1. Digital image processing deals with developing algorithms to perform operations on digital images. A digital image is a two-dimensional array of pixels, where each pixel has a number representing color/intensity. 2. When capturing an image with a digital camera, light is sensed by sensor arrays which generate voltage signals; sampling and quantization convert this into a digital image consisting of a matrix of numbers. 3. Computer vision uses digital image processing techniques to automate tasks like object detection, classification and pattern recognition in images. It has applications in autonomous vehicles, medical imaging, surveillance and more.

1. Computer vision and image
processing
Course code: CoSc4113
Introduction
1

2. What is digital image processing?
• is defined as the use of computerized algorithms for the
analysis of image with respect to an application.
• Deals with processing digital images by means of a digital
computer.
– It is a subfield of signals and systems but focus particularly on
images.
• Main focus: developing a computer system that is able to perform
processing on an image.
• The input of that system is a digital image and the system process
that image using efficient algorithms, and gives an image as an
output.
• Example: Adobe photoshop
2

3. How it works?
3

4. Signal processing
• Signal processing is a discipline in electrical engineering and
in mathematics that deals with analysis and processing of
analog and digital signals , and deals with storing , filtering ,
and other operations on signals.
• These signals include transmission signals , sound or voice
signals , image signals , and other signals e.t.c.
• Out of all these signals , the field that deals with the type of
signals for which the input is an image and the output is
also an image is done in image processing.
• It can be further divided into analog image processing
and digital image processing.
4

5. 1. Analog image processing
• Analog image processing is done on analog signals.
• It includes processing on two dimensional analog signals.
• In this type of processing, the images are manipulated by electrical
means by varying the electrical signal.
• The common example include is the television image.
• DIGITAL IMAGE PROCESSING has dominated over analog
image processing with the passage of time due its wider range of
applications.
2. Digital image processing
• The digital image processing deals with developing a digital
system that performs operations on an digital image.
5

6. What is digital Image?
• An image is nothing more than a two dimensional signal. It is
defined by the mathematical function f(x,y) where x and y are the
two co-ordinates horizontally and vertically.
• The value of f(x,y) at any point is gives the pixel value at that
point of an image.
• What is the relationship between a digital image and a signal?
• Eample of digital image
6

7. How a digital image is formed?
• Since capturing an image from a camera is a physical process.
• The sunlight is used as a source of energy.
• A sensor array is used for the acquisition of the image.
Process?
• So when the sunlight falls upon the object, then the amount of
light reflected by that object is sensed by the sensors, and
• a continuous voltage signal is generated by the amount of sensed
data.
• In order to create a digital image , we need to convert this data
into a digital form.
• This involves sampling and quantization. (They are discussed
later on).
• The result of sampling and quantization results in an two
dimensional array or matrix of numbers which are nothing but a
digital image.
7

8. Answer
• Relationship between a digital image and a signal
• An image is a two-dimensional array in which color information is arranged
along x and y spatial axis. So, in order to understand how the image is
formed, we should first understand how the signal is formed?
• Signal
• A signal is a mathematical and statistical approach that relates us to the
physical world. It can be measured through its dimensions and time over
space. Signals are used to convey information from one source to another.
• A signal can be measured on one or two-dimensional array or higher
dimensional signal. The common example is a sound, images, and sensor
output signals.
• Here, one-dimensional signals are measured on time over space and two-
dimensional signals are measured on some other physical quantities, for
example, digital image.
• Relationship
• A signal is that which conveys information around us in the physical world, it
can be any voice, images etc. whatever we speak, it first converted into a
signal or wave and then transfer to others in due time period. While
capturing an image in the digital camera, a signal is transferred from one
system to another.
8
Shalom

9. Overlapping fields
• Machine/computer vision
• Computer graphics
• Artificial intelligence
• Signal processing
9

10. • 1. Machine or Computer Vision
• Machine and computer vision are almost the same with a few
other characteristics. It deals with developing a system in
which input is an image and output is some information.
• For example: In an ice cream industry, the vision system can
be used to identify many things during packaging of ice cream
in a cup. It can verify that the empty cup is free of damage. It
can verify the correct level to fill the ice cream. It can check
the label is correctly placed or not. It can verify the cup is
sealed correctly or not.
• Depending on the coding of the software, system can verify
and act.
• 2. Computer graphics
• Computer graphics is a series of an image which is formed
from object models. Instead of capturing images from a
device, images are generated using an object model.
Computer generated images are used for making video
games, movies, advertisement, etc.
10
Shalom

11. • Artificial intelligence
• Artificial Intelligence (AI) is an area of Computer Science that
creates machine?s work and reacts like humans. Computer
scenes are used after the image has been processed to
obtain features. Artificial intelligence has many applications in
image processing.
• For example: X-ray, MRI, CT-Scan, etc. are in the form of the
image which helps the doctor in treatment.
• Signal processing
• Signal processing includes extracting, manipulating, and
storing information that has complex signals and images.
There are many applications of signals in image processing.
• For Example: In the field of medicine, there are both one and
multidimensional signals such as EEG, ultrasound signals,
etc. and 2 and 3-dimensional images such as ultrasound
images, holographic images, etc.
11
Shalom
Shalom

12. Computer vision
12

13. Introduction
• It is an interdisciplinary scientific field that deals with how
computers can be made to gain a high level understanding of
digital images or videos.
• From the perspective of engineering, it seeks to automate tasks
that the human visual system can do.
• Computer vision tasks include method for acquiring, processing,
analyzing and understanding digital images.
• Other definition through its applications:
– Computer vision is building algorithms that can understand
the content of images and use it for other applications.
13

14. How computer vision works
 Acquiring an image: Images, even large sets, can be acquired in
real-time through video, photos or 3D technology for analysis.
 Processing the image: Deep learning models automate much
of this process, but the models are often trained by first being
fed thousands of labeled or pre-identified images.
 Understanding the image: The final step is the interpretative
step, where an object is identified or classified.
14

15. There are many types of computer vision that are used in
different ways:
• Image segmentation partitions an image into multiple regions or
pieces to be examined separately.
• Object detection identifies a specific object in an image.
• Advanced object detection recognizes many objects in a single
image:
– a football field,
– an offensive player,
– a defensive
player,
– a ball and so on. T
– these models use an X, Y coordinate to create a bounding
box and identify everything inside the box.
15

16. • Facial recognition is an advanced type of object detection that
not only recognizes a human face in an image but identifies a
specific individual.
• Edge detection is a technique used to identify the outside edge
of an object or landscape to better identify what is in the image.
• Pattern detection is a process of recognizing repeated shapes,
colors and other visual indicators in images.
• Image classification groups images into different categories.
• Feature matching is a type of pattern detection that matches
similarities in images to help classify them.
16

17. Applications of computer vision
• Optical character recognition (OCR): reading handwritten
postal codes on letters and automatic number plate recognition.
• Machine inspection: rapid parts inspection for quality
assurance using stereo vision with specialized illumination to
measure tolerances on aircraft wings or auto body parts or
looking for defects in steel castings using X-ray vision.
• Retail: object recognition for automated checkout lanes.
• Medical imaging: registering pre-operative and intra-operative
imagery or performing long-term studies of people’s brain
morphology as they age
17

18. • Automotive safety: detecting unexpected obstacles such as
pedestrians on the street, under conditions where active vision
techniques such as radar.
• Surveillance: monitoring for intruders, analyzing highway
traffic and monitoring pools for drowning victims;
• Fingerprint recognition and biometrics: for automatic
access authentication as well as forensic applications
18

19. 19

20. 20

21. Applications of Digital Image
Processing
• Image Correction, Sharpening, and Resolution Correction
• Medical field
• Remote sensing
• Transmission and encoding
• Machine/Robot vision
• Color processing
• Pattern recognition
• Video processing
• Microscopic Imaging
• Others
21

22. Image Correction, Sharpening, and Resolution Correction
• Often, we wish we could make old images better. And that possible
nowadays.
• Zooming, sharpening, edge detection, high dynamic range edits all
fall under this category.
• All these steps help in enhancing the image. Most editing software
and Image correction code can do these things easily.
22

23. Medical Technology :
• In the medical field, Image Processing is used for various tasks
like PET scan, X-Ray Imaging, Medical CT, UV imaging, Cancer
Cell Image processing, and much more.
• The introduction of Image Processing to the medical technology
field has greatly improved the diagnostics process.
• The image on the left is the original image. The image on the right
is the processed image.
• We can see that the processed image is far better and can be used
for better diagnostics. 23

24. Computer / Machine Vision :
• One of the most interesting and useful applications of Image
Processing is in Computer Vision.
• Computer Vision is used to make the computer see, identify things,
and process the whole environment as a whole.
• An important use of CV is Self Driving cars, Drones etc.
• CV helps in obstacle detection, path recognition, & understanding
the environment.
• This is how typical Computer Vision works for Car Autopilots. The
computer takes in live footage and analyses other cars, the road,
and other obstacles.
24

25. 25

26. Pattern recognition:
• Pattern recognition is a part of Image Processing that involves
AI and Machine Learning.
• Image processing is used to find out various patterns and aspects
in images.
• Pattern Recognition is used for Handwriting analysis, Image
recognition, Computer-aided medical diagnosis, and much more.
Video Processing:
• Video is basically a fast movement of images.
• Various image processing techniques are used in Video
Processing.
• Some methods of Video Processing are noise removal, image
stabilization, frame rate conversion, detail enhancement, and
much more.
26

27. Reading assignment
• how the three dimension in physical world can
be converted in to two dimensional image?
• Define the process of capturing an image on
digital camera? Elaborate it with the working
principles of human eye.
27

28. • What is quantization
• Quantization is opposite to sampling. It is
done on y axis. When you are quantizing
an image, you are actually dividing a signal
into quanta(partitions).
• On the x axis of the signal, are the co-
ordinate values, and on the y axis, we have
amplitudes. So digitizing the amplitudes is
known as Quantization.
28
Shalom

29. • Sampling.
• The term sampling refers to take samples
• We digitize x axis in sampling
• It is done on independent variable
• In case of equation y = sin(x), it is done on
x variable
• It is further divided into two parts , up
sampling and down sampling
29
Shalom