Online Computer Vision Training in the UAE

This instructor-led, live training in the UAE (online or onsite) is designed for intermediate to advanced computer vision engineers, AI developers, and IoT professionals seeking to implement and optimize computer vision models for real-time processing on edge devices.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of Edge AI and its applications in computer vision.
• Deploy optimized deep learning models on edge devices for real-time image and video analysis.
• Utilize frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimize AI models to enhance performance, power efficiency, and reduce inference latency.

This instructor-led live training in the UAE (online or onsite) is designed for advanced professionals aiming to deepen their grasp of computer vision and explore TensorFlow’s capabilities for building advanced vision models using Google Colab.

By the conclusion of this training, participants will be able to:

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

The CANN SDK (Compute Architecture for Neural Networks) equips developers with robust deployment and optimization tools for real-time AI applications in computer vision and NLP, specifically tailored for Huawei Ascend hardware.

This instructor-led live training, available online or onsite, targets intermediate-level AI practitioners aiming to build, deploy, and optimize vision and language models via the CANN SDK for production environments.

Upon completion, participants will be capable of:

• Deploying and optimizing CV and NLP models utilizing CANN and AscendCL.
• Employing CANN utilities to convert models and integrate them into active pipelines.
• Enhancing inference performance for tasks such as detection, classification, and sentiment analysis.
• Constructing real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures accompanied by demonstrations.
• Practical labs focused on model deployment and performance profiling.
• Live pipeline design exercises using real-world CV and NLP use cases.

Customization Options

• For customized training arrangements for this course, please reach out to us.

This instructor-led live training in the UAE (online or onsite) targets intermediate-level AI developers and computer vision engineers seeking to build robust vision systems for autonomous driving applications.

Upon completion of this training, participants will be able to:

• Understand the fundamental concepts of computer vision in autonomous vehicles.
• Implement algorithms for object detection, lane detection, and semantic segmentation.
• Integrate vision systems with other autonomous vehicle subsystems.
• Apply deep learning techniques for advanced perception tasks.
• Evaluate the performance of computer vision models in real-world scenarios.

This instructor-led, live training in the UAE (online or onsite) is aimed at beginner-level law enforcement personnel who wish to transition from manual facial sketching to using AI tools for developing facial recognition systems.

By the end of this training, participants will be able to:

• Understand the fundamentals of Artificial Intelligence and Machine Learning.
• Learn the basics of digital image processing and its application in facial recognition.
• Develop skills in using AI tools and frameworks to create facial recognition models.
• Gain hands-on experience in creating, training, and testing facial recognition systems.
• Understand ethical considerations and best practices in the use of facial recognition technology.

This instructor-led, live training in the UAE (online or onsite) is aimed at beginner-level to intermediate-level researchers and laboratory professionals who wish to process and analyze images related to histological tissues, blood cells, algae, and other biological samples.

By the end of this training, participants will be able to:

• Navigate the Fiji interface and utilize ImageJ’s core functions.
• Preprocess and enhance scientific images for better analysis.
• Analyze images quantitatively, including cell counting and area measurement.
• Automate repetitive tasks using macros and plugins.
• Customize workflows for specific image analysis needs in biological research.

This instructor-led, live training in the UAE (online or onsite) is tailored for intermediate-level professionals who wish to use Vision Builder AI to design, implement, and optimize automated inspection systems for SMT (Surface-Mount Technology) processes.

By the end of this training, participants will be able to:

• Set up and configure automated inspections using Vision Builder AI.
• Acquire and preprocess high-quality images for analysis.
• Implement logic-based decisions for defect detection and process validation.
• Generate inspection reports and optimize system performance.

This instructor-led live training in the UAE (online or onsite) is designed for intermediate to advanced developers, researchers, and data scientists who wish to learn how to implement real-time object detection using YOLOv7.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of object detection.
• Install and configure YOLOv7 for object detection tasks.
• Train and test custom object detection models using YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Troubleshoot common issues related to YOLOv7 implementation.

Fiji is a powerful open-source image processing suite that combines ImageJ (a program designed for scientific multidimensional images) with a comprehensive suite of plugins for scientific image analysis.

In this instructor-led, live training, participants will learn how to leverage the Fiji distribution and its underlying ImageJ program to create robust image analysis applications.

By the conclusion of this training, participants will be able to:

• Utilize Fiji's advanced programming features and software components to extend ImageJ capabilities
• Assemble large 3D images from overlapping tiles
• Automate the update of a Fiji installation upon startup using the integrated update system
• Choose from a wide range of scripting languages to develop custom image analysis solutions
• Utilize Fiji's robust libraries, such as ImgLib, to process large bioimage datasets efficiently
• Deploy applications and collaborate effectively with other scientists on similar projects

Format of the Course

• Interactive lecture and discussion
• Extensive exercises and practical application
• Hands-on implementation in a live-lab environment

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source library licensed under BSD, offering several hundred computer vision algorithms.

Audience

This course is aimed at engineers and architects who wish to leverage OpenCV for computer vision projects.

This instructor-led, live training in the UAE (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

By the end of this training, participants will be able to:

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate impactful reports from images and videos.

Pattern Matching is a methodology employed to identify specific patterns within an image. This technique allows for the verification of particular characteristics in captured images, such as confirming the presence of an expected label on a defective item on a production line or verifying the precise dimensions of a component. It differs from "Pattern Recognition," which identifies broad patterns derived from large sets of related samples, by strictly defining what is being sought and reporting whether that specific pattern exists.

Course Format

• This course explores the approaches, technologies, and algorithms utilized in pattern matching within the context of Machine Vision.

Computer Vision is a discipline focused on the automatic extraction, analysis, and interpretation of valuable insights from digital media. Python, a high-level programming language renowned for its clear syntax and readability, serves as the ideal tool for this purpose.

Through this instructor-led live training, participants will master the fundamentals of Computer Vision by building a series of practical applications using Python.

By the conclusion of this training, participants will be capable of:

• Grasping the core concepts of Computer Vision
• Implementing Computer Vision tasks using Python
• Developing custom systems for face, object, and motion detection

Audience

• Python developers interested in Computer Vision

Course Format

• A blend of lectures, discussions, exercises, and intensive hands-on practice

SimpleCV is an open-source framework, which means it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It helps you build software to make your various technologies not only see the world, but understand it too.

Audience

This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.

This instructor-led, live training in the UAE (online or onsite) is designed for backend developers and data scientists who wish to integrate pre-trained YOLO models into enterprise applications and implement cost-efficient object detection solutions.

Upon completion of this training, participants will be able to:

• Install and configure the necessary tools and libraries required for object detection using YOLO.
• Customize Python command-line applications that leverage YOLO pre-trained models.
• Apply pre-trained YOLO model frameworks to various computer vision projects.
• Convert existing object detection datasets into the YOLO format.
• Understand the core concepts of the YOLO algorithm within the context of computer vision and deep learning.