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Image & Graphics
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- 2. A digital imageis a representation of a two- dimensional image as a finite set of digital values, called picture elements or pixels
- 3. •Pixel values typicallyrepresent 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 imageformats include: ▫ 1 sample per point (B&W or Grayscale) ▫ 3 samples per point (Red, Green, and Blue) ▫ 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. Image Format Bitmap: An arrayof 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 (GraphicalInterchange 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 • Monochromejust 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. JPEGFile Sizes Both images are the same relative size. 900kb .JPEG High Quality ~700kb
- 9. Graphics • Computer generatedor 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 •MotionDynamics ▫ 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 • Architectureof Raster Display Minimum refresh rate 60 Hz is used to avoid flickering
- 13. Screen Mosaic TriadArrangement
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- 15. Dithering • Dithering ▫ Ifwe 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 •Digitalimage 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 imageprocessing • 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. Digital Image Processing(DIP) LowLevel 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
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- 21. Image Recognition Steps Conditioning Suppressesnoise 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 Extractingoperation 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 ImageTransmission Size = spatial resolution X pixel quantization Compressed image data transmission JPEG, MPEG Symbolic image data transformation image primitive, attribute etc.