Computer Vision with SimpleCV Training Course

The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time AI solutions in computer vision and natural language processing, particularly on Huawei Ascend hardware.

This instructor-led live training, available online or onsite, targets intermediate 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 utilizing CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into operational pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Course Format

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

Customization Options

• For customized training arrangements, please reach out to us.

This instructor-led live training in South Korea (online or onsite) targets intermediate-level AI developers and computer vision engineers who aim to build robust vision systems for autonomous driving applications.

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

• Grasp 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 South Korea (online or onsite) is designed for advanced-level professionals seeking 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 South Korea (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 completing this training, participants will be able to:

• Grasp the fundamentals of Edge AI and its 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 performance, power efficiency, and low-latency inference.

This instructor-led, live training in South Korea (available online or on-site) targets beginner-level law enforcement professionals seeking to shift from manual facial sketching to AI-driven tools for developing facial recognition systems.

Upon completion 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 relevance to 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 industry best practices associated with facial recognition technology.

Fiji is a powerful open-source image processing package 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
• 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 South Korea (online or onsite) targets beginner to intermediate-level researchers and laboratory professionals who want to process and analyze images of histological tissues, blood cells, algae, and other biological specimens.

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

• Navigate the Fiji interface and leverage ImageJ’s core functionalities.
• Preprocess and enhance scientific images to improve analysis accuracy.
• Perform quantitative image analysis, such as cell counting and area measurement.
• Automate repetitive tasks using macros and plugins.
• Customize workflows to meet specific image analysis requirements 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 South Korea (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 dedicated to automatically extracting, analyzing, and interpreting meaningful data from digital media. Python stands out as a high-level programming language, renowned for its clear syntax and code readability.

In this instructor-led live training, participants will explore the fundamentals of Computer Vision by working through the development of several simple Computer Vision applications using Python.

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

• Grasp the core concepts of Computer Vision
• Apply Python to implement 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, discussions, exercises, and extensive hands-on practice

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

By the conclusion of this training, participants will be capable of:

• Grasping the fundamental concepts of object detection.
• Installing and configuring YOLOv7 for object detection tasks.
• Training and testing custom object detection models using YOLOv7.
• Integrating YOLOv7 with other computer vision frameworks and tools.
• Troubleshooting common issues related to YOLOv7 implementation.