
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 Indonesia or in NobleProg corporate training centers in Indonesia.
NobleProg -- Your Local Training Provider
Testimonials
I genuinely enjoyed the hands-on approach.
Kevin De Cuyper
Course: Computer Vision with OpenCV
The easy use of the VideoCapture functionality to acquire video images from laptop camera.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: Computer Vision with OpenCV
I enjoyed the advises given by the trainer about how to use the tools. This is something that can't be got from the internet and are very useful.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: Computer Vision with OpenCV
I enjoyed the advises given by the trainer about how to use the tools. This is something that can't be got from the internet and are very useful.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: Computer Vision with OpenCV
It was easy to follow.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: Computer Vision with OpenCV
Computer Vision Subcategories in Indonesia
Computer Vision Course Outlines in Indonesia
This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example
Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.
After completing this course, delegates will be able to:
- understand Caffe’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, implementing layers and logging
Some of Marvin's video applications include filtering, augmented reality, object tracking and motion detection.
In this instructor-led, live course participants will learn the principles of image and video analysis and utilize the Marvin Framework and its image processing algorithms to construct their own application.
Format of the Course
- The basic principles of image analysis, video analysis and the Marvin Framework are first introduced. Students are given project-based tasks which allow them to practice the concepts learned. By the end of the class, participants will have developed their own application using the Marvin Framework and libraries.
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
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.
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.
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
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.
By the end of this training, participants will be able to:
- Use computer vision techniques to identify lanes.
- Use Keras to build and train convolutional neural networks.
- Train a deep learning model to differentiate traffic signs.
- Simulate a fully autonomous car.
Audience
This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.
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.