2. Contents
Multimedia Definition (elements and Image & graphics)
Image and Graphics ( definition and example)
Digital Image Representation
Types of Digital images
Digital Image File Formats
Raster and Vector Graphics
Image Resolution
Colour Schemas
Colour Dithering
Image Processing Steps
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3. Multimedia
Text
Image
Videos
Audio
Animation
Multimedia is composed of
two words “multi” and
“media” where “multi”
means various and “media”
means the medium or tools
for the communication.
It consists of basic 5 main elements
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Simple Text
4. Image and Graphics
Image:
A visual representation of something.
Either created or photographed.
Example: Image of a place, object,
Person etc..
Graphics: It includes:-
Pictures or Photographs
Drawings or Line arts
Buttons and Banner
Charts and graphs
Icons
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5. Digital Image Representation
Digital Image can be described as 2D array, in
form of function, I(x,y)
Where, I = Intensity value
(gray level, bit depth),
at a point (x,y). x = row , y = column
The value of light reflection from objects at each
point is acquired by the sensors inside the digital
camera.
Gray levels range from 0 (Black) to 255 (White).
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6. Types of Digital Image
Binary Image: Each pixel is just Black and White. i.e. 0 or 1.
Gray Scale Image: Each pixel gray intensity ranges from 0 to 255.
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7. Color Image (RGB): Color image is Produced from 3
colors red, green and blue.
Each color consists of 8 bit number to represent their
intensity, ranging from 0-255.
i.e. For Red (255,0,0) , Green (0,255,0) Blue(0,0,255).
Example: White: (255,255,255), Decimal
(11111111,11111111,11111111), Binary (FF,FF,FF)
Hexadecimal
In the figure right side, the green intensity increases
along x-axis, and red along y-axis. Blue is set to zero.
Yellow: (255,255,0), (11111111,11111111,00000000),
(FF,FF,00)
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8. Digital Image File Formats
Raster (Bitmap):
Pixel-based images representation
Best for editing photos
Losses of quality while Scaling
.jpeg , .png , .bmp , .tiff , .gif etc ..
Vector:
Mathematical calculations that form shapes
creating logos, drawings and illustrations
Can be scaled to any size without losing quality
.svg , .ai etc..
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9. Raster Graphics
Bitmap use combination blocks of different colors (known as pixels) to
represent an image.
Each pixel is assigned a specific location and color value.
Software to edit bitmapped graphics are :
Adobe Photoshop
Paint
Advantage:
Different textures detailed and comprehensive drawing.
Disadvantage:
Large file size.
Not easy to make modification to objects/drawings.
Graphics become "blocky" when the size is increased.
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10. Vector Graphics
Objects are stored as a series of command that define the Shapes of object
Packages that allow to create vector graphics include :
Macromedia Flash
Adobe Illustrator
Advantage:
Small file size.
Maintain quality as the size of the graphics is increased.
Easy to edit the drawings
Disadvantage:
plain colours or gradients
limited level of detail
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11. Resolution
Resolution is measured in terms of three different aspects
( quality, detail and size of an image)
Image resolution
Display resolution
Color resolution
Image Resolution:
The term Image resolution refers to the image’s degree of detail or quality.
Display Resolution:
Display Resolution refer to quality capability of graphic output (monitor).
Color Resolution / Color Depth:
Color depth describe the number of bits used to represent the color of a single
pixel.
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12. Image Resolution:
Image resolution means how many pixel the image has.
Image resolution is measured in width and height.
For example, 100 * 100-pixel image has a total of 10,000 pixels.
Display Resolution:
It simply means how many pixels can be displayed on the computer
screen.
Display resolution normally uses a setting of 640x480, 800x600,
etc.
Color Resolution:
Number of bits used to represent the color of a single pixel.
2^n number of colors, n represents number of bits.
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13. Image Color Schemes
Color models or formats developed to represent color
mathematically.
There are 4 commonly used color schemes :
RGB Color Scheme
CMY or CMYK Color Scheme
HSB or HSI (Hue, Saturation, Brightness/Intensity)
Color Scheme
YUV Color Scheme
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HSB YUV
14. Color Dithering
Colour Dithering – the process through which colours are changed to
meet the closest available colour based on the available palette.
Usually, digitised images are 24 bit, 16 million colour depth.
If display system is limited to less than 16 million colours, the image must
be transformed for display in the lesser colour environment (colour
dithering).
Colours are substituted with closest available colours (output device).
The quality of dithering will depend on the algorithm used to select the
closest colour.
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15. Image Analysis
Image analysis is concerned with techniques for extracting descriptions about
images:
computation of perceived brightness and color.
partial or complete recovery of 3D data in a scene.
characterization of the properties of uniform regions in a image.
Image Enhancement: improves image quality by eliminating noise (extraneous
or missing pixels) or by enhancing
contrast, i.e. X-ray images, computerized axial tomography (CAT)
Scene Analysis and Computer Vision: deals with recognizing and
reconstructing 3D models of a scene from several 2D images, i.e.
industrial robot sensing (relative sizes, shapes, positions, colors)
Pattern Detection and Recognition: deals with detecting and clarifying
standard patterns and finds distortions from these patterns, i.e.
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17. Formatting:
capturing of an image and transforming to a digital representation
Conditioning:
based on a model that assumes that the observed image is composed of an informative
pattern modified by uninteresting variations
estimates informative pattern based on the observed image
suppresses noise and perform background normalization by suppressing uninteresting
Labelling:
based on a model that assumes that the informative pattern has structure as a spatial
arrangement of events
edge detection, corner detection,
identification of pixels that participate in various shape primitives
Grouping:
identifies events by collecting or identifying maximal connected sets of pixels
determines new sets of entities
changes the logical data structure
entities of interest after grouping are sets of pixels – e.g. line-fitting is a grouping operation, where
are grouped into lines Extracting
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18. Extracting:
computes for each group of pixels a list of properties:
centroid, area, orientation, spatial moments, grey tone moments,
circumscribing circle number of holes in a region, average curvature in an
etc.
measures topological or spatial relationships between two or more
groupings, i.e. clarifies whether two groupings touch, are spatially close or
layered
Matching:
determines the interpretation of some related set of image events
previously with the extracting step
associates events with some given 3D objects or 2D shapes
compares examined pattern with known and stored models and chooses
best match
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19. Conclusion:
Conditioning, labelling, grouping, extracting and matching constitute a
canonical decomposition of the image recognition problem.
Each step prepares and transforms the data to facilitate the next step.
On any level the transformation is an unit process and data are prepared
for the unit transformation to the next higher level.
Depending on the application, the sequence of steps has more than one
level of recognition and description process.
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23. 1. Image acquisition:
to acquire a digital image
2. Image preprocessing:
to improve the image in ways that increase the chances for success
the other processes.
3. Image segmentation:
to partitions an input image into its constituent parts or objects.
4. Image representation:
to convert the input data to a form suitable for computer processing.
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24. 5. Image description:
to extract features that
result in some quantitative information of interest
or features that are basic for differentiating one
class of objects from another.
6. Image recognition:
to assign a label to an
object based on the information provided by its
descriptors.
7. Image interpretation:
to assign meaning to an
ensemble of recognized objects.
Knowledge about a problem domain is coded into
an image processing system in the form of a
knowledge database.
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