Intelligent System Engineering

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19 Οκτ 2013 (πριν από 3 χρόνια και 10 μήνες)

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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
-
/perspective
-
invariance
, 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


-

c
reate
s

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, end
-
point 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!

Scene

Image

Description

Application
feedback

Imaging
device

MACHINE
VISION

Illumination

A typical CV
-
based control system

Machine Vision Stages

Image Acquisition

Image Processing

Image Segmentation

Image Analysis

Pattern Recognition

Analog to digital
conversion

Remove noise,

improve contrast…

Find regions (objects)

in the image

Take measurements of

objects/relationships

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