Computer Vision with SimpleCV Training Course

The CANN SDK (Compute Architecture for Neural Networks) offers 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, live training session (available online or onsite) targets intermediate-level AI professionals aiming to build, deploy, and optimize vision and language models using the CANN SDK for practical production scenarios.

Upon completing this course, participants will be capable of:

• Deploying and optimizing CV and NLP models using CANN and AscendCL.
• Utilizing CANN tools to convert models and seamlessly integrate them into operational pipelines.
• Enhancing inference performance for applications such as detection, classification, and sentiment analysis.
• Constructing real-time CV/NLP pipelines suitable for edge or cloud-based deployment environments.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on laboratory exercises focusing on model deployment and performance profiling.
• Real-time pipeline design utilizing actual CV and NLP use cases.

Customization Options

• For customized training arrangements for this course, please contact us to discuss your needs.

This instructor-led, live training in Norway (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 Norway (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.

This instructor-led, live training in Norway (online or onsite) targets intermediate to advanced computer vision engineers, AI developers, and IoT professionals who wish 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 fundamentals of Edge AI and its practical 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 for enhanced performance, power efficiency, and low-latency inference.

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

By the end of this training, participants will be able 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 competence in employing AI tools and frameworks to construct facial recognition models.
• Obtain practical experience in developing, training, and evaluating facial recognition systems.
• Comprehend the ethical implications and adhere to best practices when deploying facial recognition technology.

Fiji is a powerful open-source image processing suite 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
• Choose 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 customized training for this course, please contact us to arrange.

This instructor-led, live training in Norway (online or on-site) targets beginner to intermediate researchers and laboratory professionals who need to process and analyze images of histological tissues, blood cells, algae, and other biological specimens.

Upon completing this training, participants will be able to:

• Navigate the Fiji interface and leverage ImageJ’s core capabilities.
• Preprocess and enhance scientific images to improve analytical outcomes.
• Perform quantitative image analysis, including cell counting and area measurements.
• Automate routine tasks through macros and plugins.
• Tailor workflows to meet specific image analysis requirements in biological research.

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

Target Audience

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

This instructor-led live training in Norway (online or onsite) is designed for software engineers who wish to apply Python and OpenCV 4 to deep learning projects.

By the conclusion 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 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 focuses on the automated extraction, analysis, and interpretation of valuable information from digital media. Python, recognized for its clear syntax and high readability, serves as an ideal high-level programming language for this domain.

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

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

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

Audience

• Python developers seeking to expand into Computer Vision

Course Format

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

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

By the conclusion 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 Norway (online or onsite) is intended for backend developers and data scientists who aim to integrate pre-trained YOLO models into their enterprise-driven software 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 for object detection using YOLO.
• Customize Python command-line applications that run on YOLO pre-trained models.
• Implement the framework of pre-trained YOLO models 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.

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

By the conclusion 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.