A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
IEEE EED2021 AI use cases in Computer VisionSAMeh Zaghloul
AI Use Cases in Computer Vision
Introduction and Overview about AI Use Cases in Computer Vision, to answer a basic question: “How Machines See?”, covering Neural Networks, Object detection and recognition, Content-based image retrieval, Object tracking, Image restoration, Scene reconstruction, Computer Vision Tools, Frameworks, Pretrained Models, and Public Train/Test Datasets.
With real-project examples on using Computer Vision in Egyptian Hieroglyph Alphabet recognition, Face Recognition/Matching, in addition to hands-on interactive session on Object/Image Tagging/Annotation on Videos/Images to prepare model training dataset.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
IEEE EED2021 AI use cases in Computer VisionSAMeh Zaghloul
AI Use Cases in Computer Vision
Introduction and Overview about AI Use Cases in Computer Vision, to answer a basic question: “How Machines See?”, covering Neural Networks, Object detection and recognition, Content-based image retrieval, Object tracking, Image restoration, Scene reconstruction, Computer Vision Tools, Frameworks, Pretrained Models, and Public Train/Test Datasets.
With real-project examples on using Computer Vision in Egyptian Hieroglyph Alphabet recognition, Face Recognition/Matching, in addition to hands-on interactive session on Object/Image Tagging/Annotation on Videos/Images to prepare model training dataset.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
Mika Kaukoranta presents what computer vision is and how it can be utilized in software testing by gaining high-level understanding from digital images or videos.
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
We create a group presentation for Simulation & Modeling. This presentation has so many related fields as like artificial intelligence ,Information engineering,Neurology, Signal processing etc.
Efficient and accurate object detection has been an important topic in the advancement of computer vision systems.
Our project aims to detect the object with the goal of achieving high accuracy with a real-time performance.
In this project, we use a completely deep learning based approach to solve the problem of object detection.
The input to the system will be a real time image, and the output will be a bounding box corresponding to all the objects in the image, along with the class of object in each box.
Objective -
Develop a application that detects an object and it can be used for vehicles counting, when the object is a vehicle such as a bicycle or car, it can count how many vehicles have passed from a particular area or road and it can recognize human activity too.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
Mika Kaukoranta presents what computer vision is and how it can be utilized in software testing by gaining high-level understanding from digital images or videos.
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
We create a group presentation for Simulation & Modeling. This presentation has so many related fields as like artificial intelligence ,Information engineering,Neurology, Signal processing etc.
Efficient and accurate object detection has been an important topic in the advancement of computer vision systems.
Our project aims to detect the object with the goal of achieving high accuracy with a real-time performance.
In this project, we use a completely deep learning based approach to solve the problem of object detection.
The input to the system will be a real time image, and the output will be a bounding box corresponding to all the objects in the image, along with the class of object in each box.
Objective -
Develop a application that detects an object and it can be used for vehicles counting, when the object is a vehicle such as a bicycle or car, it can count how many vehicles have passed from a particular area or road and it can recognize human activity too.
In this presentation, we walk through what is Deep Learning in General, we see the anatomy of a typical Deep Learning Neural Network, how is it trained, how do we get the inference, optimisation of parameters, and regularising it. Then we dive deep into the Face Recognition technology, different paradigms and aspects of it. How do we train it, how are the features extracted, etc. We talk about the security as well.
The model explains how we can Automate System using Artificial Intelligence.
It broadly concerns about:-
1. Lane Detection.
2. Traffic Sign Classification.
3. Behavioural Cloning.
Computer Vision and various subcategories will have drastic changes in the future, and will surely lead to the betterment of services. Along with increased capacity, future algorithms will be easy to train on such massive data. The intervention of other technologies of the same sub-family will lead to surprising results.
So let us study what is computer vision and how it works.
https://www.datatobiz.com/blog/what-is-computer-vision/
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
Face recognition is a technology that involves identifying or verifying the identity of a person by analyzing and comparing patterns in their facial features. This process typically involves the use of computer algorithms and machine learning techniques, such as neural networks, to analyze facial images and extract key features that are unique to each individual's face. These features are then compared against a database of known faces to determine the identity of the person in question.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
2. What is image recognition?
o Image Recognition is a technology that strives to acquire, process, analyse and
understand images and high-dimensional data from real world in order to produce
numerical or symbolic information
o In other words it is a process of identifying and detecting an object or a feature in a
digital image or video
o It is also known as Computer Vision
3. Why we need image recognition?
o Image recognition is a vital component in robotics such as the driverless vehicles or
domestic robots. It is also important in security systems such as face recognition
o In image search engines such as Google or Bing image search whereby you use rich image
content to query for similar stuff. Like in Google photos where the system uses image
recognition to categorize your images into things like cats, dogs, people and so on
o In medical imaging such as cancer detection in x-ray images to assist doctors
o In robotic navigation systems to track motion of objects or camera tracking
o Image recognition is great for marketers in order to optimize all of their marketing
strategies. By implementing logo detection, they can gain much clearer brand insights,
data, and metrics that they wouldn’t have if they weren’t using image recognition
technology
o automatic panorama stitching, is used in commercial panorama software such as Adobe
Photoshop to recover 3D camera rotations and camera distortion matrices in order to
align images into a very wide-angle panoramas
4. Why we need image recognition?
• Marketers can track how well a sponsorship is doing with image recognition
and logo detection which makes it much easier to figure out how much
revenue they will return
• Over 85% of logos within images posted to social media don’t contain any tag
or text brand mention
5. How image recognition works?
Image Recognition Using Machine Learning:
A machine learning approach to image recognition involves identifying and extracting key
features from images and using them as input to a machine learning model
Image Recognition Using Deep Learning:
A deep learning approach to image recognition may involve the use of a convolutional
neural network to automatically learn relevant features from sample images and
automatically identify those features in new images
Fig. denoting image recognition using Machine Learning
6. Machine Learning vs Deep Learning
• Machine learning uses algorithms to parse data, learn from that data, and make
informed decisions based on what it has learned
• Deep learning structures algorithms in layers to create an artificial “neural
network” that can learn and make intelligent decisions on its own
• Deep learning is a subfield of machine learning. While both fall under the broad
category of artificial intelligence, deep learning is the term that’s often used to
describe how human-like artificial intelligence works
Fig. denoting image recognition using Deep Learning
7. Neural Network
o A neural network is a system of interconnected artificial “neurons” that
exchange messages between each other
o The connections have numeric weights that are tuned during the training process, so
that a properly trained network will respond correctly when presented with an image
or pattern to recognize
o The network consists of multiple layers of feature-detecting “neurons”. Each layer
has many neurons that respond to different combinations of inputs from the
previous layers
o Typical CNNs use 5 to 25 distinct layers of pattern recognition
9. Understanding CNN
• First, the computer tries to identify very simple
aspects of the images: lines, edges, corners, blobs,
etc. Using that information, we build up into slightly,
just slightly more complex shapes: squares, circles,
triangles
• After a few iterations, it starts to recognize high-
level features such as eyes, nose, mouth, etc. Finally,
by putting all the pieces together, it computes a
probability score for this image for each class of
objects it could belong to (e.g., cat, dog, bird, etc)
10. Understanding CNN
• Now the computer sees the image as an array of
pixels values. Let’s say the cat image we saw earlier
is of size 10x10x3 (where 3 represents the three
RGB values). Then the pixel value representation, for
one of the 3 RGB color channels, would look
something like this:
• Then, it scans this entire image a bunch of times,
each time looking for one specific feature
• There are a few patterns that the computer is
interested in: blobs, circles, colors, and edges. It
prepares a few reference objects where each
represents a blob, a circle, a color, an edge, etc. It
puts the reference object on the image and scans
over the image, looking for areas of overlap
between the reference and the scanned region
11. Understanding CNN
• This is how the computer looks for areas of overlap
between the reference and the scanned region
• In deep learning, this “reference object” is called
a filter (also referred to as kernel), and the part of
the image that is being compared to is called
a receptive field
I have a filter that tries to identify round shapes, then
my filter might look like this:
12. Understanding CNN
Applying this filter on a part of the image: This image denotes a dot product
between the filter and the receptive
field to compute how much they
overlap
Once the other filters like color, blobs and edges are computed the first layer of
convolution has been completed. This is called an activation map.
Since only one filter won’t be enough to identify other features Thus this process
repeats and more convolutional layers are formed.
16. Future Prospect and Conclusion
• Google Self-Driven Cars
• fully automated machinery used in factories
• In space exploration
• AI powered robots
• Face recognition based ATM
Image recognition is a futuristic and relatively unexplored field, with wide areas of
practical applications, including industrial, scientific and medical applications.
This field has a lot of potential for development and implementation in new areas
like space exploration, processing signal images, computer vision etc.