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Top 5 Open Source AI Solutions for Image Processing

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Top 5 Open Source AI Solutions for Image Processing
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A few decades or even years ago, self-driving cars and computers with a human-like vision were just a figment of the fantasy writer’s imagination. But today, Artificial intelligence (AI) technologies allow cars to drive safely across busy streets and computers to interpret pictures almost like humans do.

Developers have made impressive progress on how to implement AI on image processing tasks. Furthermore, image processing solutions with AI are widely used in various fields, from medicine to law enforcement to cybersecurity and retail.

AI and machine learning make it possible for the machines to inherit new functionalities through the process of learning. Similarly to small kids, machines can gain the ability to acquire and understand visual information by processing massive amounts of images. To process so much data faster without compromising the final result, developers use image processing models based on machine learning and deep learning algorithms.

In particular, image processing and computer vision solutions use deep learning for accomplishing such complex tasks as:

  • Image classification
  • Object recognition
  • Object tracking
  • Image generation
  • Image retrieval

To implement some computer vision functionalities into your product, you’ll need a large set of tools and resources: image datasets, libraries with code samples, frameworks for creating and training deep learning models. Fortunately, there are a lot of open-course services that you can use to make machine learning less of a mystery and ease the development of your own AI-based image processing solution.

Below, we take a closer look at the five best open source image processing solutions that you can apply to your projects:

  • OpenCV
  • TensorFlow
  • Keras
  • Caffe
  • Google Colab

Let’s get started!

OpenCV

Open Source Computer Vision Library (OpenCV) is one of the most popular open source services for image processing. It’s a free computer vision library that you can use to perform various image processing tasks:

  • Image acquisition
  • Image compression and decompression
  • Image enhancement and restoration
  • Image denoising
  • Image segmentation
  • Data extraction, and more

The library includes numerous algorithms and functions as well as special modules aimed at image processing tasks. Starting from the library version 3.1, there’s also a deep learning module for building and training deep learning models.

With this module, you can use OpenCV to extract features from processed images, and then apply a machine learning model using one of the supported machine learning frameworks.

As of today, OpenCV supports a number of popular frameworks, including TensorFlow, PyTorch, and Caffe. The list of supported types of neural networks includes convolutional, deconvolutional, and recurrent neural networks.

The library is written in C++ and supports C++, Java, Python, and MATLAB interfaces.

TensorFlow

TensorFlow is an open-source machine learning framework created by Google. Initially, the project was started for research purposes of the Google Brain team. Today, however, TensorFlow is widely used by both small startups and large companies such as Dropbox, Intel, and Twitter.

You can use TensorFlow to process different types of data, but in relation to image processing, this framework works best for:

  • Image classification
  • Image recognition
  • Image segmentation
  • Image to image translation (pix2pix)

TensorFlow includes a set of libraries for creating and training custom deep learning models and neural networks. The framework supports Jupyter notebooks and provides a style guide with recommendations on writing readable, consistent code.

TensorFlow supports several popular programming languages, including C++, Python, Java, Rust, and Go. However, you can install third-party bindings for other languages, such as Ruby, Scala, or PHP.

Keras

Keras is an open-source Python library for creating deep learning models. It’s a great solution for those who only begin to use machine learning algorithms in their projects as it simplifies the creation of a deep learning model from scratch. Keras is easy to manage and it is suitable for fast and simple prototyping of different types of neural networks.

The library was built on TensorFlow and is currently fully integrated into the framework. This means that you can write your deep learning model in Keras, as it has a much more user-friendly interface, and then easily implement a specific functionality or feature from TensorFlow in this model.

Keras can also be deployed on top of other popular AI frameworks such as Microsoft Cognitive Toolkit and Theano.

Caffe

Convolutional Architecture for Fast Feature Embedding (Caffe) is an open-source framework that can also be used for creating and training popular types of deep learning architectures. You can use Caffe to accomplish such tasks as image classification, segmentation, and recognition.

Caffe is written in C++ but it also has a Python interface. The framework supports both CPU- and GPU-based accelerated libraries such as NVIDIA cuDNN and Intel MKL. The framework also has a special database, Caffe Model Zoo, containing a set of pre-trained deep learning models. As of today, it offers four BAIR-trained models:

  • BAIR Reference CaffeNet
  • BAIR Reference R-CNN ILSVRC-2013
  • BAIR AlexNet
  • BAIR GoogLeNet

Model Zoo also includes a number of community models trained by other Caffe users. As for the types of supported neural networks, Caffe works best with convolutional neural networks (CNN) and feedforward networks. At the same time, it’s not the best choice for training recurrent neural networks.

In 2017, Facebook launched Caffe2, an open-source framework for training and deploying deep learning models. And in 2018, Caffe2 was integrated with another popular AI framework, PyTorch.

Caffe2 comes with C++ and Python APIs and supports all popular platforms.

Google Colaboratory (Colab)

Google Colaboratory, or simply Colab, is one of the top image processing services. While it’s rather a cloud service than a framework, you can still use Colab for building custom deep learning applications from scratch. With the help of Colab, you can perform such image processing tasks as image classification, segmentation, and object detection.

Google Colab eases the use of other popular AI-based tools such as OpenCV, TensorFlow, and Keras. The service uses Jupyter Notebooks, helping developers to share their knowledge, tips, and best practices on building AI-based applications. Plus, in contrast to other similar services, Colab offers free usage of both CPU- and GPU-based acceleration.

Conclusion

Machine learning models and algorithms help developers implement specific image processing functionalities into their products both quickly and easily. However, building a custom machine learning model or neural network requires lots of resources and a high level of technology expertise. With the help of the listed open-source tools, libraries, and frameworks, you can simplify the process of leveraging Artificial Intelligence technologies to your benefit.

 

This article is a contribution from Marcell Gogan.  Marcell is a specialist within digital security solutions, business design and development, virtualization and cloud computing, R&D projects, establishment and management of software research direction – working with Ekran System. He also loves writing about data management and cybersecurity. 

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The Nomination Committee’s Proposal of Catena Media’s Board of Directors at the Annual General Meeting 2024

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The Nomination Committee’s Proposal of Catena Media’s Board of Directors at the Annual General Meeting 2024
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The Nomination Committee of Catena Media proposed re-election of the following members of the Board of Directors:

Øystein Engebretsen

Theodore Bergquist

Adam Krejcik

Sean Hurley

The Nomination Committee proposed that Erik Flinck and Dan Castillo be elected as new members of the Board of Directors.

Göran Blomberg, Esther Teixeira-Boucher and Austin Malcomb have declined re-election as board members.

The Nomination Committee proposed that there will be six (6) members of the Board of Directors, changed from seven (7).

The Nomination Committee also proposed Erik Flinck to be elected as Chairman of the Board of Directors.

Erik Flinck, born in 1980, currently provides high end business consulting combined with serving as Chairman for the digital health startup, dr HUD. Mr Flinck previously served as Head of BCG Sweden and has extensive experience from corporate management, growth and turnarounds from nearly 20 years of Management Consulting and serving as Head of Group Strategy and M&A at Sandvik AB. He has a Masters Degree in Engineering (Software development and Financial Mathematics) from the Royal Institute of Technology in Stockholm and a Masters Degree in Business and Administration from Stockholm University and Stockholm School of Business.

Born in 1980, Dan Castillo has accumulated over 20 years of experience across startups, growth companies and turnarounds. Since 2015, Castillo has invested in Catena Media, maintaining a close watch on its progression, especially after its IPO in 2016. He has previous experience of listed board work in Kotipizza which Orkla acquired in 2018. He currently serves on the boards of five companies in different sectors, including Quartr.com in Fintech and Hope Studios in movie production. His academic background includes studies in Finance and Economics at Linköping University.

The Nomination Committee of Catena Media consists of:

Nicklas Paulson, representing Investment AB Öresund (chair of the nomination committee)

Marianne Stenberg, representing Second Swedish National Pension Fund

Martin Zetterlund, representing Niklas Karlsson

Göran Blomberg, chairman of the board of Catena Media.

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IGT Announces Executive and Board Leadership Changes

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IGT Announces Executive and Board Leadership Changes
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International Game Technology PLC (IGT) announced that on March 21, 2024, its Board of Directors implemented changes to the Company’s Board and executive team.

Marco Drago announced that he will step down from his role as a non-executive director of the IGT Board of Directors. He will carry out his remaining term and depart from the Board at the conclusion of the Company’s Annual General Meeting (AGM) on May 14, 2024.

Enrico Drago has been appointed by the IGT Board of Directors as a non-executive director of the IGT Board. In addition, Enrico Drago will resign from his current role of CEO of IGT PlayDigital, and Gil Rotem, who is currently IGT PlayDigital President of iGaming, will expand his role to become IGT PlayDigital President and report directly to Vince Sadusky, CEO of IGT. These changes will be effective April 1, 2024. Enrico Drago will continue in his role as vice chairman of De Agostini S.p.A.

“As Marco Drago prepares to step down from his position on the IGT Board of Directors, I’d like to thank him for his many years of service and his unwavering commitment to driving results and creating value for all IGT stakeholders. Enrico Drago joining the Board and leaving his executive leadership position at IGT is a natural evolution that supports the Company’s vision for its next era of growth and transformation. Enrico’s value-creation mindset and understanding of global growth opportunities will enhance the Board and align with IGT’s strategic priorities,” said Marco Sala, IGT Executive Chair of the Board.

“Watching and guiding IGT through its evolution from a collection of companies that started with Lottomatica and GTECH grow into a unified global gaming leader has been very gratifying. We have been fortunate to have a great group of board members and business unit leaders that have helped drive IGT’s growth during this time. I thank them for their contributions and am certain that IGT is positioned for continued growth as we go forward with the bold initiatives we have undertaken,” said Marco Drago, IGT Non-Executive Director.

“Over the last five-plus years, IGT PlayDigital has established leadership positions in the global iGaming and North American sports betting sectors that will be foundational to the Company’s future successes. I thank the entire IGT PlayDigital team for all that we have accomplished in this time, and I look forward to supporting IGT in a new capacity and further helping the Company define its vision and strategy,” said Enrico Drago, CEO of IGT PlayDigital.

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Bidstack Executive Team Acquires Bidstack Limited and all its Subsidiaries

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Bidstack Executive Team Acquires Bidstack Limited and all its Subsidiaries
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The executive team of Bidstack has acquired Bidstack Limited and all the operating entities of the Group, from the administrators of Bidstack Group PLC. The deal sees James Draper, Founder & CEO, and the executive team of Bidstack Ltd become significant majority shareholders.

All contracts and client relationships will continue within the new ownership structure – safeguarding jobs for the UK and European-based staff.

Bidstack, the multi award-winning in-game middleware technology provider that initially set about bringing programmatic advertising revenue to the most renowned sports gaming franchises in the world, has diversified into a broader offering, taking advantage of the most advanced off-engine content management system in gaming.

Following the announcement of a partnership with the Washington Commanders, where the NFL franchise became the first-ever sports team to utilise a platform to control advertising within their virtual stadium, across official NFL games, from multiple studios and developers, the management team has been focusing on sports rights holders as a key customer type.

The executive team has the support of the world’s leading rights-holder professionals in the sports industry. It will continue to execute from its position as the leading technology for the sports industry, for fan engagement and brand activations, in video games.

The executive management consists of James Draper continuing as Chief Executive Officer, with Lisa Hau stepping up to Chief Financial Officer, Dave Garvey continuing as Chief Legal Officer, Will Stewart moving to Chief Product Officer and Daniel Barrigas to Chief Technology Officer.

James Draper, Bidstack’s Founder & CEO, said: “The acquisition is a pivotal moment for the next phase of growth for the business. Our technology is at the forefront of sports technology, and I couldn’t be more excited. I am proud that we are able to reward our ambitious and industry-pioneering team and have them as shareholders alongside myself.”

“I want to thank the staff and customers for standing by us during this strategic review, which has obviously been an uncertain period. For all of our customers to have stood strong alongside us is testament to the relationships we’ve built over the years, as well as the incredible staff we have here who have fostered those connections.

“The company can now focus on the enormous potential we have, to enable sports teams to get closer to their fans and improve the player experience by bringing their virtual IP to life, with real-time messaging, rewards and engagements.

“Thank you to the management team who have invested to protect the incredible work our talented group has produced. It’s extremely motivating to see the unwavering belief we collectively have in our vision and product. Sadly, the public market is an uncertain place currently, and it’s a challenging environment for growth businesses such as ours.

“The interest and support we’ve had from some of the leading players in the sports industry has given our team great confidence and motivation as we work with some of the world’s largest sporting franchises and leagues.

“Thank you to everyone’s support and to our Board of Directors, who have assisted myself and management throughout.”

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