Computer Vision Training in Christchurch

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

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

• Grasp the fundamentals of Edge AI and its applications within computer vision.
• Deploy optimised deep learning models on edge devices for real-time image and video analysis.
• Utilise frameworks such as TensorFlow Lite, OpenVINO, and the NVIDIA Jetson SDK for model deployment.
• Optimise AI models for performance, power efficiency, and low-latency inference.

This instructor-led, live training in Christchurch (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.
• Visualise and interpret the results of image classification models.

The CANN SDK (Compute Architecture for Neural Networks) delivers powerful deployment and optimisation tools for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

This instructor-led, live training (online or on-site) is designed for intermediate-level AI practitioners who wish to build, deploy and optimise vision and language models using the CANN SDK for production use cases.

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

• Deploy and optimise CV and NLP models using CANN and AscendCL.
• Use CANN tools to convert models and integrate them into live pipelines.
• Optimise inference performance for tasks such as detection, classification and sentiment analysis.
• Build real-time CV/NLP pipelines for edge or cloud-based deployment scenarios.

Course Format

• Interactive lecture and demonstration.
• Hands-on lab covering model deployment and performance profiling.
• Live pipeline design using real-world CV and NLP use cases.

Course Customisation Options

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

This instructor-led, live training in Christchurch (online or on-site) 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 Christchurch (online or on-site) 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.

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 Christchurch (online or onsite) is aimed at beginner-level to intermediate-level researchers and laboratory professionals who wish to process and analyse 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 utilise ImageJ's core functions.
• Preprocess and enhance scientific images for better analysis.
• Analyse images quantitatively, including cell counting and area measurement.
• Automate repetitive tasks using macros and plugins.
• Customise workflows for specific image analysis needs in biological research.

This instructor-led, live training in Christchurch (delivered online or on-site) is designed for intermediate-level professionals who wish to use Vision Builder AI to design, implement, and optimise 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 optimise system performance.

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

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

• Grasp the fundamental concepts of object detection.
• Install and configure YOLOv7 for object detection tasks.
• Train and evaluate custom object detection models using YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Troubleshoot common issues associated with 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 hundred computer vision algorithms.

Audience

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

This instructor-led, live training in Christchurch (delivered online or on-site) is tailored for software engineers who wish to program in Python using OpenCV 4 for deep learning applications.

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 meaningful reports from images and videos.

Pattern Matching is a method employed to identify specific patterns within an image. This technique allows users to confirm the presence of defined characteristics in a captured image, such as verifying the correct label on a defective item along a factory line or checking the specified dimensions of a component. Unlike "Pattern Recognition," which identifies general patterns from broad collections of related data, pattern matching is specific: it defines exactly what to look for and confirms whether that expected pattern exists.

Course Format

• This course covers the approaches, technologies, and algorithms utilised in pattern matching as it applies to Machine Vision.

Computer Vision is a field focused on automatically extracting, analysing, and interpreting valuable information from digital media. Python is a high-level programming language renowned for its clear syntax and readability.

In this instructor-led, live training, participants will learn the fundamentals of Computer Vision while building a series of simple Computer Vision applications using Python.

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

• Understand the fundamentals of Computer Vision
• Use Python to implement Computer Vision tasks
• Build their own face, object, and motion detection systems

Audience

• Python programmers interested in Computer Vision

Course Format

• A blend of lecture, discussion, exercises, and extensive hands-on practice

SimpleCV is an open-source framework—meaning it is a collection of libraries and software tools you can use to develop computer vision applications. It enables you to work with images or video streams from webcams, Kinect devices, FireWire cameras, IP cameras, or mobile phones. It helps you build software that allows your technologies not only to see the world but also to understand it.

Audience

This course is aimed at engineers and developers who want to create computer vision applications using SimpleCV.

This instructor-led, live training in Christchurch (online or on-site) is designed for back-end developers and data scientists who wish to integrate pre-trained YOLO models into their enterprise-driven applications 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.
• Customise 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.