Download Lec 1- Introduction to Computer Vision

Lecture # 01
Introduction to Digital Image
Processing & Computer Vision.
Syllabus:
 Course goals:
 Cover basic topics of computer vision, and
introduce some fundamental approaches for
computer vision research.
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Basics of an Image
Imaging Geometry
Camera Modeling
Filtering and Enhancing Images
Region Segmentation
References:
1)
Digital Image Processing
By R.C. Gonzalez and R.E.Woods 2003, Pearson Edition
2)
Digital Image Processing Using MATLAB
By: Gonzalez, Woods, and Eddins, Prentice Hall, 2004
http://www.imageprocessingplace.com/
3)
Fundamentals of Computer Vision
By: Dr. Mubarak Shah http://www.cs.ucf.edu/~vision/
4)
Computer Vision: a Modern Approach Forsyth,
By: D. A & Ponce, J.Prentice Hall.
5)
Computer Vision” By:
Adrian Low
Introduction To Digital Image Processing
Two principal application areas are:
1.
2.
Improvement of pictorial information for human interpretation
Processing of image data for storage, transmission, and
representation for autonomous machine perception
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Vision is the most advanced of our senses.
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Images play the most important role in human perception.
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Humans are limited to the visual band of the electromagnetic (EM)
spectrum.
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Imaging machines cover almost the entire EM spectrum, ranging
from gamma to radio waves.
Introduction To Digital Image Processing
They can operate on images generated by sources that humans are not
accustomed to associating with images.
These include ultrasound, electron microscopy, and computer-generated
images.
Thus, digital image processing encompasses a wide and varied field of
applications.
There is no general agreement among authors regarding where image
processing stops and other related areas, such as image analysis and
computer vision, start.
Introduction To Computer Vision
 Ultimate goal of computer vision is to use computers to emulate
human vision, including learning and being able to make inferences
and take actions based on visual inputs.
 This area itself is a branch of artificial intelligence (AI) whose
objective is to emulate human intelligence.
 The area of image analysis (also called image understanding) is in be
tween image processing and computer vision.
 There are no clear-cut boundaries in the continuum from image
processing at one end to computer vision at the other. However, one
useful paradigm is to consider three types of computerized processes.
Introduction To Digital Image Processing
These processes are:
1. Low Level processes
2. Medium Level processes
3. High Level processes
 Low-level processes involve primitive operations such as image
preprocessing to reduce noise, contrast enhancement, and image
sharpening. A low-level process is characterized by the fact that both
its inputs and outputs are images.
 Mid-level processing on images involves tasks such as segmentation
(partitioning an image into regions or objects), description of those
objects to reduce them to a form suitable for computer processing,
and classification (recognition) of individual objects. A mid-level
process is characterized by the fact that its inputs generally are
images, but its outputs are attributes extracted from those images
(e.g., edges, contours, and the identity of individual objects).
Introduction To Digital Image Processing
 Finally, higher-level processing involves "making sense" of an
ensemble of recognized objects, as in image analysis, and, at the far
end of the continuum, performing the cognitive functions normally
associated with vision.
Vision:
Vision is the process of discovering , what is present in the
world and where it is.
perception is the process of acquiring, interpreting, selecting,
and organizing sensory information.
Visual System:
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The visual system allows us to assimilate information from the
environment.
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The act of seeing starts when the lens of the EYE focus an
image of the outside world onto a light-sensitive membrane in
the back of the eye, called the Retina.
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The retina is actually part of the brain that is isolated to serve
as a transducer for the conversion of patterns of light into
neuronal signals.
Visual System:
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The lens of the Eye focuses light on the photo receptive cells of
the retina, which detect the photons of light and respond by
producing neural Impulses.
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These signals are processed in a hierarchical fashion by
different parts of the brain, such as the lateral geniculate
nucleus, and the primary and secondary visual cortex of the
brain.
Visual System:
Color Vision:
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Color vision is the capacity of an organism or machine to
distinguish objects based on the wavelengths (or frequencies)
of the light they reflect or emit.
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The nervous system derives color by comparing the responses
to light from the several types of cone photoreceptors in the
eye.
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For humans, the visible spectrum ranges approximately from
380 to 750 nm.
Color Vision:
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A 'red' apple does not emit red light. Rather, it simply absorbs
all the frequencies of visible light shining on it except for a
group of frequencies that is perceived as red, which are
reflected.
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An apple is perceived to be red, only, because the human Eye
can distinguish between different wavelengths.
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Three things are needed to see color
 a light source,
 a detector (e.g. the Eye)
 a sample to view.
Computer Vision:
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Computer vision is the science and technology of machines
that see.
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Computer Vision is the study of analysis of pictures and
videos in order to achieve results similar to those as by human.
Computer Vision:
Sub-domains of computer vision include:
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Image Acquisition
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Image restoration
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Object recognition
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Scene reconstruction.
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Event detection and tracking.
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Motion and 3-Dimmensional aspects.
Computer Vision:
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Since perception can be seen as the extraction of information
from sensory signals,
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computer vision can be seen as the scientific investigation of
artificial systems for perception from images or multidimensional data
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Computer vision can also be described as a complement of
Biological Vision, as computer vision, studies and describes
artificial vision system that are implemented in software and/or
hardware.
Related Disciplines:

Image processing
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Computer graphics
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Pattern recognition
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Artificial intelligence
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Applied mathematics
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Learning
Related Disciplines:
Related Fields of an Image:
Input Image
Image Description
Image
Image
Image Processing
Output Image
Image
Computer Graphics
Pattern Recognition
Computer Vision
Description Statistics
Action
Applications Areas of Computer Vision:
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Law enforcement.
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Nuclear medicine and Defense.
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Automatic character recognition.
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Industrial applications (machine vision).
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Satellite imagery for weather prediction .
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Solving problems with machine perception .
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Enhance the contrast or code the intensity levels into
color for easier interpretation.
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Interpretation of X-rays and other Images used in
industry, medicine and biological sciences
Conclusion:
Ultimate goal of Computer Vision is to emulate
human vision, including Learning, to make
inferences and take actions based on visual
input.