Computer Vision with SimpleCV Training Course

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

This instructor-led live training (available online or onsite) targets intermediate-level AI practitioners looking to build, deploy, and optimize vision and language models with 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 live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines for edge or cloud-based deployment scenarios.

Course Format

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

Course Customization Options

• To request a customized training session for this course, please reach out to us for arrangement.

This instructor-led, live training in Brazil (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:

• Grasp the fundamental concepts of computer vision in autonomous vehicles.
• Develop 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 Brazil (online or onsite) targets advanced 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 Brazil (online or onsite) is aimed at intermediate-level to advanced-level computer vision engineers, AI developers, and IoT professionals who wish to implement and optimize computer vision models for real-time processing on edge devices.

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

• Understand 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.
• Use frameworks like TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimize AI models for performance, power efficiency, and low-latency inference.

This guided, live training in Brazil (online or on-site) targets entry-level law enforcement officers seeking to transition from manual facial sketching to employing AI tools for developing facial recognition systems.

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

• Grasp 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 utilizing AI tools and frameworks to construct facial recognition models.
• Obtain practical experience in designing, training, and evaluating facial recognition systems.
• Comprehend the ethical implications and established best practices surrounding 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 Brazil (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 library licensed under the BSD license, encompassing hundreds of computer vision algorithms.

Audience

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

This instructor-led, live training in Brazil (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 employed to identify specific patterns within an image. It helps determine whether certain characteristics exist in a captured image, such as verifying the presence of a required label on a defective product on a factory line or confirming the specified dimensions of a component. Unlike "Pattern Recognition"—which identifies general patterns based on larger sets of related samples—Pattern Matching explicitly defines what is being sought and confirms whether the expected pattern is present.

Course Format

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

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

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

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

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

Audience

• Python programmers interested in Computer Vision

Course Format

• A mix of lectures and discussions, along with exercises and extensive hands-on practice

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

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

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

• Grasp the fundamental concepts of object detection.
• Set up 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.
• Resolve common issues related to YOLOv7 implementation.