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---+!! Course %FORMFIELD{"Bezeichnung"}%


%TOC{depth="3"}%
%STARTSECTION{"no_toc"}%
---
*Responsible:* Prof. Dr. Thieling

---++ Course
---+++ Meets requirements of following modules(MID)
   * in active programs
      * [[BaET2012_IBA]]
      * [[BaMT2012_BV2]]
      * [[BaTIN2012_IBA]]

---+++ Course Organization
<sticky>
  <table border="0">
    <tr valign="top">
      <td>
        <table border="1" cellpadding="2" cellspacing="0">
          <th colspan="2">Version</th>
          <tr>
            <td>created</td>
            <td>2019-01-17</td>
          </tr>
          <tr>
            <td>VID</td>
            <td>1</td>
          </tr>
          <tr>
            <td>valid from</td>
            <td>WS 2019/20</td>
          </tr>
          <tr>
            <td>valid to</td>
            <td/>
          </tr>
        </table>
      </td>
      <td>         </td>
      <td>
        <table border="1" cellpadding="2" cellspacing="0">
          <th colspan="2">Course identifiers</th>
          <tr>
            <td>Long name</td>
            <td>%FORMFIELD{"Bezeichnung"}%</td>
          </tr>
          <tr>
            <td>CID</td>
            <td>F07_IBA</td>
          </tr>
          <tr>
            <td>CEID (exam identifier)</td>
            <td/>
          </tr>
        </table>
      </td>
    </tr>
  </table>
</sticky><sticky>
  <table border="0">
    <tr valign="top">
      <td>
        <table border="1" cellpadding="2" cellspacing="0">
          <th colspan="2">Contact hours per week (SWS)</th>
          <tr>
            <td>Lecture</td>
            <td>%FORMFIELD{"VorlesungSWS"}%</td>
          </tr>
          <tr>
            <td>Exercise (unsplit)</td>
            <td>%FORMFIELD{"UebungGanzSWS"}%</td>
          </tr>
          <tr>
            <td>Exercise (split)</td>
            <td>%FORMFIELD{"UebungHalbSWS"}%</td>
          </tr>
          <tr>
            <td>Lab</td>
            <td>%FORMFIELD{"PraktikumSWS"}%</td>
          </tr>
          <tr>
            <td>Project</td>
            <td>%FORMFIELD{"ProjektSWS"}%</td>
          </tr>
          <tr>
            <td>Seminar</td>
            <td>%FORMFIELD{"SeminarSWS"}%</td>
          </tr>
          <tr>
            <td>Tutorial(voluntary)</td>
            <td>%FORMFIELD{"TutoriumSWS"}%</td>
          </tr>
        </table>
      </td>
      <td>    </td>
      <td>
        <table border="1" cellpadding="2" cellspacing="0">
          <th colspan="2">Total contact hours</th>
          <tr>
            <td>Lecture</td>
            <td>%FORMFIELD{"VorlesungPZ"}%</td>
          </tr>
          <tr>
            <td>Exercise (unsplit)</td>
            <td>%FORMFIELD{"UebungGanzPZ"}%</td>
          </tr>
          <tr>
            <td>Exercise (split)</td>
            <td>%FORMFIELD{"UebungHalbPZ"}%</td>
          </tr>
          <tr>
            <td>Lab</td>
            <td>%FORMFIELD{"PraktikumPZ"}%</td>
          </tr>
          <tr>
            <td>Project</td>
            <td>%FORMFIELD{"ProjektPZ"}%</td>
          </tr>
          <tr>
            <td>Seminar</td>
            <td>%FORMFIELD{"SeminarPZ"}%</td>
          </tr>
          <tr>
            <td>Tutorial (voluntary)</td>
            <td>%FORMFIELD{"TutoriumPZ"}%</td>
          </tr>
        </table>
      </td>
      <td>    </td>
      <td>
        <table border="1" cellpadding="2" cellspacing="0">
          <th colspan="2">Max. capacity</th>
          <tr>
            <td>Exercise (unsplit)</td>
            <td>%FORMFIELD{"UebungGanzTeilnehmer"}%</td>
          </tr>
          <tr>
            <td>Exercise (split)</td>
            <td>%FORMFIELD{"UebungHalbTeilnehmer"}%</td>
          </tr>
          <tr>
            <td>Lab</td>
            <td>%FORMFIELD{"PraktikumTeilnehmer"}%</td>
          </tr>
          <tr>
            <td>Project</td>
            <td>%FORMFIELD{"ProjektTeilnehmer"}%</td>
          </tr>
          <tr>
            <td>Seminar</td>
            <td>%FORMFIELD{"SeminarTeilnehmer"}%</td>
          </tr>
        </table>
      </td>
    </tr>
  </table>
</sticky>

*Total effort (hours):* %FORMFIELD{"Gesamtaufwand"}%

---++++ Instruction language
   * German

---++++ Study Level
   * %FORMFIELD{"Niveau"}%
---++++ 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
<sticky>
  <table border="1" cellpadding="2" cellspacing="0">
    <th colspan="2">Type</th>
    <tr>
      <td>oE</td>
      <td>normal case (except on large numbers of assessments: wE</td>
    </tr>
  </table>
</sticky>

<sticky>
  <table border="1" cellpadding="2" cellspacing="0">
    <th colspan="2">Total effort [hours]</th>
    <tr>
      <td>oE</td>
      <td>10</td>
    </tr>
  </table>
</sticky>

*Frequency:* 3/year

-----
---++ Course components

%STARTSECTION{"Vorlesung / Übung"}%
---+++ <u>Lecture/Exercise</u>

---++++ 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

%ENDSECTION{"Vorlesung / Übung"}%



%STARTSECTION{"Praktikum"}%
---+++ <u>Lab</u>

---++++ 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

%ENDSECTION{"Praktikum"}%




%ENDSECTION{"no_toc"}%
Topic-Revision: r5 - 17 Jan 2019, GeneratedContent
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