Course Industrial Computer Vision


Responsible: Prof. Dr. Thieling

Course

Meets requirements of following modules(MID)

Course Organization

Version
created 2019-01-17
VID 1
valid from WS 2019/20
valid to
Course identifiers
Long name Industrial Computer Vision
CID F07_IBA
CEID (exam identifier)

Contact hours per week (SWS)
Lecture 2
Exercise (unsplit)
Exercise (split)
Lab 2
Project
Seminar
Tutorial(voluntary)
Total contact hours
Lecture 30
Exercise (unsplit)
Exercise (split)
Lab 30
Project
Seminar
Tutorial (voluntary)
Max. capacity
Exercise (unsplit)
Exercise (split) 30
Lab 15
Project
Seminar

Total effort (hours): 150

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

Industrial Computer Vision

Assessment

Type
oE normal case (except on large numbers of assessments: wE

Total effort [hours]
oE 10

Frequency: 3/year


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

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