Course Industrial Computer Vision

Course

Meets requirements of following modules(MID)

• in active programs
  • Ba ET2012 IBA
  • Ba MT2012 BV2
  • Ba TIN2012 IBA

Course Organization

Instruction language

• German

Study Level

• Undergraduate

Prerequisites

• fundamentals in image processing (as treated in IBV or BV1)
• 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

• M.Sc. Hanna Sidnenka

Transcipt Entry

Assessment

Course components

Lecture/Exercise

Objectives

Contents

• image construction and access to image data
  • grey-level image and colour image
  • 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
• segmentation
  • histogram-based segmentation
    • histogram analysis
    • shading and its compensation
  • region-based segmentation
    • filling
    • split and merge
    • region growing
  • contour-based segmentation
    • contour tracking
    • hough-transformation
• feature extraction
  • geometric features
    • basic features (area, perimeter, shape factor)
    • central moments
    • normalized central moments
    • polar distance
    • curvature
    • DFT of polar distance and curvature
  • color features (HSI)
  • texture features
    • co-occurrence matrix
    • haralick features
• Klassifikation von Merkmalen
  • terms and concepts
    • feature vector, feature space, object classes
    • supervised / unsupervised classification
    • learning / not learning classification
  • typical methods
    • quader method
    • minimum distance
    • nearest neighbour
    • maximum likelihood
  • neuronale Netze
    • the artificial neuron as a simple classifier
      • operation
      • activation function
      • bias
      • training a neuron (gradient descent)
    • multi-layer-perceptron
      • operation
      • purposes of the layers
      • backpropagation training algorithm
    • development environment for creating and training neural networks
      • design and configuration of neural networks
      • training neural networks
      • verification tof rained networks
      • generating C-functions from trained networks

Acquired Skills

• the presented methods for segmentation can be
  • named
  • described
  • delineated in terms of application areas
  • evaluated in terms of advantages and disadvanteges
  • problemspecific parameterized
• the presented methods for feature extraction can be
  • named
  • described
  • delineated in terms of application areas
  • evaluated in terms of advantages and disadvanteges
  • problemspecific parameterized
• the presented methods for scallsification can be
  • 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
• purposeful handling of the development environment for creating and training neural networks

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