Transcript No Slide Title - Md. Atiqur Rahman Ahad

APECE-505
Intelligent System Engineering
Basics of Digital Image Processing!
Md. Atiqur Rahman Ahad
Reference books:
– Digital Image Processing, Gonzalez & Woods.
- Digital Image Processing, M. Joshi
- Computer Vision – a modern approach, Forsyth & Ponce
Syllabus:
1. Expert system
2. Neural networks
3. Fuzzy logic
4. Robot vision – Intro, 2-stages of robot
vision, image processing, genetic/pattern
discovery program, scene analysis,
interpreting line & curves in the image,
model-based vision
5. Genetic Algorithm
Computer / Robot / Machine vision
vs.
Human vision
Machine vs. Human
Camera vs. Eye
Computer/Processor vs. Brain
Artificial intelligence vs. Human brain…
- Very difficult for a machine – as object varies, number of object
varies, dimensional issues, view-/illumination-/angle-/perspectiveinvariance, etc.
• Computer vision
– Endowing machines with the means to “see”
• Create an image of a scene and extract features
– Very difficult problem for machines
• Several different scenes can produce identical images.
• Images can be noisy .
• Cannot directly ‘invert’ the image to reconstruct the scene.
• CV 
- creates a model of the real world from images
- recovers useful information about a scene from its two
dimensional projections
• Finding out objects in the scene
– Looking for “edges” in the image
• Edge: a part of the image across which the image intensity or some other property
of the image changes abruptly.
– Attempting to segment the image into regions.
• Region: a part of the image in which the image intensity or some other property of
the image changes only gradually.
1. Image processing stage – transform the original image into
something that can be helpful for scene analysis
- Interpreting lines  edge detection, edge accumulation, endpoint identification
- Curves analysis  junctions
2. Scene Analysis stage – attempt to create an iconic [build a
model] or a feature-based description of the original scene,
providing a task-specific information
Robot-player
Identify lines, corners
Identify the ball [ellipse or circle]
Identify players – opponents!
A typical CV-based control system
MACHINE
VISION
Imaging
device
Scene
Image
Illumination
Application
feedback
Description
Machine Vision Stages
Image Acquisition
Analog to digital
conversion
Image Processing
Remove noise,
improve contrast…
Image Segmentation
Find regions (objects)
in the image
Image Analysis
Take measurements of
objects/relationships
Pattern Recognition
Match the description with
similar description of known
objects (models)
Model-based vision:
Considering various models and fit into it.
- Cylindrical, stick model, etc.
- e.g., Hierarchical representation through smaller cylinders to
recreate a person
Stereo vision & depth information:
- Stereo vision has two or more cameras
- Depth info from a single camera is difficult or almost
impossible – though through texture analysis, it might be
possible a bit
- Depth  calculate the distance of foreground objects – far or
closer!
- Stereo vision – key constraint is correspondence problem or
registration problem