Understand Digital Image Representation, (Binart, GrayScale, RGB), Color Schemas, Raster, and Vector Graphics, Resolutions, Image Recognition, Image Analysis, and Processing Steps.

1. Fundamentals Into
Image & Graphics
Shrawan Adhikari
https://www.adhikarishrawan.com.np/
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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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16. Image recognition Steps
Image recognition:
 Formatting
 Conditioning
 Marking
 Grouping
 Extraction
 Matching
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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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20. 20
Fig: Digital Image Processing
Digital Image Processing

21. 21
1. Image Acquisition 2. Image Enhancement
4. Morphological Processing
3. Image Restoration
5. Segmentation 6. Object Recognition

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7. Representation & Description 8. Image Compression
9. Colour Image Processing

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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25. THE END
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Thank You