- Image representation
- Pixel-wise operations
- Histogram equalization (notebook)
- Template matching
- Morphology operators
- Connected components
- Color space (notebook)
- Ex2: morphological operators and friends
We will learn both hands-on and the theory behind some of the best known algorithms behind CV (computer vision).
Even though nowadays it is common to hear about deep learning based solutions, there are many algorithms that can give us great results with less effort-time-data, plus, those algorithms can also be a great companion to deep learning solutions.
Each week a different topic will be covered – starting from the basics and working the way to some of the best known computer vision algorithms.
The sessions are build of theory + interactive code practicing with google colab + home exercises.
In the workshop, we will use python, numpy & matplotlib, opencv.
Starting date 02.11.2020, every Monday until (including) 21.12.2020 between 19:00-21:00.
Additional details will be sent after registration.
The workshop is for anyone who has at least 2 years of experience with programming (preferably some knowledge of python) , logic orientation, and an outstanding will to learn.
In case you don’t have prior knowledge with code, you can still gain a lot and close the gap with the right will and effort – but please take into consideration that it will be challenging ^_^ .
Each workshop day is built from live lecturing and hands-on practice.
We will be available over slack to answer anything for the rest of the week.
Practice makes perfect – at the each workshop day you will have lots of home exercise to help you examine all you’ve learned.
During the workshop you will meet the “Amutot” we donate to in this workshop.
What differs AI from other kind of algorithms? What’s the relation between AI and Machine Learning? How did it all start and what is Deep Learning? In this lecture we will cover the buzz words, history and general notions around the topic of AI from a technical perspective.
“>(𝑚,𝑏)(m,b) parameter space
“>(𝜌,𝜃)(ρ,θ) parameter space