Computer Vision Training in Skåne

This instructor-led, live training session in Skåne (online or onsite) targets computer vision engineers, AI developers, and IoT professionals at an 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 able to:

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

This instructor-led, live training in Skåne (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) delivers robust deployment and optimization capabilities for real-time AI applications in computer vision and natural language processing, particularly when leveraging Huawei Ascend hardware.

This instructor-led 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 environments.

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

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Utilize CANN tools to convert models and integrate them into live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for both edge and cloud deployment scenarios.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on labs focusing on model deployment and performance profiling.
• Live pipeline design exercises using real-world CV and NLP use cases.

Course Customization Options

• For customized training arrangements, please contact us to discuss your specific needs.

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

Upon completing this training, participants will be able to:

• Grasp the fundamentals of Artificial Intelligence and Machine Learning.
• Master the basics of digital image processing and its role in facial recognition.
• Develop proficiency in using AI tools and frameworks to build facial recognition models.
• Gain practical experience in creating, training, and testing facial recognition systems.
• Comprehend ethical considerations and best practices regarding the use of facial recognition technology.

This instructor-led, live training in Sweden (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, available Skåne (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 Skåne (online or onsite) is intended for intermediate to advanced developers, researchers, and data scientists who want to learn how to implement real-time object detection using YOLOv7.

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

• Grasp the core 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 package that bundles 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
• Utilize 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 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 BSD-licensed library that includes several hundreds of computer vision algorithms.

Audience

This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects

This instructor-led, live training in Skåne (online or onsite) is tailored for software engineers interested in programming with Python and 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 method employed to identify specific patterns within an image. This technique helps determine whether defined characteristics exist in a captured image, such as verifying the correct label on a defective product on a factory line or confirming the specified dimensions of a component. It differs from "Pattern Recognition," which identifies general patterns based on larger collections of related samples, because it explicitly defines what is being sought and then confirms whether the expected pattern is present.

Course Format

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

Computer Vision is a domain focused on the automatic extraction, analysis, and comprehension of valuable insights from digital media. Python stands out as a high-level programming language, renowned for its clean syntax and code readability.

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

Upon completing this training, participants will be able to:

• Grasp the core concepts of Computer Vision
• Utilize Python to execute Computer Vision tasks
• Develop custom systems for face, object, and motion detection

Audience

• Python developers interested in Computer Vision

Course Format

• A blend of lectures and discussions, complemented by 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 Skåne (online or onsite) is designed for backend developers and data scientists who wish to integrate pre-trained YOLO models into their enterprise applications and implement cost-effective object detection components.

By the end 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 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.