Grayscale
  Structuring Elements
• Chapter 15 Digital Image Watermarking

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Chapter 1. Introduction

• Interest in image processing and analysis stems
  from two principal application areas
• Improvement of pictorial information for human
  interpretation.
• Processing of scene data for autonomous machine
  perception.
• Digital image processing has a broad spectrum of
  applications
• Remote sensing via satellites and other
  spacecrafts, image transmission and storage for
  business applications, medical processing, radar,
  sonar, and acoustic image processing, robotics,
  and automated inspection of industrial parts.

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• Images acquired by satellites are useful in
  tracking of earth resources, geographical
  mapping, prediction of agricultural crops, urban
  growth, weather, flood and many other
  environmental applications.

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• Space image applications include recognition and
  analysis of objects in the images obtained from
  deep space-probe missions.

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• In medical applications, one is concerned with
  processing of chest x-rays, cineangiograms,
  projection images of transaxial tomography, and
  other medical images that occur in radiology,
  nuclear magnetic resonance (NMR), ultrasonic
  scanning, MRI, and CT. These images may be used
  for patient screening and monitoring or for
  detection of tumors or other diseases in patients.

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• Radar and sonar images are used for detection and
  recognition of various types of targets or in
  guidance and maneuvering of aircraft or missile
  systems.

Figure 1. Image of solar disk observed at BBSO on
11 January 2002, 172344 UT (a fragment with a
filament used in further examples is marked with
a black rectangle.)
Figure 2. An example of a solar filament.
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• There are many other applications ranging from
  robot vision for industrial automation to image
  synthesis for cartoon making or fashion design.
  In other words, whenever a human, a machine, or
  any other entity receives data of two or more
  dimensions, an image is processed.
• Image transmission and storage application occur
  in broadcast television, teleconferencing,
  transmission of facsimile images (printed
  documents and graphics) for office automation,
  communication over computer networks,
  closed-circuit television based security
  monitoring systems, and in military
  communications.

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What is an Image?

• Webster's Dictionary "An image is a
  representation, likeness or imitation of an
  object or thing, a vivid or graphic description,
  something introduced to represent something
  else".
• Images can be classified into several types based
  upon their form or method of generation
• Physical Images distributions of measurable
  physical properties.
• Visible Images - optical images are spatial
  distribution of light intensity.
• Non-visible Images - temperature, pressure,
  elevation, population density maps.
• Mathematical Functions abstract images of
  mathematics.

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WHAT IS A PICTURE?

• A restricted type of image. Webster "A
  representation made by painting, drawing, or
  photography. A vivid, graphic, accurate
  description of an object or thing so as to
  suggest a mental image or give an accurate idea
  of the thing itself."
• In image processing, the word "picture" sometimes
  equivalent to "image".
• "Webster" Digital calculation by numerical
  methods or discrete units. Digital image a
  numerical representation of an object.
  Processing the act of subjecting something to a
  process. Process a series of actions or
  operations leading to a desired result. Example
  car wash, change auto from dirty to clean.
• Digital image processing starts with one image
  and produces a modified version of that image.

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• Digital image analysis a process that takes a
  digital image into something other than a digital
  image, such as a set of measurement data or a
  decision.
• Digitization convert a pictorial form to
  numerical data. A digital image is an image
  f(x,y) that has been discretized both in spatial
  coordinates and brightness.
• The image is divided into small regions called
  picture elements, or pixels for short. The most
  common subdivision scheme is the rectangular
  sampling grid.

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• The image is represented by a rectangular array
  of integers. The image sizes and number of gray
  levels are integer powers of 2.
• The number at each pixel represents the
  brightness or darkness of the image at that
  point.
• For example 8 by 8 with 1 byte (8 bits) -gt 256
  gray levels

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FUNDAMENTAL STEPS IN IMAGE PROCESSING

• An example automatically reading the address on
  pieces of mail.
• Problem domain pieces of mail. Objective read
  the address on each piece. Output a stream of
  alphanumeric characters.
• Image Acquisition Digitization and Compression
• An imaging sensor and digitizer - convert
  pictures to discrete (digital) form, efficient
  coding or approximation of pictures so as to save
  storage space or channel capacity.
• Preprocessing Enhancement, Restoration, and
  Construction
• Improve degraded (low-contrast, blurred,
  noisy) pictures reconstruct pictures from sets
  of projection.

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• Segmentation Partition an input image into its
  constituent parts or objects. In OCR, the key
  role of segmentation is to extract individual
  characters and words from the background.
• Representation and Description (feature
  selection)
• Boundary representation is appropriate when the
  focus is on external shape characteristics, such
  as corners and inflections. Regional
  representation is appropriate when the focus is
  on internal properties, such as texture or
  skeletal shape.
• Transform raw data into a form suitable for
  subsequent computer processing extract
  quantitative information of interest or features
  that are basic for differentiating one class of
  objects from another.

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• Matching (or Recognition) and Interpretation
• compare and register pictures to one another
  measure properties of and relationships among the
  parts assign a label to an object based on the
  information provided by its descriptors assign
  meaning to an ensemble of recognized objects.
• Knowledge Base
• may be as simple as detailing regions of
  interest, or quite complex such as an
  interrelated list of all major possible defects
  in a material inspection problem.
• It controls the interaction communication
  between processing modules that are based on the
  prior knowledge of what a result should be. It
  also aids in feedback operations between modules
  through the knowledge base.

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ELEMENTS OF IMAGE PROCESSING SYSTEMS

• (1) Image Acquisition A physical device that
  is sensitive to a band in the electromagnetic
  energy spectrum (such as x-ray, ultraviolet,
  visible, or infrared bands) and that produces an
  electrical signal output proportional to the
  level of energy sensed.
• A digitizer for converting the electrical output
  of the physical sensing device into digital form.
• (2) Storage
• (a) Short-term storage for use during process.
  One method for short-term storage is computer
  memory. Another is by specialized boards, called
  frame buffers, that store one or more images and
  can be accessed rapidly, usually at video rates.
• (b) On-line storage for relatively fast recall,
  e.g. magnetic disks.
• (c) Archival storage characterized by infrequent
  access, e.g. magnetic tapes.

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• (3) Processing Most image processing functions
  can be implemented in software running on a host
  computer. The specialized image processing
  hardware is the need for speed in some
  applications.
• (4) Communication A voice-grade telephone line
  can transmit at a maximum rate of 56K bits/sec.
  Optical cable and wireless links using
  intermediate stations, such as satellites, are
  much faster.
• (5) Display Color TV monitors are the
  principle display devices used in image
  processing systems. The signals at the output of
  the display module can also be fed into an image
  recording device that produces a hard copy
  (slides, photographs, or transparencies) of the
  image being viewed on the monitor screen. Other
  display media include random-access cathode ray
  tubes (CRTs), and printing devices.

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OPERATIONS ON PICTURES

• (a) Point (Pixel) Operations
• Output at a point depends on the input only at
  the same point.
• May be more than one input picture.
• Ex. take the difference or product of two
  pictures point by point.
• (b) Local (Neighborhood) Operations
• Output at a point depends only on the input
  levels in the neighborhood of that point.
• Ex deblurring, noise cleaning, edge features
  detection.

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• (c) Geometric Operations
• Output at a point depends only on the input
  levels at some other point defined by geometric
  transformations.
• (d) Global Operations
• Output at a point depends on the input levels in
  the entire image.
• Ex distance transformation.

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DIGITAL IMAGE FUNDAMENTALS

• In analyzing the structure of an image for
  purposes of picture description, one wants to
  extract image parts that correspond to those seen
  by humans, and to describe them in terms
  corresponding to those used by humans.
• The subject of visual perception is very broad
  and complex. Many important topics will not be
  covered here for example, the ways in which
  perceptual abilities are acquired (perceptual
  learning), or the way in which one adapts to
  perceptual distortions. We shall not discuss the
  anatomy and physiology of human's visual system,
  but rather shall treat it from the black box''
  standpoint of perception psychology.

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• When an image has been transmitted by eyes to the
  brain, there is no little man'' inside the
  brain to look at it! The input to the VS may be
  an image, but the output which it furnishes to
  the higher brain centers must be something quite
  different.

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• Brightness and Contrast
• The ability to detect a bright spot or flash
  depends not only on such properties as the
  brightness, size, and duration of the spot, but
  also on the brightness of the background against
  which the spot appears. There is an absolute
  threshold - at the quantum level - below which
  detection is impossible but more generally,
  there is a contrast threshold - a just
  noticeable difference' between spot and
  background.

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• In general, detection thresholds depend on the
  previous pattern of illumination (in space and
  time) this dependence is called adaptation. When
  there has been no previous illumination for a
  long period, there is complete dark adaptation,
  and thresholds are at their lowest.
• Vision under conditions of dark adaptation is
  called scotopic, and is characterized by reduced
  ability to perceive colors under light
  adaptation, vision is called photopic. Detection
  thresholds also depend on position of the
  stimulus relative to the visual axis. There are
  adaptation-like effects that depend on subsequent
  illumination (up to about 0.1 sec) the contrast
  threshold rises even before perception of a light
  flash. Such time-dependent phenomena are known as
  metacontrast effects.

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SAMPLING AND QUANTIZATION

• Digitization of the spatial coordinates (x,y) is
  called image sampling, and amplitude digitization
  is called gray-level quantization.
• An NxM array, where
  
• and the number of gray levels

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• Assumption the discrete levels are equally
  spaced between 0 and L in the gray scale.
• The number of bits, b, required to store a
  digitized image
• A question to ask is how many samples and gray
  levels are required for a good approximation. The
  resolution (i.e. the degree of discernible
  detail) of an image is strongly dependent on both
  N and m. The more these parameters are increased,
  the closer the digitized array will approximate
  the original image. However, the above equation
  clearly points out the unfortunate fact that
  storage and processing requirements increase
  rapidly as a function of N and m.
• A good'' image is difficult to define because
  quality requirements vary according to
  application.

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