Computer Vision Training in Turin

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

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

• Grasp the core principles of Edge AI and its role 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.
• Enhance AI models for superior performance, power efficiency, and low-latency inference.

This instructor-led, live training in Turin (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 tools for deploying and optimizing real-time AI solutions in computer vision and natural language processing, particularly on Huawei Ascend hardware.

This instructor-led live training, available either online or on-site, is designed for intermediate-level AI professionals looking to construct, deploy, and refine vision and language models using the CANN SDK for production environments.

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

• Deploy and optimize computer vision (CV) and NLP models utilizing CANN and AscendCL.
• Leverage CANN utilities to convert models and embed them into live processing pipelines.
• Enhance inference performance for applications such as object detection, classification, and sentiment analysis.
• Create real-time CV/NLP pipelines suitable for both edge and cloud deployment scenarios.

Course Format

• Engaging lectures combined with practical demonstrations.
• Practical hands-on labs focused on model deployment and performance profiling.
• Real-time pipeline design using authentic CV and NLP use cases.

Customization Options

• For personalized training needs regarding this course, please reach out to us to arrange.

This instructor-led live training in Turin (online or onsite) is designed for intermediate 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:

• Grasp the core concepts of computer vision within autonomous vehicles.
• Develop algorithms for object detection, lane identification, and semantic segmentation.
• Integrate vision systems with other autonomous vehicle components.
• Utilize deep learning methods for advanced perception tasks.
• Assess the performance of computer vision models in real-world contexts.

This instructor-led, live training in Turin (online or onsite) is designed for beginner-level law enforcement officers 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:

• Comprehend the core principles of Artificial Intelligence and Machine Learning.
• Master the fundamentals of digital image processing and its role in facial recognition.
• Acquire proficiency in using AI tools and frameworks to construct facial recognition models.
• Obtain practical experience in designing, training, and evaluating facial recognition systems.
• Grasp the ethical implications and industry best practices associated with facial recognition technology.

This instructor-led, live training in Turin (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 Turin (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 Turin (online or onsite) targets intermediate to advanced-level developers, researchers, and data scientists who aim 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 an open-source image processing suite that integrates ImageJ—a program designed for handling scientific, multidimensional images—along with a variety of plugins dedicated to scientific image analysis.

During this instructor-led live training, participants will learn how to utilize the Fiji distribution and its core ImageJ program to develop an image analysis application.

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

• Leverage Fiji's advanced programming features and software components to extend ImageJ's capabilities
• Assemble large 3D images from overlapping tiles
• Automatically update a Fiji installation upon startup using its integrated update system
• Choose from a wide range of scripting languages to build custom image analysis solutions
• Utilize Fiji's powerful libraries, such as ImgLib, when working with large bioimage datasets
• Deploy their applications and collaborate with other scientists on similar projects

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source library licensed under the BSD license that encompasses hundreds of computer vision algorithms.

Audience

This course is designed for engineers and architects who wish to leverage OpenCV for their computer vision initiatives.

This instructor-led, live training in Turin (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 used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.

Format of the Course

• This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.

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

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

Upon completing this training, participants will be able to:

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

Audience

• Python programmers looking to enter the field of Computer Vision

Course Format

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

SimpleCV is an open-source framework, comprising a suite of libraries and software tools designed to facilitate the development of vision-based applications. It enables you to process images and video streams from diverse sources, including webcams, Kinect sensors, FireWire devices, IP cameras, and mobile phones. This framework empowers you to create software that not only captures the world visually but also interprets and understands it.

Audience

This course is tailored for engineers and developers aiming to build computer vision applications using SimpleCV.

This instructor-led, live training in Turin (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 pre-trained YOLO models.
• Deploy pre-trained YOLO model frameworks for various computer vision projects.
• Convert existing object detection datasets into YOLO format.
• Understand the fundamental concepts of the YOLO algorithm for computer vision and/or deep learning.