Course Industrial Image Processing

Course

Meets requirements of following modules(MID)

• in active programs
  • Ba ET2012 IBV
  • Ba TIN2012 IBV

Course Organization

Instruction language

• Deutsch

Study Level

• Bachelor

Prerequisites

• basic skills in signal processing
• basic skills in Java and/or C
• basic skills in analysis and linear algebra

Textbooks, Recommended Reading

• Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Prentice Hall
• Scott E Umbaugh, COMPUTER VISION and IMAGE PROCESSING: A Practical Approach Using CVIPtools, Prentice Hall
• Wolfgang Abmayer, Einführung in die digitale Bildverarbeitung,Teubner

Instructors

• Prof. Dr. Thieling

Supporting Scientific Staff

• n.N.

Transcipt Entry

Assessment

Course components

Lecture/Exercise

Objectives

Lerninhalte (Kenntnisse)

• image construction, global image properties, and access to image data
  • graylevel and color images
  • global image properties,
    • mean value, variance, entropy
    • histogram, cumulative histogram
  • development environment
    • software design tools
      • compiler
      • linker
      • debugger
    • softwaretools for image processing and image analysis
      • softare-based access to image data and parameters
      • overview of the available ip-modules (moduls dor image processing and image analysis)
      • design and implementation of own ip-moduls
      • design of algorithmic chains based on ip-modules using visual programming
• gray level transformation
  • linear gray level transformation, histogram spreading
  • non-linear gray level transformation
  • histogram equalization
  • local histogram equalization
  • look-up-table
• analysis and processing of color images
  • technical and human color perception
  • additive and subtractive color mixing
  • RGB color space
  • HSI color space
  • transformation RGB to HSI and vise versa
• rank-order operators (non-linear filtering)
  • max, min, median
  • morphologische Operatoren
    • erosion, dilation
    • opening, closing
    • locating structures
• analysis and processing in frequency domain
  • fourier analysis and synthesis of one-dimensional digital signals
    • real spectrum, imaginary spectrum
    • amplitude spectrum, phase spectrum
    • filtering in frequency domain
  • fourier analysis and synthesisf of images
    • real spectrum, imaginary spectrum
    • amplitude spectrum, phase spectrum
    • filtering in spatial domain
      • non directional filter
      • directional filter
      • inverse filtering
• linear filtering in spatial domain
  • convolution, convolution, transfer function
  • typical convolution maks (mean, gauß, differencial-operator, sobel-operator, laplace-operator)
  • gradient and its calculation using differential-operator and sobel-operator
  • analysis and evaluation of the operator in the frequency domain
• Tracking
  • normalized cross-correlation
  • without prediction
  • with prediction (kalman filter)
• measuring of subpixel edges
  • one-dimensional
  • two-dimensional using gradient

Acquired Skills

• the presented methods for image enhancement can be
  • named
  • described
  • delineated in terms of application areas
  • evaluated in terms of advantages and disadvanteges
  • problemspecific parameterized
• the presented color spaces and corresponding algorithms can be
  • named
  • described
  • delineated in terms of application areas
  • evaluated in terms of advantages and disadvanteges
  • problemspecific parameterized
• the presented methods for non liniar filtering can be
  • named
  • described
  • delineated in terms of application areas
  • evaluated in terms of advantages and disadvanteges
  • problemspecific parameterized
• Spectra of images and / or convolution masks can be
  • analyzed
  • designed
  • discussed
• the presented methods for linear filtering can be (space and frequency domain)
  • named
  • described
  • delineated in terms of application areas
  • evaluated in terms of advantages and disadvanteges
  • problemspecific parameterized

Additional Component Assessment

• none

Lab

Objectives

Acquired Skills

• purposeful handling of the software development environment
• purposeful handling of the softwaretools for image processing and image analysis

Operational Competences

• deal with complex tasks in a small team
• derive complex solutions that can be implemented using image processing and image analysis

Additional Component Assessment

• none