Computer vision is the automation of human visual perception to allow computers to analyze and understand digital images. The goal is to emulate the human visual system through techniques like deep learning. Computer vision involves image acquisition, processing, and analysis to interpret images beyond just recording them. It has applications in areas like object detection, facial recognition, medical imaging, and self-driving cars. While it provides advantages like unique customer experiences, it also raises privacy concerns regarding how the data used is collected and stored.

2. CONTENTS
Introduction
Motivation
Goal of Computer Vision
Relation between Computer Vision and Image
Processing
Interdisciplinary Nature of Computer Vision
Methodology
Applications
Advantages
Disadvantages
References

3. INTRODUCTION
Computer Vision (CV) is the automation of
Human Visual System so that computers can
obtain high level of understanding of the
environment from digital images and videos.

4. MOTIVATION
Imagine what would happen if computers could
“see” for themselves, analyze and recognize
events happening around them.

5. So, if we were to ask a CV enabled robot to count the
number of red cars passing by on the highway, it
would just need to look out of the window.
Img src:
https://images.deepai.org/glossary-
terms/068e67da35a647bd8f99b0148d7f99aa/Computer-Vision.png

6. GOAL OF COMPUTER VISION
 The ultimate goal of Computer Vision is to imitate the
great capabilies of the human visual system.
 Computer Vision allows the computer not only to
record images but also to interpret them.
 Deep learning has helped improve on many tasks and
approach new ones in Computer Vision.
 Deep Learning simplifies the process of feature
extraction through mathematical operations
(Convolutions).
 The first demonstration of deep learning in computer
vision was in image recognition, object detection and
face recognition.

7. RELATION BETWEEN COMPUTER
VISION AND IMAGE PROCESSING
Img src:
https://robotacademy.net.au/lesson/image-features/

8. Image processing is a subset of
computer vision.
A computer vision system uses the image processing
algorithms to try and perform emulation of vision at
human scale.

9. For example, if the goal is to enhance the image for
later use, then this may be called image processing.
Img src:
https://www.rsipvision.com/defining-borders/

10. And if the goal is to recognise objects, defect for automatic
driving, then it can be called computer vision.
Img src:
https://fedtechmagazine.com/article/2018/08/computer-vision-how-
feds-can-use-ai-advance-beyond-image-processing-perfcon

11. INTERDISCIPLINARY NATURE OF
COMPUTER VISION
CV can broadly be called a subfield of artificial intelligence and
machine learning.
Img src:
https://machinelearningmastery.com/what-is-computer-vision/

12. Artificial Intelligence : The word Artificial Intelligence
comprises of two words “Artificial” and “Intelligence”.
Artificial refers to something which is made by human or non
natural thing and Intelligence means ability to understand or
think.
Therefore, it is an intelligence where we want to add all the
capabilities to machine that human contain.
Machine Learning : Machine Learning is the learning in which
machine can learn by its own without being explicitly
programmed.
It is an application of AI that provide system the ability to
automatically learn and improve from experience.

13. METHODOLOGY
Computer Vision emulates human vision using digital
images through three main processing components, executed
one after the other i.e. in the form of a pipeline:
1. Image acquisition
2.Image processing
3. Image analysis and understanding

14. As our human visual understanding of world is reflected in our
ability to make decisions through what we see, providing such a
visual understanding to computers would allow them the same
power :
Img src:
https://hayo.io/computer-vision/

15. Image acquisition
 It is the process of translating the world around us into binary data
composed of zeros and ones, interpreted as digital images.
 Different tools have been created to build such datasets:
1. Webcams & embedded cameras
2. Digital compact cameras & DSLR
3. Consumer 3D cameras & laser range finders
Img src: https://hayo.io/computer-vision/
Webcam DSLR Laser range
finder
3D camera
Embedded
camera

16. Image processing
 Algorithms are applied to the binary data acquired in the first step to
infer information on parts of the image.
 The information is characterized by image edges, point features or
segments, etc. which are the basic geometric elements that build objects
in images.
 This step usually involves advanced applied mathematics algorithms and
techniques.
Img src: https://hayo.io/computer-vision/
Edge detection
in a color image.

17. Image analysis and understanding
 The analysis of the data helps in the decision making process.
 Algorithms are applied, using both the image data and the
information computed in previous steps.
 Examples of image analysis are:
1. 3D scene mapping
2. Object recognition
3. Object tracking
Recognition of objects. 3D mapping of a living room.

18. APPLICATIONS
 Optical Character Recognition (OCR)
 Login without a password

19.  Target Recognition
 Interpretation of satellite images

20.  Traffic Monitoring
 Face Detection

21.  Medical Imaging
 Capturing Digital Photos

22.  Self Driving Cars

23. ADVANTAGES
Improved Online Merchandising
Customers will be able to search via images to find similar
styles to what they’re looking for instead of relying on tags.
Unique Customer Experiences
Services like Snapchat filters are aimed to provide an
experience that can only be considered “unique.”
Seamless Store Experiences
Amazon Go creates a seamless, efficient environment for
shopping. No more waiting in long lines, dealing with cashiers,
or worrying about handling your wallet.

24. DISADVANTAGES
The computer vision disadvantages regard a hefty issue in
the modern age: privacy.
The driving force that makes computer vision effective
also leads to doubt whether it should be pursued.
By gathering and learning from thousands of photos,
videos, etc., everything we do is stored or owned by
corporations.
Users need to become more aware of what sort of data
they put out into the world.

25. REFERENCES
https://machinelearningmastery.com/what-is-computer-vision/
https://deepai.org/machine-learning-glossary-and-
terms/computer-vision
https://www.skyfilabs.com/blog/what-is-computer-vision
https://www.geeksforgeeks.org/difference-between-machine-
learning-and-artificial-intelligence/
https://freecontent.manning.com/computer-vision-pipeline-
part-1-the-big-picture/
https://www.analyticsindiamag.com/what-is-the-difference-
between-computer-vision-and-image-processing/
https://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/