Md. Monjur –ul-Hasan

Department of Computer Science & Engineering

Chittagong University of Engineering & Technology

Chittagong 4349http://monjur-ul-hasan.info

IMAGES AND GRAPHICS

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

•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

Image Format

•Common image formats 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

Image Format

Bitmap: An array of information that contains the information for the image.

It is a 3 dimensional arrayWidth 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)

Image Format

GIF (Graphical Interchange Format)

For large areas of the same color and a moderate level of detail.Supports up to 256 colorsAllows transparency and interlacingUses lossless compression

PNG (Portable Network Graphic)lossless, portable, well-compressed storage of raster

imagespatent-free replacement for GIF also replace many common uses of TIFFSupport indexed-color, grayscale, and true color images +

an optional alpha channel for transparency

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

Bitmapped vs. JPEG File Sizes

Both images are the same relative size.

900kb

.JPEG High Quality ~700kb

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.

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

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.

Graphics Hardware

•Architecture of Raster Display

Minimum refresh rate 60 Hz is used to avoid flickering

Screen Mosaic Triad Arrangement

Interlaced Projection

Dithering

• 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

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

•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)

Digital Image Processing(DIP)

Low Level Process

Input: ImageOutput: Image

Examples: Noise removal, image sharpening

Mid Level Process

Input: Image Output: Attributes

Examples: Object recognition, segmentation

High Level ProcessInput: 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

Digital Image Processing(DIP)

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

Image Acquisition

Image Restoration

Morphological

Processing

Segmentation

Representation &

Description

Image Enhancemen

t

Object Recognition

Problem Domain

Colour Image

Processing

Image Compression

Image Recognition Steps

Steps:

Formatting

Conditionin

g

Labeling

Grouping

Extracting

Matching

Image Recognition Steps

ConditioningSuppresses noiseBackground normalization

By suppressing uninteresting systematic or pattern variations

LabelingInformative pattern has structure with set of connected pixels.

Region, edge

GroupingThe 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

Image Recognition Steps

ExtractingExtracting operation computes for each group of pixels a list of properties.Example:

CentroidAreaOrientationSpatial moments, gray tone moments, spatial-gray tone momentsCircumscribing circle, inscribing circle,

MatchingDetermines 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

Image Transmission

Raw Image TransmissionSize = spatial resolution X pixel quantization

Compressed image data transmission

JPEG, MPEG

Symbolic image data transformationimage primitive, attribute etc.

Thank You All