1. Digital Image Processing & Analysis Fall 2008

2. Outline Sampling and Quantization Image Transforms Discrete Cosine Transforms Image Operations Image Restoration Enhancement Image Segmentation Image and Video Coding and Compression Image Understanding Image Reconstruction Computer Vision

3. Books Digital Image Processing –2 nd Edition by Rafael C. Gonzalez and Richard E. Woods Digital Image Processing Using MATLAB –Rafael C. Gonzalez and Richard E. Woods

4. Marks Distribution 1 st Mid-Term 10 2 nd Mid-Term15 Quizzes 10 Project15 Final50

5. Definitions Image Processing Image Analysis (Image Understanding) Computer Vision Low Level Processes: contrast manipulation Mid-Level Processes: segmentation, recognition High Level Processes: understanding groups of objects

6. Initial Examples of Imagery

7. Improvement

8. Digital Image Processing

9. Images in Invisible Spectrum Gamma Ray Imaging X – Ray Imaging/Computerized Axial Tomography UV band Imaging (Microscopy/Fluoroscopy) IR imaging (NVD) Images in Microwave Band Images in Radio Band (MRI) Ultrasound Transmission Electron Microscope

10. Important Stages in Image Processing Image Acquisition Preprocessing Segmentation Representation and Description Recognition and Interpretation Knowledge base

11. Important Stages in Image Processing

13. Image Acquisition Imaging sensor & capability to digitize the signal collected by the sensor –Video camera –Digital camera –Conventional camera & analog-to-digital converter

14. Preprocessing To improve the image to ensure the success of further processes e.g. enhancing contrast removing noise identifying information-rich areas

15. Segmentation To partition the image into its constituent parts (objects) –Autonomous segmentation (very difficult) Can facilitate or disturb subsequent processes –Output (representation): Raw pixel data, depicting either boundaries or whole regions (corners vs. texture for example) Need conversion to a form suitable for computer processing –(Description)

16. Representation & Description Feature selection (description) deals with extracting: –features that result in quantitative information of interest or –features that are important for differentiating one class of objects from another

17. Recognition & Interpretation To assign a label to an object based on information provided by the descriptors To assign meaning to a group of recognized objects

18. Knowledge Base Knowledge database –Guides the operation of each processing module and controls the interaction between modules

19. Comments Image enhancement for human visual interpretation usually stops at preprocessing Recognition and interpretation are associated with image analysis applications where the objective is automation (automated extraction of information from images)

20. Components