Computer Vision with SimpleCV Training Course

The CANN SDK (Compute Architecture for Neural Networks) offers robust tools for deploying and optimizing real-time AI applications in computer vision and natural language processing (NLP), particularly on Huawei Ascend hardware.

This instructor-led training, available online or onsite, targets intermediate AI professionals looking 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 object detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Course Format

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

Customization Options

• For customized training arrangements for this course, please contact us directly.

This instructor-led, live training in Turkey (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 Turkey (available online or onsite) is designed for advanced professionals aiming to expand their expertise in computer vision and investigate TensorFlow's potential for creating sophisticated vision models via Google Colab.

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

• Construct 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 Turkey (online or onsite) is designed for intermediate to advanced computer vision engineers, AI developers, and IoT professionals who want 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 Turkey (online or onsite) is designed for beginner-level law enforcement personnel aiming to transition from manual facial sketching to utilizing AI tools for developing facial recognition systems.

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

• Understand the fundamental principles of Artificial Intelligence and Machine Learning.
• Learn the essentials of digital image processing and its application in facial recognition.
• Develop proficiency in using AI tools and frameworks to construct facial recognition models.
• Gain practical experience in creating, training, and testing facial recognition systems.
• Understand ethical considerations and best practices associated with facial recognition technology.

Fiji is a powerful open-source image processing package that bundles ImageJ (a program designed for scientific multi-dimensional 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

Course Format

• Interactive lecture and discussion
• Extensive exercises and practical application
• Hands-on implementation in a live-lab environment

Options for Customizing the Course

• To request customized training for this course, please contact us to make arrangements.

This instructor-led, live training in Fiji (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 BSD that offers hundreds of computer vision algorithms.

Audience

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

This instructor-led, live training in Turkey (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 verify whether certain characteristics exist in a captured image, such as confirming the presence of the correct label on a faulty item on a factory assembly line or verifying the exact dimensions of a component. Unlike "Pattern Recognition," which identifies general patterns by analyzing large sets of related samples, Pattern Matching explicitly defines the target pattern and simply confirms its presence or absence.

Course Format

• This course explores the approaches, technologies, and algorithms utilized in pattern matching within the context of Machine Vision.

Computer Vision is a domain focused on the automatic extraction, analysis, and comprehension of valuable insights from digital media. Python stands out as a high-level programming language renowned for its clear syntax and high code readability.

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

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

• Gaining a solid understanding of Computer Vision basics
• Implementing Computer Vision tasks using Python
• Developing custom systems for face, object, and motion detection

Audience

• Python programmers looking to explore Computer Vision

Format of the course

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

This instructor-led, live training in Turkey (online or onsite) targets 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 Turkey (online or on-site) targets backend developers and data scientists who aim to integrate pre-trained YOLO models into their enterprise applications and deploy cost-effective object-detection components.

Upon completing this training, participants will be able to:

• Install and set up the essential tools and libraries needed for object detection with YOLO.
• Customize Python command-line applications that run on YOLO pre-trained models.
• Apply the pre-trained YOLO model framework across various computer vision projects.
• Transform existing object detection datasets into YOLO-compatible format.
• Grasp the core concepts of the YOLO algorithm for computer vision and/or deep learning.

This instructor-led live training in Turkey (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:

• 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.