1. Md. Monjur –ul-Hasan Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong 4349 http://monjur-ul-hasan.info IMAGES AND GRAPHICS

2. A digital image is a representation of a two- dimensional image as a finite set of digital values, called picture elements or pixels

3. Pixel values typically represent gray levels, colours, heights, opacities etc Remember digitization implies that a digital image is an approximation of a real scene Pixel 1 pixel

4. Image Format Common image formats include: ▫1▫1 sample per point (B&W or Grayscale) ▫3▫3 samples per point (Red, Green, and Blue) ▫4▫4 samples per point (Red, Green, Blue, and “Alpha”, a.k.a. Opacity) For most of this course we will focus on grey-scale images

5. Bitmap: An array of information that contains the information for the image. It is a 3 dimensional array Width x Height x 24 (8 for each color) So can be huge (.bmp and.tif or.tiff are most common bitmaps) JPG (Joint-Photographic Experts Group) Generally better for images and photos Spatial not color compression, can distort image spatially and more loss with each save Now can animate as well. For continuous tone images, such as full-color photographs Supports more than 16 millions of color (24-bit) Uses lossy compression (averaging may lose information)

6. Image Format GIF (Graphical Interchange Format) For large areas of the same color and a moderate level of detail. Supports up to 256 colors Allows transparency and interlacing Uses lossless compression PNG (Portable Network Graphic) lossless, portable, well-compressed storage of raster images patent-free replacement for GIF also replace many common uses of TIFF Support indexed-color, grayscale, and true color images + an optional alpha channel for transparency

7. Image Format Monochrome just requires one bit per pixel, representing black or white BMP – 16 KB 8 bits per pixel allows 256 distinct colors BMP – 119KB 16 bits per pixel represents 32K distinct colors (Most graphic chipsets now supports the full 65536 colors and the color green uses the extra one bit) BMP – 234 KB 24 bits per pixel allows millions of colors 32 bits per pixel – trillion of colors BMP – 350KB

8. Bitmapped vs. JPEG File Sizes Both images are the same relative size. 900kb.JPEG High Quality ~700kb

9. Graphics Computer generated or drawn by you. Specified through graphics primitives (Lines, Rectangle, circle etc) and their attribute (Line style, width, color etc). Not represent by pixel matrix. You can directly manipulate the elements because you drew them – Sprites Additional conversion is required for Draw pixel matrix.

10. Graphics Vs Images Basic element ▫Specified through graphics primitives (Lines, Rectangle, circle etc) and their attribute (Line style, width, color etc). ▫Pixel Manipulation ▫You can directly manipulate the elements because you drew them – Sprites ▫In an image, you can manipulate pixels but not directly the elements. This has a great impact. Visible ▫Additional conversion is required for Draw pixel matrix. ▫No Conversion required

11. Dynamic In Graphics Motion Dynamics ▫Object can be moved and enabled with respect to the stationary object. ▫Both the object and the camera are moving. Update Dynamics ▫Change the shape, colour, or other properties of the objects being viewed.

12. Graphics Hardware Architecture of Raster Display Minimum refresh rate 60 Hz is used to avoid flickering

13. Screen Mosaic Triad Arrangement

14. Interlaced Projection

15. Dithering ▫If we view a very small area from a sufficiently large viewing distance, our eyes average fine detail within the small area and record only the overall intensity of the area. ▫This phenomenon used in hardware display is known as dithering ▫Color display with three bits per pixel: red, green, blue ▫2X2 pattern area ▫It can produce 5X5X5 color combinations

16. Digital Image Processing Digital image processing focuses on two major tasks ▫Improvement of pictorial information for human interpretation ▫Processing of image data for storage, transmission and representation for autonomous machine perception Some argument about where image processing ends and fields such as image analysis and computer vision start

17. Computer image processing Image synthesis (generation) Image analysis (recognition) Image synthesis Pictorial synthesis of real or imaginary objects Mainly graphics concern with synthesis Image analysis Recognition of models from pictures of 2D or 3D objects Digital Image Processing (DIP)

18. Low Level Process Input: Image Output: Image Examples: Noise removal, image sharpening Mid Level Process Input: Image Output: Attributes Examples: Object recognition, segmentation High Level Process Input: Attributes Output: Understanding Examples: Scene understanding, autonomous navigation The continuum from image processing to computer vision can be broken up into low, mid- and high-level processes

19. Digital Image Processing(DIP) Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression

20. Image Recognition Steps Steps: Formatting Conditioning Labeling Grouping Extracting Matching

21. Image Recognition Steps Conditioning Suppresses noise Background normalization By suppressing uninteresting systematic or pattern variations Labeling Informative pattern has structure with set of connected pixels. Region, edge Grouping The grouping operation identifies the events by collecting together or identifying maximal connected sets of pixels participating in the same kind of event. Example: Edges are grouped into lines, is called line-fitting

22. Image Recognition Steps Extracting Extracting operation computes for each group of pixels a list of properties. Example: Centroid Area Orientation Spatial moments, gray tone moments, spatial-gray tone moments Circumscribing circle, inscribing circle, Matching Determines the interpretation of some related set of image events, associating these events with some given three-dimensional object or two-dimensional shape. Example: Template matching

23. Image Transmission Raw Image Transmission Size = spatial resolution X pixel quantization Compressed image data transmission JPEG, MPEG Symbolic image data transformation image primitive, attribute etc.