Back to School - Training on Computer Vision & Machine Learning
We have the pleasure to host our malaysian partners from Tritytech for a joint training before their presentation at the Scilab Conference 2018, on "Making Our Computer Vision System Smarter".
Register for this professional training on November 19th in Paris. The training costs 700€ per person. Seats are limited.
About the course
Computer vision deals with how computers can be used for gaining high level understanding from digital images or video with the aim to automate human visual system. The tasks include methods for acquiring, processing, analyzing and understanding digital images, which could perform better working along with Machine Learning algorithm.
Machine Learning is a subset of Artificial Intelligence, in which a system is focused on training system to perform tasks by giving the machine training data. The word “learning” define the scope of the systems in which the system would learn either by supervised or unsupervised training.
This training would focus in machine learning for computer vision, and then gradually guide participants to implemented pre-trained deep learning model in Scilab, which is the subset of the machine learning with more hidden layers in the network.
The training would be conducted mainly using Scilab and IPCV module (Image Processing and Computer Vision Module).
Fundamental of Computer Vision
- Interacting with webcam from Scilab
- Images and Video Frames
- Image Processing vs Computer Vision
Artificial Intelligence, Machine Learning, or Deep Learning?
- Introduction to Artificial Intelligence and the current development
- Introduction to various types of Machine Learning system
- Introduction to Deep Learning and the current states and variants
Image Filtering and Convolution
- Image convolution and correlation
- Spatial domain filtering
- Frequency domain filtering
Image Feature Detection and Extraction
- Image thresholding
- Edge detection
- Morphological segmentation
- Color-based image segmentation
- IPCV functions for features detections and extractions
Classification and Recognition
- Introduction classification to recognition
- Using conventional methods
- Using Machine Learning methods (NN and SVM)
- Using Deep Learning methods (using pre-trained CNN model from Scilab)
Who should attend
Lecturers, students, programmers, developers, engineers and simply anyone who would like to work on intelligence systems for their projects are encouraged to attend the course.