Online Computer Vision Training in Portugal

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

Upon completing this training, participants will be capable of:

• Grasping the fundamentals of Edge AI and its role in computer vision.
• Deploying optimized deep learning models on edge devices for real-time image and video analysis.
• Utilizing frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimizing AI models to achieve superior performance, power efficiency, and low-latency inference.

This instructor-led, live training in Portugal (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end 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) offers robust deployment and optimization utilities for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

This instructor-led live training (available online or onsite) is designed for intermediate-level AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production-grade solutions.

Upon completing this training, participants will be capable of:

• Deploying and optimizing CV and NLP models utilizing CANN and AscendCL.
• Leveraging 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 tailored for edge or cloud-based deployment environments.

Course Format

• Interactive lectures and demonstrations.
• Practical labs focused on model deployment and performance profiling.
• Designing live pipelines using real-world CV and NLP scenarios.

Customization Options

• For those requiring customized training for this course, please contact us to arrange.

This instructor-led, live training in Portugal (online or onsite) is aimed at intermediate-level AI developers and computer vision engineers who wish to build robust vision systems for autonomous driving applications.

By the end 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 Portugal (online or onsite) is designed for beginner-level law enforcement personnel aiming to transition from manual facial sketching to leveraging AI tools for developing facial recognition systems.

Upon completing this training, participants will be equipped to:

• Grasp the core principles of Artificial Intelligence and Machine Learning.
• Acquire foundational knowledge of digital image processing and its role in facial recognition.
• Build proficiency in employing AI tools and frameworks to construct facial recognition models.
• Obtain practical experience in developing, training, and testing facial recognition systems.
• Comprehend the ethical implications and industry best practices surrounding the deployment of facial recognition technology.

This instructor-led, live training in Portugal (online or onsite) is designed for beginner to intermediate researchers and laboratory professionals who wish to process and analyze images of 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 Portugal (online or onsite) is aimed at 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 Portugal (online or onsite) targets intermediate to advanced developers, researchers, and data scientists seeking to learn how to implement real-time object detection using YOLOv7.

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

• Grasp the core concepts of object detection.
• Install and configure YOLOv7 for object detection workflows.
• Train and evaluate custom object detection models using YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Resolve common issues associated with YOLOv7 implementation.

Fiji is a powerful open-source image processing package that combines ImageJ (a program designed for scientific multidimensional images) along 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 end of this training, participants will be able to:

• Use Fiji's advanced programming features and software components to extend ImageJ capabilities
• Stitch large 3D images from overlapping tiles
• Automate the update of a Fiji installation on startup using the integrated update system
• Select from a broad selection of scripting languages to build custom image analysis solutions
• Utilise Fiji's powerful 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 Customisation Options

• To request a customised 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, featuring several hundred computer vision algorithms.

Audience

This course is designed for engineers and architects who wish to apply OpenCV to computer vision projects.

This instructor-led, live training in Portugal (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 technique employed to identify specific patterns within an image. It allows you to verify the presence of defined characteristics in a captured image, such as confirming the correct label on a faulty product on a factory line or verifying the precise dimensions of a component. Unlike "Pattern Recognition," which identifies general patterns based on broader collections of related samples, pattern matching specifically defines what you are looking for and confirms whether that expected pattern exists.

Course Format

• This course explores the approaches, technologies, and algorithms used in pattern matching as applied to Machine Vision.

Computer Vision is a discipline dedicated to the automatic extraction, analysis, and interpretation of valuable information from digital media. Python stands out as a high-level programming language renowned for its clean syntax and high readability.

In this instructor-led live training, participants will grasp the fundamentals of Computer Vision by guiding them through the development of a series of straightforward Computer Vision applications using Python.

Upon completing this training, participants will be capable of:

• Comprehending the core concepts of Computer Vision
• Utilizing Python to execute Computer Vision tasks
• Developing their own systems for face, object, and motion detection

Audience

• Python programmers keen on exploring Computer Vision

Format of the course

• A blend of lectures, discussions, exercises, and extensive 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 Portugal (online or onsite) is aimed at back-end developers and data scientists who wish to incorporate pre-trained YOLO models into their enterprise-driven programs and implement cost-effective components for object-detection.

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

• Install and configure the necessary tools and libraries required in object detection using YOLO.
• Customize Python command-line applications that operate based on YOLO pre-trained models.
• Implement the framework of pre-trained YOLO models for various computer vision projects.
• Convert existing datasets for object detection into YOLO format.
• Understand the fundamental concepts of the YOLO algorithm for computer vision and/or deep learning.