The document discusses the key differences between image processing and computer vision. Image processing involves applying mathematical transformations to images, like smoothing or sharpening, without understanding the image content. Computer vision applies machine learning techniques to computer vision tasks like object recognition, classification, and interpretation of images, aiming to emulate human vision capabilities. While there is overlap, computer vision uses image processing techniques alongside pattern recognition and temporal information processing.

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2.  It is an area of computer science that emphasizes the creation
of intelligent machines that work and react like humans
 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.
 There is a misconception that Artificial Intelligence is a system, but it is not
a system .
 AI is implemented in the system.
 “It is the study of how to train the computers so that computers can do
things which at present human can do better.”Therefore It is a
intelligence where we want to add all the capabilities to machine that
human contain.

3.  The ability to learn without being explicitly programmed
 Machine learning is an application of artificial intelligence (AI) that provides
systems the ability to automatically learn and improve from experience without
being explicitly programmed.
 Machine learning focuses on the development of computer programs that can
access data and use it learn for themselves.
 The process of learning begins with observations or data, such as examples, direct
experience, or instruction, in order to look for patterns in data and make better
decisions in the future based on the examples that we provide.
 The primary aim is to allow the computers learn automatically without human
intervention or assistance and adjust actions accordingly.

4.  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.
 Here we can generate a program by integrating input and output of that
program.
 One of the simple definition of the Machine Learning is “Machine
Learning is said to learn from experience E w.r.t some class of task T and
a performance measure P if learners performance at the task in the class
as measured by P improves with experiences.”

6. ARTIFICIAL INTELLIGENCE MACHINE LEARNING
AI stands for Artificial intelligence,
where intelligence is defined
acquisition of knowledge
intelligence is defined as a ability
to acquire and apply knowledge.
ML stands for Machine Learning
which is defined as the acquisition
of knowledge or skill
The aim is to increase chance of
success and not accuracy.
The aim is to increase accuracy,
but it does not care about success
It work as a computer program
that does smart work
It is a simple concept machine
takes data and learn from data.
The goal is to simulate natural
intelligence to solve complex
problem
The goal is to learn from data on
certain task to maximize the
performance of machine on this
task.

7.  Deep learning is actually a subset of machine learning. It technically is machine
learning and functions in the same way but it has different capabilities.
 The main difference between deep and machine learning is, machine learning
models become better progressively but the model still needs some guidance.
 If a machine learning model returns an inaccurate prediction then the programmer
needs to fix that problem explicitly but in the case of deep learning, the model does
it by himself. Automatic car driving system is a good example of deep learning.
 Let’s take an example to understand both machine learning and deep learning –
Suppose we have a flashlight and we teach a machine learning model that
whenever someone says “dark” the flashlight should be on, now the machine
learning model will analyse different phrases said by people and it will search for
the word “dark” and as the word comes the flashlight will be on but what if
someone said “I am not able to see anything the light is very dim”, here the user
wants the flashlight to be on but the sentence does not the consist the word “dark”
so the flashlight will not be on. That’s where deep learning is different from
machine learning. If it were a deep learning model it would on the flashlight, a deep
learning model is able to learn from its own method of computing.

9.  Machine learning and deep learning is a way of
achieving AI, which means by the use of machine
learning and deep learning we may able to achieve AI
in future but it is not AI.

10.  Supervised machine learning algorithms can
apply what has been learned in the past to new
data using labeled examples to predict future
events. Starting from the analysis of a known
training dataset, the learning algorithm produces
an inferred function to make predictions about
the output values. The system is able to provide
targets for any new input after sufficient training.
The learning algorithm can also compare its
output with the correct, intended output and find
errors in order to modify the model accordingly.

11.  In contrast, unsupervised machine learning
algorithms are used when the information
used to train is neither classified nor labeled.
Unsupervised learning studies how systems
can infer a function to describe a hidden
structure from unlabeled data. The system
doesn’t figure out the right output, but it
explores the data and can draw inferences
from datasets to describe hidden structures
from unlabeled data.

12.  Semi-supervised machine learning
algorithms fall somewhere in between supervised
and unsupervised learning, since they use both
labeled and unlabeled data for training – typically
a small amount of labeled data and a large
amount of unlabeled data. The systems that use
this method are able to considerably improve
learning accuracy. Usually, semi-supervised
learning is chosen when the acquired labeled
data requires skilled and relevant resources in
order to train it / learn from it. Otherwise,
acquiringunlabeled data generally doesn’t require
additional resources.

13.  Reinforcement machine learning algorithms is a
learning method that interacts with its
environment by producing actions and discovers
errors or rewards. Trial and error search and
delayed reward are the most relevant
characteristics of reinforcement learning. This
method allows machines and software agents to
automatically determine the ideal behavior within
a specific context in order to maximize its
performance. Simple reward feedback is required
for the agent to learn which action is best; this is
known as the reinforcement signal.

14.  Machine learning enables analysis of massive
quantities of data. While it generally delivers
faster, more accurate results in order to
identify profitable opportunities or dangerous
risks, it may also require additional time and
resources to train it properly. Combining
machine learning with AI and cognitive
technologies can make it even more effective
in processing large volumes of information.

15.  A human eye has between six and seven million cone cells,
containing one of three colour-sensitive proteins known as
opsins. When photons of light hit these opsins, they
change shape, triggering a cascade that produces
electrical signals, which in turn transmit the messages to
the brain for interpretation.
 This whole process is a very complex phenomenon and
making a machine to interpret this at a human level has
always been a challenge. The motivation behind the
modern-day machine vision system lies at the core of
emulating human vision for recognising patterns, faces
and rendering 2D imagery from a 3D woThere is a lot of
overlap between image processing and computer vision at
the conceptual level and the jargon, often misunderstood,
is being used interchangeably. Here we give a brief
overview of the techniques and explain how they are
different at the fundamental level.rld into 3D.

16.  Digital image processing was pioneered at
NASA’s Jet Propulsion Laboratory in the late
1960s, to convert analogue signals from the
Ranger spacecraft to digital images with
computer enhancement. Now, digital imaging
has a wide range of applications, with
particular emphasis on medicine. Well-known
uses for it include Computed Aided
Tomography (CAT) scanning and ultrasounds.

17.  Image Processing is mostly related to the usage
and application of mathematical functions and
transformations over images regardless of any
intelligent inference being done over the image
itself. It simply means that an algorithm does
some transformations on the image such as
smoothing, sharpening, contrasting, stretching
on the image.
 For a computer, an image is a two-dimensional
signal, made up of rows and columns of pixels.
An input of one form can sometimes be
transformed into another. For instance, Magnetic
Resonance Imaging (MRI), records the excitation
of ions and transforms it into a visual image.

18.  Here’s an example of smoothing images with Python:
 As for one-dimensional signals, images also can be filtered with various
low-pass filters (LPF), high-pass filters (HPF), etc. An LPF helps in
removing noise or blurring the image. An HPF filter helps in finding
edges in an image.
 Via OpenCV documentationThese type of transformations using matrices
are quite prevalent in machine learning algorithms like convolution
neural network. Where a filter is convolved over an image(another matrix
of pixel values) to detect edges or colour intensities.
 Some techniques which are used in digital image processing include:
◦ Hidden Markov models
◦ Image editing and restoration
◦ Linear filtering and Bilateral filtering
 Neural networks

19.  Computer vision comes from modelling image
processing using the techniques of machine
learning. Computer vision applies machine
learning to recognise patterns for interpretation
of images. Much like the process of visual
reasoning of human vision; we can distinguish
between objects, classify them, sort them
according to their size, and so forth. Computer
vision, like image processing, takes images as
input and gives output in the form of information
on size, colour intensity etc.

20.  Below are the components of a standard machine
vision system:
◦ Camera
◦ Lighting devices
◦ Lens
◦ Frame grabber
◦ Image processing software
◦ Machine learning algorithms for pattern recognition
 Display screen or a robotic arm to carry out an
instruction obtained from image interpretation.
 For instance, a video camera mounted on a driverless
car has to detect people in front of it and distinguish
them from vehicles and other distinctive features. Or,
we may want to measure the distance covered by a
tennis player in a game.

21.  Therefore, temporal information plays a
major role in computer vision, much like it is
with our own way of understanding the world.
 The ultimate goal here is to use computers to
emulate human vision, including learning and
being able to make inferences and take
actions based on visual inputs.

22.  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.
For example, if the goal is to enhance the
image for later use, then this may be called
image processing. And if the goal is to
recognise objects, defect for automatic
driving, then it can be called computer vision.