Course code 07 67 3030 20
ECTS credits 7
Course title in the language of instruction
Image Processing
Course title in Polish Signal and Image Processing (Przetwarzanie sygnału i obrazu)
Course title in English
Image Processing
Language of instruction English
Course level first-cycle programme
Course coordinator dr inż. Bartłomiej Stasiak
Course instructors dr inż. Bartłomiej Stasiak, dr inż. Arkadiusz Tomczyk
Delivery methods and course duration
Lecture Tutorials Laboratory Project Seminar Other Total of teaching hours during semester
Contact hours 30 30 0 60
E-learning No No No No No No
Assessment criteria (weightage) 0.00 0.00 0.00
Course objective
  1. The aim of the course is to introduce the fundamentals of image reprezentation and processing and to apply it for practical realization of selected image processing algorithms.
Learning outcomes
  1. Upon completion of the course the student will be able to define the concept of a digital image and explain practical issues of its acquisition and representation;
  2. Upon completion of the course the student will be able to list several categories of image processing methods and their application areas;
  3. Upon completion of the course the student will be able to describe the details of geometric and intensity transformations, morphological operations, image filtering and segmentation;
  4. Upon completion of the course the student will be able to discriminate between the approaches based on spatial and frequency domain and demonstrate the differences and the key features of both.
  5. The new skills acquired during the course will include the ability to implement image processing methods in a chosen programming language and construct a software framework for their integration;
  6. The new skills acquired during the course will include the ability to select and apply appropriate methods for given tasks;
  7. The new skills acquired during the course will include the ability to modify relevant parameters for their enhancement.
  8. The student will be expected to evaluate the results obtained with the self-created software;
  9. The student will be expected to appreciate the importance of algorithm selection and optimization for speed and memory usage.
Assessment methods
The overall assessment includes two main components corresponding to two groups of learning outcomes, respectively. The knowledge and comprehension (1 - 4) will be verified by means of a written examination at the end of the course. The practical skills (5 - 9) will be evaluated during the course on the basis of the four laboratory tasks. The marks for both components are combined equally (arithmetic mean) to give the final mark. The students will be provided with the list of examination topics divided into four groups (corresponding to the learning outcomes 1 - 4). During the examination they will have to answer one question (selected by the examiner) from each group, accounting for 20%, 20%, 30%, 30% of the total examination mark, respectively.
The laboratory mark is the arithmetic mean of the four tasks. It is necessary to get a pass mark for all of the tasks. The students will be provided with report template for each task and they are obliged to use it for report preparation. The details of each task, along with some additional theory and explanations, the precise requirements related to individual subproblems and their relative weights in the total mark for the task are included in its instruction and report template.

The form of assessment for each learning outcome:
1. Examination
2. Examination
3. Examination. Oral response accompanying laboratory task completion.
4. Examination
5. Assessment of laboratory tasks realization
6. Oral response accompanying laboratory task completion. Assessment of laboratory tasks realization
7. Oral response accompanying laboratory task completion. Assessment of laboratory tasks realization
8. Oral response accompanying laboratory task completion. Assessment of laboratory tasks realization
9. Oral response accompanying laboratory task completion. Assessment of laboratory tasks realization

Lecture: Written exam
Laboratory: Oral response accompanying laboratory task completion and assessment of laboratory tasks realization
Prerequisites
Matematics 
Algorithms and data structures
Fundamentals of programming
Course content with delivery methods
LECTURE
1. Modern image processing – application areas
2. Sources of digital images
3. Image acquisition and representation
4. Geometric and intensity transformations
5. Histogram processing
6. Spatial filtering – preliminaries
7. Spatial filtering – linear spatial filters
8. Spatial filtering – image restoration and reconstruction
9. Morphological image processing – operations
10. Morphological image processing – algorithms
11. Fourier transform and FFT
12. Filtering in the frequency domain
13. Image compression
14. Color image processing
15. Image segmentation

LABORATORY
The students work in groups (pairs). Each group has to complete four tasks. Each task consists of several subproblems divided into variants. The variants will be assigned to groups individually by the teacher.
Task 1 - Application for image processing and analysis, elementary operations, noise removal.
Task 2 - Filtration in spatial domain (histogram modifications, linear and nonlinear operations, convolution).
Task 3 - Morphological operations, image segmentation.
Task 4 - Fast algorithms for computing discrete orthogonal transforms. Filtration in the frequency domain.
Basic reference materials
  1. R.C. Gonzalez and R.E. Woods, Digital Image Processing, 3rd Edition, NJ: Prentice Hall Inc, 2002
  2. R. Tadeusiewicz and P. Korohoda, Komputerowa analiza i przetwarzanie obrazów. Kraków: FPT, 1997
Other reference materials
  1. W. K. Pratt, Digital Image Processing, 4th Edition: Wiley-Interscience, 2007
  2. E. R. Davies, Machine Vision, 3rd Edition: Morgan Kaufmann, 2005
  3. R. Tadeusiewicz and M. Flasinski, Rozpoznawanie obrazów, Warszawa: PWN, 1991
Average student workload outside classroom
116
Comments
Nazwa przedmiotu: Image Processing
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