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.
Ai artificial intelligence professional vocabulary collection - NuAIgRuchi Jain
The field of artificial intelligence continues to expand, standing on the edge of the precipice of mainstream breakthroughs.
AI will be more involved in our day today life in the near future.
NuAIg Consulting helps you weave AI fabric in CX and auxillary operation with vertical best fit effective solutions to simplify AI adoption
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
Ai artificial intelligence professional vocabulary collection - NuAIgRuchi Jain
The field of artificial intelligence continues to expand, standing on the edge of the precipice of mainstream breakthroughs.
AI will be more involved in our day today life in the near future.
NuAIg Consulting helps you weave AI fabric in CX and auxillary operation with vertical best fit effective solutions to simplify AI adoption
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
Ai artificial intelligence professional vocabulary collectionRuchi Jain
AI is expanding with an edge on the mainstream breakthrough. AI will be involved in all spheres of our life in future. It is important for us to understand what AI is, what it’s terms means, and what are the AI terminologies. Below are some AI terms.
We, NuAIg helps businesses to reap the benefit of AI for their revenue growth with cost reduction.
Artificial Intelligence Vs Machine Learning Vs Deep Learningvenkatvajradhar1
This technology is no longer a matter of science fiction. Instead, we see artificial intelligence in every part of our lives. Smart assistants are on our phones and speakers, helping us find information and complete everyday tasks. At work, chatbots are affiliated with the Customer Support Team, with estimates that they will be responsible for 85% of customer service by next year.
This technology is no longer a matter of science fiction. Instead, we see artificial intelligence in every part of our lives. Smart assistants are on our phones and speakers, helping us find information and complete everyday tasks. At work, chatbots are affiliated with the Customer Support Team, with estimates that they will be responsible for 85% of customer service by next year.
Artificial Intelligence (AI) Interview Questions and Answers | EdurekaEdureka!
(** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-progra... **)
This PPT on Artificial Intelligence Interview Questions covers all the important concepts involved in the field of AI. This PPT is ideal for both beginners as well as professionals who want to learn or brush up their knowledge on AI concepts. Below are the topics covered in this tutorial:
1. Artificial Intelligence Basic Level Interview Question
2. Artificial Intelligence Intermediate Level Interview Question
3. Artificial Intelligence Scenario based Interview Question
Check out the entire Machine Learning Playlist: https://bit.ly/2NG9tK4
Follow us to never miss an update in the future.
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Machine Learning approach for Assisting Visually ImpairedIJTET Journal
Abstract- India has the largest blind population in the world. The complex Indian environment makes it difficult for the people to navigate using the present technology. In-order to navigate effectively a wearable computing system should learn the environment by itself, thus providing enough information for making visually impaired adapt to the environment. The traditional learning algorithm requires the entire percept sequence to learn. This paper will propose algorithms for learning from various sensory inputs with selected percept sequence; analyze what feature and data should be considered for real time learning and how they can be applied for autonomous navigation for blind, what are the problem parameters to be considered for the blind navigation/protection, tools and how it can be used on other application.
Facial Emotion Recognition using Convolution Neural NetworkYogeshIJTSRD
Facial expression plays a major role in every aspect of human life for communication. It has been a boon for the research in facial emotion with the systems that give rise to the terminology of human computer interaction in real life. Humans socially interact with each other via emotions. In this research paper, we have proposed an approach of building a system that recognizes facial emotion using a Convolutional Neural Network CNN which is one of the most popular Neural Network available. It is said to be a pattern recognition Neural Network. Convolutional Neural Network reduces the dimension for large resolution images and not losing the quality and giving a prediction output whats expected and capturing of the facial expressions even in odd angles makes it stand different from other models also i.e. it works well for non frontal images. But unfortunately, CNN based detector is computationally heavy and is a challenge for using CNN for a video as an input. We will implement a facial emotion recognition system using a Convolutional Neural Network using a dataset. Our system will predict the output based on the input given to it. This system can be useful for sentimental analysis, can be used for clinical practices, can be useful for getting a persons review on a certain product, and many more. Raheena Bagwan | Sakshi Chintawar | Komal Dhapudkar | Alisha Balamwar | Prof. Sandeep Gore "Facial Emotion Recognition using Convolution Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39972.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/39972/facial-emotion-recognition-using-convolution-neural-network/raheena-bagwan
How to implement artificial intelligence solutionsCarlos Toxtli
In this presentation, we show how a novice can learn artificial intelligence and implement the basic principles in real-world solutions. There is an easy quick start guide.
What Will I Learn?
How Machine learning works.
What are some simple applications of Machine learning?
What are the ethics of Machine learning?
How big is the future of Machine learning?
Who is the target audience?
People who are progressing their journey towards machine learning
Where there is data and it needs to be analyzed, Machine learning is the best way to do so.
Benefits
Data Science sector is increasing rapidly, so is the demand of people who can write algorithms to analyze that data.
With the increasing amount of data, the accuracy of the result has to be increased.
Ai artificial intelligence professional vocabulary collectionRuchi Jain
AI is expanding with an edge on the mainstream breakthrough. AI will be involved in all spheres of our life in future. It is important for us to understand what AI is, what it’s terms means, and what are the AI terminologies. Below are some AI terms.
We, NuAIg helps businesses to reap the benefit of AI for their revenue growth with cost reduction.
Artificial Intelligence Vs Machine Learning Vs Deep Learningvenkatvajradhar1
This technology is no longer a matter of science fiction. Instead, we see artificial intelligence in every part of our lives. Smart assistants are on our phones and speakers, helping us find information and complete everyday tasks. At work, chatbots are affiliated with the Customer Support Team, with estimates that they will be responsible for 85% of customer service by next year.
This technology is no longer a matter of science fiction. Instead, we see artificial intelligence in every part of our lives. Smart assistants are on our phones and speakers, helping us find information and complete everyday tasks. At work, chatbots are affiliated with the Customer Support Team, with estimates that they will be responsible for 85% of customer service by next year.
Artificial Intelligence (AI) Interview Questions and Answers | EdurekaEdureka!
(** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-progra... **)
This PPT on Artificial Intelligence Interview Questions covers all the important concepts involved in the field of AI. This PPT is ideal for both beginners as well as professionals who want to learn or brush up their knowledge on AI concepts. Below are the topics covered in this tutorial:
1. Artificial Intelligence Basic Level Interview Question
2. Artificial Intelligence Intermediate Level Interview Question
3. Artificial Intelligence Scenario based Interview Question
Check out the entire Machine Learning Playlist: https://bit.ly/2NG9tK4
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Machine Learning approach for Assisting Visually ImpairedIJTET Journal
Abstract- India has the largest blind population in the world. The complex Indian environment makes it difficult for the people to navigate using the present technology. In-order to navigate effectively a wearable computing system should learn the environment by itself, thus providing enough information for making visually impaired adapt to the environment. The traditional learning algorithm requires the entire percept sequence to learn. This paper will propose algorithms for learning from various sensory inputs with selected percept sequence; analyze what feature and data should be considered for real time learning and how they can be applied for autonomous navigation for blind, what are the problem parameters to be considered for the blind navigation/protection, tools and how it can be used on other application.
Facial Emotion Recognition using Convolution Neural NetworkYogeshIJTSRD
Facial expression plays a major role in every aspect of human life for communication. It has been a boon for the research in facial emotion with the systems that give rise to the terminology of human computer interaction in real life. Humans socially interact with each other via emotions. In this research paper, we have proposed an approach of building a system that recognizes facial emotion using a Convolutional Neural Network CNN which is one of the most popular Neural Network available. It is said to be a pattern recognition Neural Network. Convolutional Neural Network reduces the dimension for large resolution images and not losing the quality and giving a prediction output whats expected and capturing of the facial expressions even in odd angles makes it stand different from other models also i.e. it works well for non frontal images. But unfortunately, CNN based detector is computationally heavy and is a challenge for using CNN for a video as an input. We will implement a facial emotion recognition system using a Convolutional Neural Network using a dataset. Our system will predict the output based on the input given to it. This system can be useful for sentimental analysis, can be used for clinical practices, can be useful for getting a persons review on a certain product, and many more. Raheena Bagwan | Sakshi Chintawar | Komal Dhapudkar | Alisha Balamwar | Prof. Sandeep Gore "Facial Emotion Recognition using Convolution Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39972.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/39972/facial-emotion-recognition-using-convolution-neural-network/raheena-bagwan
How to implement artificial intelligence solutionsCarlos Toxtli
In this presentation, we show how a novice can learn artificial intelligence and implement the basic principles in real-world solutions. There is an easy quick start guide.
What Will I Learn?
How Machine learning works.
What are some simple applications of Machine learning?
What are the ethics of Machine learning?
How big is the future of Machine learning?
Who is the target audience?
People who are progressing their journey towards machine learning
Where there is data and it needs to be analyzed, Machine learning is the best way to do so.
Benefits
Data Science sector is increasing rapidly, so is the demand of people who can write algorithms to analyze that data.
With the increasing amount of data, the accuracy of the result has to be increased.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
Hot Topics in Machine Learning for Research and ThesisWriteMyThesis
Machine Learning is a hot topic for research for research. There are various good thesis topics in Machine Learning. Writemythesis provides thesis in Machine Learning along with proper guidance in this field. Find the list of thesis topics in this document.
http://www.writemythesis.org/master-thesis-topics-in-machine-learning/
Deep learning vs. machine learning what business leaders need to knowSameerShaik43
Artificial intelligence isn’t the future — it is the present. Already, businesses are deploying AI tools in a variety of ways: improving marketing and sales, guiding research and development, streamlining IT, automating HR and more.
https://www.tycoonstory.com/technology/deep-learning-vs-machine-learning-what-business-leaders-need-to-know/
Unlocking the Potential of Artificial Intelligence_ Machine Learning in Pract...eswaralaldevadoss
Machine learning is a subset of artificial intelligence that involves training computers to learn from data and make predictions or decisions based on that data. It involves building algorithms and models that can learn patterns and relationships from data and use that knowledge to make predictions or take actions.
Here are some key concepts that can help beginners understand machine learning:
Data: Machine learning algorithms require data to learn from. This data can come from a variety of sources such as databases, spreadsheets, or sensors. The quality and quantity of data can greatly impact the accuracy and effectiveness of machine learning models.
Training: In machine learning, training involves feeding data into a model and adjusting its parameters until it can accurately predict outcomes. This process involves testing and tweaking the model to improve its accuracy.
Algorithms: There are many different algorithms used in machine learning, each with its own strengths and weaknesses. Common machine learning algorithms include decision trees, random forests, and neural networks.
Supervised vs. Unsupervised Learning: Supervised learning involves training a model on labeled data, where the desired outcome is already known. Unsupervised learning, on the other hand, involves training a model on unlabeled data and allowing it to identify patterns and relationships on its own.
Evaluation: After training a model, it's important to evaluate its accuracy and performance on new data. This involves testing the model on a separate set of data that it hasn't seen before.
Overfitting vs. Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, leading to poor performance on new data. Underfitting occurs when a model is too simple and fails to capture important patterns in the data.
Applications: Machine learning is used in a wide range of applications, from predicting stock prices to identifying fraudulent transactions. It's important to understand the specific needs and constraints of each application when building machine learning models.
Overall, machine learning is a powerful tool that can help businesses and organizations make more informed decisions based on data. By understanding the basic concepts and techniques of machine learning, beginners can begin to explore the potential applications and benefits of this exciting field.
Everything You Need to Know About Computer VisionKavika Roy
https://www.datatobiz.com/blog/computer-vision-guide/
To most, they consist of pixels only, but digital images, like any other form of content, can be mined for data by computers. Further, they can also be analyzed afterward. Use image processing methods, including computers, to retrieve the information from still photographs, and even videos. Here we are going to discuss everything you must know about computer vision.
There are two forms-Machine Vision, which is this tech’s more “traditional” type, and Computer Vision (CV), a digital world offshoot. While the first is mostly for industrial use, as an example are cameras on a conveyor belt in an industrial plant, the second is to teach computers to extract and understand “hidden” data inside digital images and videos.
Facebook this August said it was open-sourcing its work to improve its Computer Visiontechnology software for users further. This image was posted by FB Research scientist Piotr Dollar to explain the difference between human and computer vision.
Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take big leaps in recent years, and in some tasks related to detection and labeling of objects has been able to surpass humans.
One of the driving factors behind computer vision development is the amount of data we produce now, which will then get used to educate and develop computer vision.
Hot Topics in Machine Learning For Research and thesisWriteMyThesis
Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields. A lot of research work is going on in this field. For more information, check out the PPT details...
Machine Learning is a fascinating field that has been making headlines for its incredible advancements in recent years. Whether you're a tech enthusiast or just curious about how machines can learn, this article will provide you with a simple and easy-to-understand overview of some key Machine Learning concepts. Think of it as your first step towards a Machine Learning Complete Course!
The Ultimate Guide to Machine Learning (ML)RR IT Zone
Machine learning is a broad term that refers to a variety of techniques that computers learn to do. These include speech recognition, natural language processing, and computer vision. But it’s also the concept behind things like Google Search, and Facebook’s Like button. With machine learning, machines can learn to do things that only humans can do. For example, your smartphone can translate languages with a combination of artificial intelligence, big data, and the internet. It can identify faces in photos, recognize text, and analyze other information—all without human intervention. In addition, machine learning is used to train robots, predict customer behavior, and even build virtual reality environments.
Introduction to Artificial Intelligence describing domains of AI including machine learning , deep learning , natural language processing , speech recognition.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
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.”
5.
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.
8.
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.