Computer vision is an artificial intelligence technology that allows computers to analyze visual data and understand situations. Computer vision tools like OpenCV, TensorFlow, Keras and MXNet use machine learning algorithms to perform tasks like object detection, image segmentation, and image classification. Real-life applications of computer vision include retail shelf analysis, luggage screening at airports, automatic video tagging for personalized ads, real estate valuation from photos, facial recognition for security, and extracting data from identity cards.
Interactive business intelligence visualizations with R Shiny and beyond with scalable big data architectures. Going beyond MS Excel and other non-scalable proprietary solutions.
Journal club done with Vid Stojevic for PointNet:
https://arxiv.org/abs/1612.00593
https://github.com/charlesq34/pointnet
http://stanford.edu/~rqi/pointnet/
Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing.
Alternative download link:
https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0
Outlining the common challenges encountered when structuring clinical and research datasets for deep learning training.
Typically the datasets are so unstructured that they are impossible to analyze by any deep learning practitioners. And the cleaning and data wrangling ends up taking most of the time which could have been planned properly even before the clinical data acquisition.
One could argue that especially for medical data, the annotated data is the new gold, and not just the Big Data scattered all over the place. This is practice translates to efforts to design as intelligent as possible data labelling pipelines for efficient use of expert clinician annotation work.
Alternative download link:
https://www.dropbox.com/s/bbgc21yc86h0t14/Efficient_Ocular_Data_Labelling.pdf?dl=0
Using synthetic data for computer vision model trainingUnity Technologies
During this webinar Unity’s computer vision team provides an overview of computer vision, walks through current real-world data workflows, and explains why companies are moving toward synthetically generated data as an alternate data source for model training.
Watch the webinar: https://resources.unity.com/ai-ml/cv-webinar-dec-2021
Shallow introduction for Deep Learning Retinal Image AnalysisPetteriTeikariPhD
Overview of retinal imaging techniques such as fundus photography, optical coherence tomography (OCT) along with future upgrades such as multispectral imaging, OCT angiography, adaptive optics imaging and polarization-sensitive OCT. This is followed by an overview of deep learning image analysis methods suitable to be used with retinal imaging techniques.
Alternative download link: https://www.dropbox.com/s/n01w02cjaf68vbo/retina_deepLearning_pipeline.pdf?dl=0
Interactive business intelligence visualizations with R Shiny and beyond with scalable big data architectures. Going beyond MS Excel and other non-scalable proprietary solutions.
Journal club done with Vid Stojevic for PointNet:
https://arxiv.org/abs/1612.00593
https://github.com/charlesq34/pointnet
http://stanford.edu/~rqi/pointnet/
Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing.
Alternative download link:
https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0
Outlining the common challenges encountered when structuring clinical and research datasets for deep learning training.
Typically the datasets are so unstructured that they are impossible to analyze by any deep learning practitioners. And the cleaning and data wrangling ends up taking most of the time which could have been planned properly even before the clinical data acquisition.
One could argue that especially for medical data, the annotated data is the new gold, and not just the Big Data scattered all over the place. This is practice translates to efforts to design as intelligent as possible data labelling pipelines for efficient use of expert clinician annotation work.
Alternative download link:
https://www.dropbox.com/s/bbgc21yc86h0t14/Efficient_Ocular_Data_Labelling.pdf?dl=0
Using synthetic data for computer vision model trainingUnity Technologies
During this webinar Unity’s computer vision team provides an overview of computer vision, walks through current real-world data workflows, and explains why companies are moving toward synthetically generated data as an alternate data source for model training.
Watch the webinar: https://resources.unity.com/ai-ml/cv-webinar-dec-2021
Shallow introduction for Deep Learning Retinal Image AnalysisPetteriTeikariPhD
Overview of retinal imaging techniques such as fundus photography, optical coherence tomography (OCT) along with future upgrades such as multispectral imaging, OCT angiography, adaptive optics imaging and polarization-sensitive OCT. This is followed by an overview of deep learning image analysis methods suitable to be used with retinal imaging techniques.
Alternative download link: https://www.dropbox.com/s/n01w02cjaf68vbo/retina_deepLearning_pipeline.pdf?dl=0
Deep learning @ Edge using Intel's Neural Compute Stickgeetachauhan
Talk @ Intel Global IoT DevFest, Nov 2017
The new generation of hardware accelerators are enabling rich AI driven, Intelligent IoT solutions @ the edge.
The talk showcased how to use Intel's latest Nervana Compute Stick for accelerating deep learning IoT solutions. It also covered use cases and code details for running Deep Learning models on Intel's Nervana Compute Stick.
Augmented reality applications in manufacturing and maintenance Jeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how augmented reality is becoming economically feasible for manufacturing and maintenance applications. Augmented reality adds useful information to a real-world image that is seen through head-mounted glasses or a tablet computer’s camera. Academic tests reveal that manufacturing and maintenance activities can be done more effectively when workers use augmented reality and many firms have begun using augmented reality.
Furthermore, continued improvements in display resolution and graphic processing speeds and the emergence of transparent displays will expand the use of AR. In particular, it takes several seconds for current devices to update the images that are overlaid on the real-world image, which confuses workers and slows them down. Improvements in graphic processors for tablet computers are reducing the time it takes for tablet computers to recognize and register objects and thus make the overlaid images look clear in the tablet’s display. While graphic processors in game consoles and desktop computers can easily handle this problem, graphic processors in mobile devices lag their game console and desktop computer counterparts by several years.
Best Practices aren't static -- as Unity's underlying architecture evolves to support Data-Oriented Design, the old tricks might no longer be the best ways to squeeze performance out of the engine. In this talk, we'll discuss how Unity has changed between Unity 5, Unity 2017 and Unity 2018 and how to take advantage of these changes.
Ian Dundore (Unity Technologie)
- How to tackle an object detection competition
- Schwert's 6th-place solution on Open Images Challenge 2019
- presented at the lunch workshop of the 26th Symposium on Sensing via Image Information (2020).
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
Top 10 Trending Technologies To Master In 2021LokeshLusifer
Change is the only constant. This applies to your professional life as well. Upskilling Yourself is needed nowadays, the reason is pretty simple, technology is evolving very quickly? I have listed the top trending technologies which are expected to acquire a huge market in 2021.
2. You can also the link for getting special offers and related article
Generative AI models are transforming various fields by creating realistic images, text, music, and videos. This guide will take you through the essential steps and considerations for building a generative AI model, providing a comprehensive understanding of the process.
Building a generative AI solution involves defining the problem, collecting and processing data, selecting suitable models, training and fine-tuning them, and deploying the system effectively. It’s essential to gather high-quality data, choose appropriate algorithms, ensure security, and stay updated with advancements.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-khronos
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Mark Bünger, Vice President of Research at Lux Research, delivers the presentation "Imaging + AI: Opportunities Inside the Car and Beyond" at the December 2016 Embedded Vision Alliance Member Meeting. Bünger presents his firm’s perspective on how embedded vision will upend the automotive industry.
Dear Reader,
We are a leading system integrator and IT solutions provider in Mumbai for automating your enterprise needs with reliable solutions since 30 years.
We are happy to publish 68th issue of our monthly newsletter "TechTalk". The earlier issues, can be found in the Newsletter section at www.goapl.com
We have helped 100+ companies in last 30 years for various IT Solutions. We’ll be happy to know if you have any requirement for IT or IT Related services, you could share the same on https://goapl.com/connect-with-us/
Generative AI: A Comprehensive Tech Stack BreakdownBenjaminlapid1
Build a reliable and effective generative AI system with the right generative AI tech stack that helps create smarter solutions and drive growth.
Click here for more information: https://www.leewayhertz.com/generative-ai-tech-stack/
With the vigorous development of emerging information technology, artificial intelligence application scenarios are everywhere. When it comes to AI, the first thing we think of is machine learning and deep learning. However, they are only part of the field of artificial intelligence research. The scope of artificial intelligence is extremely wide. This presentation describes the hot topics in artificial intelligence research and ten major technical categories.
DETECTING EMOTION FROM FACIAL EXPRESSION HAS BECOME AN URGENT NEED BECAUSE OF
ITS IMMENSE APPLICATIONS IN ARTIFICIAL INTELLIGENCE SUCH AS HUMAN-COMPUTER
COLLABORATION, DATA DRIVEN ANIMATION, HUMAN-ROBOT COMMUNICATION ETC. SINCE IT
IS A DEMANDING AND INTERESTING PROBLEM IN COMPUTER VISION, SEVERAL WORKS HAD
BEEN CONDUCTED REGARDING THIS TOPIC. THE OBJECTIVE OF THIS PROJECT IS TO DEVELOP A
FACIAL EXPRESSION RECOGNITION SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
WITH DATA AUGMENTATION. THIS APPROACH ENABLES TO CLASSIFY SEVEN BASIC EMOTIONS
CONSIST OF ANGRY, DISGUST, FEAR, HAPPY, NEUTRAL, SAD AND SURPRISE FROM IMAGE DATA.
CONVOLUTIONAL NEURAL NETWORK WITH DATA AUGMENTATION LEADS TO HIGHER
VALIDATION ACCURACY THAN THE OTHER EXISTING MODELS (WHICH IS 96.24%) AS WELL AS
HELPS TO OVERCOME THEIR LIMITATIONS.
Deep learning @ Edge using Intel's Neural Compute Stickgeetachauhan
Talk @ Intel Global IoT DevFest, Nov 2017
The new generation of hardware accelerators are enabling rich AI driven, Intelligent IoT solutions @ the edge.
The talk showcased how to use Intel's latest Nervana Compute Stick for accelerating deep learning IoT solutions. It also covered use cases and code details for running Deep Learning models on Intel's Nervana Compute Stick.
Augmented reality applications in manufacturing and maintenance Jeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how augmented reality is becoming economically feasible for manufacturing and maintenance applications. Augmented reality adds useful information to a real-world image that is seen through head-mounted glasses or a tablet computer’s camera. Academic tests reveal that manufacturing and maintenance activities can be done more effectively when workers use augmented reality and many firms have begun using augmented reality.
Furthermore, continued improvements in display resolution and graphic processing speeds and the emergence of transparent displays will expand the use of AR. In particular, it takes several seconds for current devices to update the images that are overlaid on the real-world image, which confuses workers and slows them down. Improvements in graphic processors for tablet computers are reducing the time it takes for tablet computers to recognize and register objects and thus make the overlaid images look clear in the tablet’s display. While graphic processors in game consoles and desktop computers can easily handle this problem, graphic processors in mobile devices lag their game console and desktop computer counterparts by several years.
Best Practices aren't static -- as Unity's underlying architecture evolves to support Data-Oriented Design, the old tricks might no longer be the best ways to squeeze performance out of the engine. In this talk, we'll discuss how Unity has changed between Unity 5, Unity 2017 and Unity 2018 and how to take advantage of these changes.
Ian Dundore (Unity Technologie)
- How to tackle an object detection competition
- Schwert's 6th-place solution on Open Images Challenge 2019
- presented at the lunch workshop of the 26th Symposium on Sensing via Image Information (2020).
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
Top 10 Trending Technologies To Master In 2021LokeshLusifer
Change is the only constant. This applies to your professional life as well. Upskilling Yourself is needed nowadays, the reason is pretty simple, technology is evolving very quickly? I have listed the top trending technologies which are expected to acquire a huge market in 2021.
2. You can also the link for getting special offers and related article
Generative AI models are transforming various fields by creating realistic images, text, music, and videos. This guide will take you through the essential steps and considerations for building a generative AI model, providing a comprehensive understanding of the process.
Building a generative AI solution involves defining the problem, collecting and processing data, selecting suitable models, training and fine-tuning them, and deploying the system effectively. It’s essential to gather high-quality data, choose appropriate algorithms, ensure security, and stay updated with advancements.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-khronos
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Mark Bünger, Vice President of Research at Lux Research, delivers the presentation "Imaging + AI: Opportunities Inside the Car and Beyond" at the December 2016 Embedded Vision Alliance Member Meeting. Bünger presents his firm’s perspective on how embedded vision will upend the automotive industry.
Dear Reader,
We are a leading system integrator and IT solutions provider in Mumbai for automating your enterprise needs with reliable solutions since 30 years.
We are happy to publish 68th issue of our monthly newsletter "TechTalk". The earlier issues, can be found in the Newsletter section at www.goapl.com
We have helped 100+ companies in last 30 years for various IT Solutions. We’ll be happy to know if you have any requirement for IT or IT Related services, you could share the same on https://goapl.com/connect-with-us/
Generative AI: A Comprehensive Tech Stack BreakdownBenjaminlapid1
Build a reliable and effective generative AI system with the right generative AI tech stack that helps create smarter solutions and drive growth.
Click here for more information: https://www.leewayhertz.com/generative-ai-tech-stack/
With the vigorous development of emerging information technology, artificial intelligence application scenarios are everywhere. When it comes to AI, the first thing we think of is machine learning and deep learning. However, they are only part of the field of artificial intelligence research. The scope of artificial intelligence is extremely wide. This presentation describes the hot topics in artificial intelligence research and ten major technical categories.
DETECTING EMOTION FROM FACIAL EXPRESSION HAS BECOME AN URGENT NEED BECAUSE OF
ITS IMMENSE APPLICATIONS IN ARTIFICIAL INTELLIGENCE SUCH AS HUMAN-COMPUTER
COLLABORATION, DATA DRIVEN ANIMATION, HUMAN-ROBOT COMMUNICATION ETC. SINCE IT
IS A DEMANDING AND INTERESTING PROBLEM IN COMPUTER VISION, SEVERAL WORKS HAD
BEEN CONDUCTED REGARDING THIS TOPIC. THE OBJECTIVE OF THIS PROJECT IS TO DEVELOP A
FACIAL EXPRESSION RECOGNITION SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
WITH DATA AUGMENTATION. THIS APPROACH ENABLES TO CLASSIFY SEVEN BASIC EMOTIONS
CONSIST OF ANGRY, DISGUST, FEAR, HAPPY, NEUTRAL, SAD AND SURPRISE FROM IMAGE DATA.
CONVOLUTIONAL NEURAL NETWORK WITH DATA AUGMENTATION LEADS TO HIGHER
VALIDATION ACCURACY THAN THE OTHER EXISTING MODELS (WHICH IS 96.24%) AS WELL AS
HELPS TO OVERCOME THEIR LIMITATIONS.
leewayhertz.com-How to build a generative AI solution From prototyping to pro...KristiLBurns
Generative AI has gained significant attention in the tech industry, with investors, policymakers, and the society at large talking about innovative AI models like ChatGPT and Stable Diffusion.Generative AI has gained significant attention in the tech industry, with investors, policymakers, and the society at large talking about innovative AI models like ChatGPT and Stable Diffusion.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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.
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Computer Vision - White Paper 2020
1. COMPUTER
VISION
APPLICATIONS
Table of contents:
02 What is computer
vision?
04 What tools are used
to create computer
vision solutions?
07 Real-life examples
of computer vision
applications
09 Addepto case study
10 About Addepto
ADDEPTO WHITE PAPER 2020
2. Addepto White Paper 2020
Computer vision 02
WHAT IS COMPUTER
VISION?
WHAT IS COMPUTER
VISION?
Computer vision (CV) is an artificial intelligence-based technology that
allows computers to observe the world. By analyzing visual data, this
innovation can almost perfectly understand a particular situation, and
without missing any factors, find the best solutions or the most reasonable
decisions.
The algorithms which stand for computer vision reached an amazing level of
accuracy in understanding what is happening around. Today’s systems are
right in 99% of cases – which makes them more accurate than humans.
Computer Vision applications will become crucial in future automation,
visual intensive works like RTG luggage inspection, finding criminals with
public cameras, or preventing financial fraud using face recognition. This
domain will open new areas of development and help to create new
industries.
OBJECT DETECTIONOBJECT DETECTION
Object Detection is a part of Computer Vision which focuses on detecting
various objects on photos like cats, dogs, cars, bikes, humans, etc., by
extracting features from pixels and applying deep learning to recognize
patterns. One of the main areas of Object Detection is face recognition.
Algorithms of Computer Vision are able to reconstruct 3D objects from 2D
imagery taken from different angles. As an example, we can acquire a city
model from images gathered by drones. We may even create a model of the
cave based on a movie recorded inside it.
3D SCENE RECONSTRUCTION3D SCENE RECONSTRUCTION
3. A model trained to detect objects on
photos can extract its content and
prepare tags automatically.
Nowadays, the inference is so fast
that videos can be processed in real-
time. This technology can be used in
personalized advertisements (for
example screens in public space)
where ads are chosen basing on
your clothes and things you carry.
Traditionally, to detect an object on
an image it used to be sufficient to
just select its position by the
rectangle. Now, an improvement of
this technique is outlining the given
object (for example by a slight
change of its color) and in that way
segment image on different objects
where the result is obtaining an
image very similar to the stained
glass. This technology will be
extensively used in autonomous
navigation and radiology (outlining
cancerous changes in tissue).
Addepto White Paper 2020
Computer vision
IMAGE AND VIDEO PRE-
PROCESSING
IMAGE AND VIDEO PRE-
PROCESSING
Advanced CV with the use of neural
networks can perform image
transformations not available for
traditional image processing
algorithms. As an example, we can
artificially increase the number of
trees or remove them without
noticing an artificial change.
It is possible to generate missing
parts of the photo or change the
sky’s appearance from Earth to
Mars. Possibilities of image
enhancing and transformation are
limitless and require just creating a
specialized model for a given task.
VIDEO AND
IMAGE
CONTENT
INDEXING
VIDEO AND
IMAGE
CONTENT
INDEXING
SCENE
SEGMENTATION
SCENE
SEGMENTATION
03
4. WHAT TOOLS ARE USED TO
CREATE COMPUTER VISION
SOLUTIONS?
WHAT TOOLS ARE USED TO
CREATE COMPUTER VISION
SOLUTIONS?
C++ is a programming language which supports procedural, object-oriented, and
generic programming. It is statically typed, compiled, general-purpose, case-
sensitive, free-form framework. It comprises a combination of both high-level
and low-level language features.
Python is one of the most popular programming languages in the world. Is being
used by companies like Wikipedia, Google, Yahoo!, CERN, and NASA.
It’s often used as a “scripting language” for web applications - it can automate a
specific series of tasks, making it more efficient. Python is often used in software
applications, web pages, and games. It is also used in scientific and mathematical
computing, and in AI projects.
OpenCV library is an open-source computer vision and machine learning
software library. It was built primarily to provide an infrastructure for computer
vision applications.
OpenCV library has over 2,500 optimized algorithms, which include either the
computer vision and machine learning algorithms. These algorithms can be used
by companies detect and recognize faces (face recognition), identify objects,
classify human actions in videos, track camera movements, track moving
objects, extract 3D models of objects, find similar images from an image
database, follow eye movements, recognize scenery, and establish markers to
overlay it with augmented reality.
Addepto White Paper 2020
Computer vision
04
C++C++
PYTHONPYTHON
OPEN CVOPEN CV
5. Torch offers a wide support for machine learning algorithms that puts GPUs
(graphics processing units) first. It is very efficient if it comes to fast scripting
language, LuaJIT, and an underlying C/CUDA implementation.
PyCharm is one of many IDEs (integrated development environment) available
for Python. It is user-friendly, powerful, and provides integration with git.
PyCharm has its own terminal, python console, and provides support for various
useful plugins.
Keras is a high-level library that uses TensorFlow, CNTK, or Theano as a back-
end. It is officially supported by Google (TensorFlow) which has intercepted its
development. Keras positions itself as a CV API for “human beings”. It focuses
on simplicity so creating networks is fast and intuitive.
Model architecture is divided on fully-configurable modules like neural layers,
optimizers (Adam, RMSProp), cost functions, etc. It includes built-in models like
ResNet50, InceptionV3, or MobileNet. Keras can be used on multi-GPU systems
but it requires more time to configure with using both Keras and Tensorflow
API.
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TORCHTORCH
PYCHARMPYCHARM
KERASKERAS
THEANOTHEANO
Theano is one of the oldest Python libraries built for operating on multi-
dimensional arrays and that allow training neural networks. It is integrated with
NumPy, it has efficient symbolic differentiation, possibilities to evaluate
expressions faster thanks to dynamic C code generation, and can automatically
diagnose many types of errors. Its development has finished in late 2017 but it is
still a decent library to use for your project.
COMPUTER VISION TOOLSCOMPUTER VISION TOOLS
6. TensorFlow was designed by Google Brain Team and released as an open-source
library for abstract (using tensors) numerical computation. It is a low-level
library, old enough to have many sophisticated projects using it as a backbone,
decent documentation, and vast community. TensorFlow’s main advantage (over
Theano) is multi-GPU support. It has two API: low-level (original), and high-level
Keras.
MXNet allows using many GPUs in distributed systems. It is also easy to
manage where every piece of data should be stored in the systems. This library
has also built-in methods for fast derivative calculations. Every coded layer has
been optimized and now MXNet is one of the fastest available CV libraries.
However, it takes it more time to start modeling comparing to Keras.
LASAGNELASAGNE
Lasagne is built on top of Theano with the intention to be simple to understand,
use, and easy to directly process and return Theano expression or NumPy data
types. Lasagne allows defining Convolutional Neural Networks, Recurrent Neural
Networks, and its combinations. It supports CPU and GPU thanks to Theano’s
compiler. In terms of library level, it is medium – somewhere between low-level
libraries like TensorFlow or Theano and high-level libraries like Keras.
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TENSORFLOWTENSORFLOW
COMPUTER VISION TOOLSCOMPUTER VISION TOOLS
MXNETMXNET
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REAL-LIFE EXAMPLES OF
COMPUTER VISION
APPLICATIONS
REAL-LIFE EXAMPLES OF
COMPUTER VISION
APPLICATIONS
Automatic product detection allows recognizing
missing and misplaced products on shelves with
comparison to the planogram. Aggregated
information about shop conditions gives the
opportunity to improve the quality of customer
service.
RETAIL SHELF ANALYSISRETAIL SHELF ANALYSIS
Computer Vision can also automate the process
of discovering illicit items in luggage during
customs inspection on the airports. Such a
mundane task is ideal for Convolutional Neural
Networks taking into consideration the huge
size of the available data-set.
RTG ANALYSISRTG ANALYSIS
This technology will improve the
advertisement industry, making it more
personalized. For example, after tagging
customer’s favorite brands and gaining
deep insights into their preferences, we can
recommend products with a higher
probability of being chosen. It is a win-win
situation for both customers (more relevant
ads) and e-commerce (higher income).
AUTOMATIC VIDEO
TAGGING FOR REAL-TIME
MARKETING
AUTOMATIC VIDEO
TAGGING FOR REAL-TIME
MARKETING
8. 04
Addepto White Paper 2020
Computer vision
COMPUTER VISION APPLICATIONSCOMPUTER VISION APPLICATIONS
Having real estate imagery data with its value,
we can create a model that will predict value
from new real estate photos. It allows fast
comparison of given and predicted prices in
order to find investment gems or to find
undervalued rent occasions.
REAL ESTATE VALUATIONREAL ESTATE VALUATION
Make identification easier for security officers
and ordinary people – no more need for
additional cards or keys. Also, there is a
possibility to determine when somebody is a
wanted criminal.
RECOGNIZING FACES IN
SECURITY SYSTEMS
RECOGNIZING FACES IN
SECURITY SYSTEMS
This technique protects from misspelling and it
is much faster than reading information
manually. It has the potential to simplify
maintaining a customer database and improve
the quality of data.
AUTOMATIC READING OF
PERSONAL INFORMATION
FROM IDENTITY CARDS
AUTOMATIC READING OF
PERSONAL INFORMATION
FROM IDENTITY CARDS
CV techniques use data from cameras to
visually check the condition of assets, for
example, valves and pipes, and compare it with
optimal conditions. This information can be
transferred to a remote maintenance crew, that
checks anomalies.
INDUSTRIAL MAINTENANCEINDUSTRIAL MAINTENANCE
08
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ADDEPTO CASE STUDYADDEPTO CASE STUDY
There are cases of lost passengers' luggage. Our goal was to find lost
luggage at other airports. The problem is that manual search is a very labor
intensive task because you have to analyze terabytes of videos.
CHALLENGE:CHALLENGE:
To solve the existing problem, we created the Deep Learning model to find
lost luggage. We used FgSegNet for background segmentation, and we used
Triplet and Siam networks to find luggage (accuracy in the top five is 94%).
The final solution was built in C ++ with an intuitive interface for users. The
solutions work in real time using Jetson graphics processors.
OUR SOLUTION:OUR SOLUTION:
The prepared solutions processed terabytes of films within a few hours and
are looking for lost luggage with great accuracy. It saves many hours of
work and optimizes airport costs.
BENEFITS:BENEFITS:
Discover other Addepto case studies.
BAGGAGE SIMILARITYBAGGAGE SIMILARITY
10. contact@addepto.com
Our team builds innovative applications and products by integrating
computer vision services with other systems like POS, ERP, and diagnostic
software. It is used to detect anomalies in shopping centers, track quality in
production lines, analyze medical images, identify products on shelves, and
analyze people and their demographics in social media.
AI development experts at Addepto have outstanding experience in building
customized computer vision applications with advanced components based
on neural networks such as object classification, feature recognition, image
segmentation, pattern recognition, object detection, background
segmentation, and emotion detection. Those solutions help to solve complex
business challenges in different industries.
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ABOUT ADDEPTOABOUT ADDEPTO
If you are looking for more details, or you would like to
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