EELE 5310: Digital Image Processing Lecture 1 Eng. Ruba A. Salamah Rsalamah @ iugaza.Edu

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EELE 5310 Digital Image ProcessingLecture
1Eng. Ruba A. SalamahRsalamah _at_ iugaza.Edu
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Course Objectives

• To Cover the basic theory and algorithms that are
  widely used in digital image processing.
• To Expose students to current technologies and
  issues that are specific to image processing
  systems.
• To Develop hands-on experience in using computers
  to process images.
• Familiarize with MATLAB Image Processing Toolbox.

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Recommended Textbook

• Digital Image Processing by R.C. Gonzalez and
  R.E. Woods, 3rd edition, Pearson Prentice Hall,
  2008
• Additional readings on the class website

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Prerequisites

• Knowledge of the following three areas
• -Linear Algebra.
• -Elementary Probability Theory.
• -Signals and Systems.

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Grading Policy

• Quizzes 15
• H.W 10
• Attendance 10
• Projects 20
• FinalExam45

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Course outline

• Introduction
• Digital Image Fundamentals
• Image Enhancement in the Spatial Domain
• Image Enhancement in the Frequency Domain
• Image Restoration
• Image Compression
• Image Segmentation
• Representation and Description

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Fundamentals of Digital Image Processing
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What is a Digital Image?

• A finite array of data values

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What is Image Processing

• Processing digital images by means of a digital
  computer.
• Image processing typically attempts to accomplish
  one of three things
• Restoring Images
• Enhancing Images
• Understanding Images
• Restoration takes a corrupted image and
  attempts to recreate a clean original
• Enhancement alters an image to makes its
  meaning clearer to human observers
• Understanding usually attempts to mimic the
  human visual system in extracting meaning from an
  image

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Three Types of Processes

• Low-level Processes
• Involve primitive operations such as image
  preprocessing to reduce noise, contrast
  enhancement, and image sharpening.
• A low-level process is characterized by the fact
  that both its inputs and outputs are images.
• Mid-level Processes
• Involves tasks such as segmentation (partitioning
  an image into regions or objects), description of
  those objects to reduce them to a form suitable
  for machine learning , and classification(recognit
  ion) of individual objects.
• Its inputs generally are images, but its outputs
  are attributes extracted from those images (e.g.,
  edges, contours, and the identity of individual
  objects).

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Three Types of Processes

• High-level Processes
• Processing involves "making sense of an ensemble
  of recognized objects, as in image analysis, and,
  at the far end of the continuum, performing the
  cognitive functions normally associated with
  vision.

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Applications

• Processing of remote-sensed images via satellite.
• Radar, MRI, Ultrasonic image processing.
• Noise Reduction.
• Character recognition.
• Automatic inspection of industrial parts.
• Content based image retrieval.
• Biometrics.
• Target tracking.

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Sources of Energy for Image Formation

• The principle energy source for images is the EM
  spectrum
• Other sources include ultrasonic, electronic, and
  synthetic images.

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Some Applications -- Medical Diagnostics
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Some Applications -- MRI
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Some Applications -- Microscopy
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Some Applications -- Industrial Inspection
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Some Applications -- Remote Sensing
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Some Applications -- Transmitting Images
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Key Stages in Digital Image Processing
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Image Acquisition
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Image Enhancement
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Image Restoration
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Morphological Processing
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Segmentation
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Object Recognition
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Image Representation Description
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Image Compression
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Colour Image Processing