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 (NLP), particularly on Huawei Ascend hardware.

This instructor-led live training, available online or onsite, targets intermediate-level AI practitioners looking to develop, 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 seamlessly integrate them into live pipelines.
• Enhance inference performance for tasks such as object detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud deployment scenarios.

Course Format

• Interactive lectures and live demonstrations.
• Hands-on lab sessions focused on model deployment and performance profiling.
• Live pipeline design exercises using practical CV and NLP use cases.

Course Customization Options

• For tailored training requirements, please contact us to arrange a customized session.

This instructor-led, live training in Malaysia (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 Malaysia (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 Malaysia (online or onsite) targets intermediate to advanced computer vision engineers, AI developers, and IoT professionals who wish to implement and optimise computer vision models for real-time processing on edge devices.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of Edge AI and its applications in 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 NVIDIA Jetson SDK for model deployment.
• Optimise AI models for performance, power efficiency, and low-latency inference.

This instructor-led live training in Malaysia (available online or onsite) is tailored for beginner-level law enforcement personnel who aim to transition from manual facial sketching to employing 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.

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.

This instructor-led, live training in Malaysia (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.

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 Malaysia (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 method employed to identify specific patterns within an image. This technique helps determine whether certain defined characteristics exist in a captured image, such as verifying the correct label on a faulty product on a production line or confirming that a component meets specified dimensions. It differs from "Pattern Recognition," which identifies broader patterns based on extensive sets of related samples, by focusing on pinpointing exactly what is being sought and confirming the presence or absence of that specific pattern.

Course Format

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

Computer Vision is a discipline focused on automatically extracting, analyzing, and interpreting valuable information from digital media. Python, a high-level programming language renowned for its clear syntax and readability, serves as an ideal tool for this purpose.

During this instructor-led live training, participants will gain a foundational understanding of Computer Vision while building a series of simple applications using Python.

Upon completing this training, participants will be able to:

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

Audience

• Python programmers who wish to explore Computer Vision

Course Format

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

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

This instructor-led live training in Malaysia (available online or onsite) targets intermediate to advanced developers, researchers, and data scientists seeking 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 underlying object detection.
• Install and configure YOLOv7 for various object detection tasks.
• Train and evaluate custom object detection models leveraging YOLOv7.
• Seamlessly integrate YOLOv7 with existing computer vision frameworks and tools.
• Resolve common challenges associated with YOLOv7 implementation.