Shape Recognition and Image Processing (ETF RII POOS 4755)

General information

Module title

Shape Recognition and Image Processing

Module code





Computing and Informatics





Module type












Module goal - Knowledge and skill to be achieved by students

  Introduction to basic algorithms and instruments for analysis of digital media.
On the basis of acquired knowledge, students will be able to create algorithms based on image processing techniques, image acquisition, which can be applied in video surveillance, biometrics, analysis of medical images and other environments.


  1. Definition of image processing and computer vision. Brief reference to the appliances: automatic quality control, robotic and industrial automation. <br>
2. ITS (Intelligent Transportation Systems), video surveillance, biometrics, medical image analysis. <br>
3. Image creation and acquisition - geometric models. Perspective projection. Depth of field. Field of view. Image digitalization. <br>
4. Image acquisition techniques - CCD camera. The basic parameters of the camera. Video standard RS-170 and CCIR. Color space. NTSC and PAL. Frame-grabber principle. <br>
5. Histogram of gray shades. Contrast adjusting. Histogram equalization. <br>
6. Correlation. Gaussian filter. Median filter. Sharpening filter. <br>
7. Image segmentation - Methods for automatic determination of threshold. Growth of the region. <br>
8. Morphology of binary images - Expansion. Erosion. <br>
9. Transformation – Distance Transformation (DT). <br>
10. Separation of contour - Defining edge step 1D and 2D. Edge detection using the gradient. Standardization. Prewitt operator. Sobel operator. Frei-Chen operator. Canny edge detection. <br>
11. Locating shapes - compatibility with the pattern. Measures of similarity (SSD, SAD, NCC). Fast algorithms. <br>


Recommended1. Notes and slides from lectures (See Faculty WEB Site) <br>
2.Gonzales R., Woods R. : "Digital Image Processing", Second Edition, Prentice- Hall, New-Jersey, USA, 2002. <br>
3. Nalwa V. : "A Guided Tour of Computer Vision", Addison-Wesley, Mass., USA, 1993. <br>
4. Jain R,, Kasturi R., Schunk B "Machine Vision", Mc Graw-Hill, 1995 <br>
5. Trucco E., Verri A.: "Introductory Techniques for 3D Computer Vision", Prentice-Hall, 1998. <br>
AdditionalCVonline: Vision Related Books including Online Books and Book Support Sites []

Didactic methods

  Through lectures, students will learn about the theory, tasks and applicative examples within thematic units. Lectures consist of theoretical part, presentational descriptive examples, genesis and resolution of specific tasks. In this way, students will have basis for appliance of skilled material in engineering applications. Additional examples and exam tasks are discussed and solved during the laboratory exercises. Laboratory practice and home assignments will enable students of continuous work and their knowledge verification.


  During the course students will collect points according to the following system: <br>
- Attending lectures, exercises and tutorial classes: 10 points, student with more then three absences from lectures, exercises and/or tutorials can not achieve these points; <br>
- Home assignments: maximum of 10 points, assuming solving 5 to 10 assignments evenly distributed throughout the semester; <br>
- Partial exams: two written partial exams, maximum of 20 points for each positively evaluated partial exam; <br>
Student who during the semester achieved less than 20 points must re-enroll this course. <br>
Student who during the semester achieved 40 or more points will access to final oral exam, the exam consists of discussing the partial exams tasks, home assignments and answers to simple questions related to course topics. <br>
Final oral exam provides maximum of 40 points. To achieve a positive final grade, students in this exam must achieve a minimum of 20 points. Students who do not achieve this minimum will access to makeup oral exam. <br>
Student who during the semester achieved 20 or more points, and less than 40 points will access to makeup exam. Makeup exam is structured as follows: <br>
- Written part structured in the same way as a partial written exam, during which students solve problems in topics they failed on partial exams (achieved less then 10 points), <br>
- Oral part structured in the same way as a final oral exam. <br>
Only students who, after passing the written part of the makeup exam managed to achieve a total score of 40 or more points, can access to oral makeup exam, where the score consists of points achieved through attending classes, home assignments, passing partial exams and passing the written part of makeup exam. <br>
Oral makeup exam provides maximum of 40 points. To achieve a positive final grade, students in this exam must achieve a minimum of 20 points. Students who do not achieve this minimum must re-enroll this course. <br>

Aditional notes