Computer Vision with SimpleCV Training Course

Caffe is a deep learning framework designed with emphasis on expressiveness, speed, and modularity.

This course delves into the application of Caffe as a deep learning tool for image recognition, using MNIST as an illustrative example.

Audience

This course is ideal for deep learning researchers and engineers who wish to leverage Caffe as their framework.

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

• grasp the structure and deployment methods of Caffe
• execute installation tasks, manage production environments, and configure architecture
• evaluate code quality, conduct debugging, and monitor performance
• implement advanced functionalities such as training models, creating layers, and logging

This instructor-led, live training in the UAE (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 the UAE (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 the UAE (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 instructor-led, live training in the UAE (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.

Fiji is an open-source image processing package that integrates ImageJ (a program designed for scientific multidimensional images) along with numerous plugins for advanced scientific image analysis.

During this instructor-led live session, participants will learn how to utilize the Fiji distribution and its core ImageJ software to develop an application for image analysis.

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

• Leverage Fiji's sophisticated programming capabilities and software modules to enhance ImageJ
• Combine large 3D images from overlapping segments
• Automatically update a Fiji installation upon startup using its built-in update system
• Select from a wide range of scripting languages to create custom image analysis solutions
• Utilize powerful libraries like ImgLib for extensive bioimage datasets with Fiji
• Deploy their application and collaborate with other researchers on similar projects

Course Format

• Interactive lecture and discussion.
• Absorbing exercises and ample practice opportunities.
• Practical implementation in a live-lab setting.

Customization Options for the Course

• To request tailored training for this course, please contact us to arrange.

This instructor-led, live training in the UAE (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 encompasses hundreds of computer vision algorithms.

Audience

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

This instructor-led, live training in the UAE (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. It helps ascertain whether certain features are present in a captured image, such as identifying the expected label on a faulty product on an assembly line or verifying the precise dimensions of a part. This technique differs from "Pattern Recognition," which identifies general patterns based on extensive collections of related samples, by specifically defining what needs to be found and then indicating if the anticipated pattern is present.

Course Format

• This course covers the methodologies, technologies, and algorithms utilized in the realm of pattern matching as it pertains to Machine Vision.

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

In this instructor-led live training session, participants will gain foundational knowledge in Computer Vision by developing a series of basic applications using Python.

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

• Grasp the fundamentals of Computer Vision
• Utilize Python for executing Computer Vision tasks
• Create their own face, object, and motion detection systems

Audience

• Python developers interested in exploring Computer Vision

Course Format

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

This instructor-led, live training in the UAE (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 the UAE (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 the UAE (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.