During the course, the following material, among others, will be covered:
- Introductory concepts for Image Processing & Analysis and their applications
- Basic elements of 2-D signal processing and image transforms
- Image acquisition systems and different types of degradation
- Image enhancement methods
- Image restoration methods
- Techniques for lossless and lossy image compression
- Reconstruction of 3D objects based on 2D projections
- Edge detection and linking
- Image segmentation
- Shape description and representation
- Object recognition
- Basic structure of an image analysis system
- Elements of color theory and color image processing basics.
B. Laboratory exercises and project
- Exercise 1: Image transforms and image filtering in the frequency domain
- Exercise 2: Image quantization (scalar and vector)
- Exercise 3: Image compression using DCT trnasform
- Exercise 4: Histogram based image processing
- Exercise 5: Image restoration (inverse filering, Wiener filtering)
- Exercise 6: Edge detection.
- Each student will choose to implement one from a list of possible projects.