Online or onsite, instructor-led live Computer Vision training courses demonstrate through interactive discussion and hands-on practice the basics of Computer Vision as participants step through the creation of simple Computer Vision apps.
Computer Vision training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Computer Vision training can be carried out locally on customer premises in Indiana or in NobleProg corporate training centers in Indiana.
NobleProg -- Your Local Training Provider
Indianapolis, IN - Lockerbie Marketplace
333 N. Alabama Street Suite 350, Indianapolis, United States, 46204
Regus at Lockerbie Marketplace is centrally located in downtown Indianapolis and easily accessible by car, with public parking available along North Alabama Street and in nearby garages. Visitors flying into Indianapolis International Airport (IND) can reach the venue in approximately 20 to 25 minutes via taxi or rideshare, following I‑70 E and exiting onto New York Street toward downtown. For public transit users, IndyGo routes serving the Massachusetts Avenue and Chatham Arch districts stop within a few blocks, making the location convenient for those traveling from other parts of the city.
Fort Wayne, IN - Regus – Power Center
110 E Wayne St floor 12, Fort Wayne, United States, 46802
The venue is conveniently located in downtown Fort Wayne, easily accessible by car via Interstate 69 through either the South Clinton Street or Apple Street exits, which lead directly into the Wayne Street corridor. Visitors will find nearby parking garages as well as metered street parking options. For those arriving by air, the venue is approximately 13 miles northeast of Fort Wayne International Airport (FWA), with a taxi or rideshare ride taking about 20 minutes via I‑69 and Jefferson Boulevard. Public transit is also available: Citilink buses serve downtown with stops just a few blocks away from the venue, near the intersection of Wayne and Clinton Streets.
Indianapolis, IN - Regus – Parkwood Crossing Center
450 E 96th St #500, Indianapolis, United States, 46240
This venue is conveniently accessed by car via the I‑465 beltway, exiting north onto Keystone Avenue before turning onto E 96th Street; ample parking is available in the adjacent surface and garage lots. For those arriving by air, the Indianapolis International Airport (IND) is approximately 17 miles away, with taxis or rideshares taking roughly 25–30 minutes via I‑465 and Keystone Avenue. Public transit is available via IndyGo routes 19 and 120, which serve the 96th Street corridor; the bus stop at Parkwood Crossing is only a short walk from the building.
This instructor-led, live training in Indiana (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 Indiana (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 Indiana (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 Indiana (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 Indiana (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 instructor-led, live training in Indiana (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 Indiana (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.
Fiji is an open-source image processing package that bundles ImageJ (an image processing program for scientific multidimensional images) and a number of plugins for scientific image analysis.
In this instructor-led, live training, participants will learn how to use the Fiji distribution and its underlying ImageJ program to create an image analysis application.
By the end of this training, participants will be able to:
Use Fiji's advanced programming features and software components to extend ImageJ
Stitch large 3d images from overlapping tiles
Automatically update a Fiji installation on startup using the integrated update system
Select from a broad selection of scripting languages to build custom image analysis solutions
Use Fiji's powerful libraries, such as ImgLib on large bioimage datasets
Deploy their application and collaborate with other scientists on similar projects
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
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 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 Indiana (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.
OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.
In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.
By the end of this training, participants will be able to:
Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
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 field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python is a high-level programming language famous for its clear syntax and code readibility.
In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python.
By the end of this training, participants will be able to:
Understand the basics of Computer Vision
Use Python to implement Computer Vision tasks
Build their own face, object, and motion detection systems
Audience
Python programmers interested in Computer Vision
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces the software, hardware, and step-by-step process needed to build a facial recognition system from scratch. Facial Recognition is also known as Face Recognition.
The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.
By the end of this training, participants will be able to:
Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi.
Configure OpenCV to capture and detect facial images.
Understand the various options for packaging a Rasberry Pi system for use in real-world environments.
Adapt the system for a variety of use cases, including surveillance, identity verification, etc.
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
Other hardware and software options include: Arduino, OpenFace, Windows, etc. If you wish to use any of these, please contact us to arrange.
SimpleCV is an open source framework — meaning that it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It’s helps you build software to make your various technologies not only see the world, but understand it too.
Audience
This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.
This instructor-led, live training in Indiana (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.
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Testimonials (2)
Trainer was very knowlegable and very open to feedback on what pace to go through the content and the topics we covered. I gained alot from the training and feel like I now have a good grasp of image manipulation and some techniques for building a good training set for an image classification problem.
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