Computer Vision Training in Plovdiv

This instructor-led, live training in Plovdiv (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 live, instructor-led training in Plovdiv (online or onsite) is aimed at experienced 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.

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

This instructor-led live training, available online or onsite, targets intermediate-level AI professionals looking to build, deploy, and optimize vision and language models using the CANN SDK for production scenarios.

Upon completion of this training, participants will be capable of:

• Deploying and optimizing CV and NLP models via CANN and AscendCL.
• Utilizing CANN tools to convert models and integrate them into active pipelines.
• Enhancing inference performance for tasks such as detection, classification, and sentiment analysis.
• Developing real-time CV/NLP pipelines suitable for edge or cloud deployment environments.

Course Format

• Interactive lectures paired with live demonstrations.
• Practical labs focused on model deployment and performance profiling.
• Designing live pipelines using real-world CV and NLP use cases.

Customization Options

• To arrange customized training for this course, please contact us.

This instructor-led, live training in Plovdiv (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 Plovdiv (online or onsite) is aimed at 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 Plovdiv (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.

This guided, live training session Plovdiv (online or in-person) targets intermediate-level professionals aiming to leverage Vision Builder AI to design, implement, and refine automated inspection systems for SMT (Surface-Mount Technology) processes.

Upon completion of this training, participants will be capable of:

• Establishing and configuring automated inspections using Vision Builder AI.
• Acquiring and preprocessing high-quality images for analysis.
• Implementing logic-based decision-making for defect detection and process validation.
• Generating inspection reports and optimizing system performance.

This instructor-led live training Plovdiv (online or onsite) is designed for intermediate to advanced developers, researchers, and data scientists eager to learn real-time object detection implementation 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.

Fiji is a powerful open-source image processing package that bundles ImageJ (a program tailored 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 harness the Fiji distribution and its underlying ImageJ program to build 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 library licensed under BSD, offering hundreds of computer vision algorithms.

Audience

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

This instructor-led, live training in Plovdiv (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 allows for the determination of whether certain defined characteristics exist in a captured image, such as verifying the presence of the correct label on a faulty product on a factory assembly line or checking if a component meets specified dimensions. This process differs from "Pattern Recognition," which identifies general patterns by analyzing larger collections of related samples. In pattern matching, the goal is to define exactly what is being sought and then confirm whether that specific expected pattern is present.

Course Format

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

Computer Vision is a discipline focused on the automatic extraction, analysis, and interpretation of valuable information from digital media. Python, a high-level programming language renowned for its clean syntax and readability, serves as an excellent tool for this purpose.

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

Upon completing this training, participants will be able to:

• Comprehend the core concepts of Computer Vision
• Utilize Python to execute Computer Vision tasks
• Develop custom systems for face, object, and motion detection

Audience

• Python programmers seeking to expand into Computer Vision

Course Format

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

SimpleCV is an open-source framework, which implies that it consists of a set of libraries and software tools that you can leverage to develop vision applications. It allows you to process images or video streams sourced from webcams, Kinects, FireWire, IP cameras, or mobile devices. The platform facilitates the creation of software that enables your technologies not only to perceive the world but also to understand it.

Audience

This course is designed for engineers and developers who aim to build computer vision applications using SimpleCV.

This instructor-led, live training in Plovdiv (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.