The power of classical Computer Vision
by Yoni Chechik

8 online sessions of theory and practice

Workshop Brief

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 will be held in Hebrew
  • The sessions will be recorded and available offline for the participants
  • This is an online program using zoom
90% OF THE PROCEEDS ARE DONATED TO 3 CHOSEN NONPROFIT ORGANISATIONS
THE OTHER 10% HEPLS US TO EXPAND OUR ACTIVITY

Who is it for?

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 ^_^ .

Who we donate to?

The Lecturer

Interdisciplinary work is my passion: Algorithms • Computer Vision & Image Processing • Digital Signal Processing • Machine learning, AI & NN • Hardware & System R&D

Yoni Chechik
Computer Vision & Algorithms researcher
Creator of AI is Math
Checkout his Linkedin ^_^

Schedule

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.

Syllabus

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.

  • Image representation
  • Pixel-wise operations
  • Histogram equalization (notebook)
  • Template matching
  • Morphology operators
  • Connected components
  • Color space (notebook)
  • Ex2: morphological operators and friends
  • Noise and filtering (notebook)
  • Frequency representation
  • Decimation
  • Interpolation

  • Intro to edges
  • Basic edge image (gradient filters notebook)
  • Edge thinning
    • LoG
    • NMS
  • Edge mask
  • Canny edge detector (edge detection notebook)
  • Other edge related topics
    • Frequency representation
    • Unsharp filter
  • Ex3: Bilateral filter

  • Least squares
  • Total least squares
  • RANSAC (LS, TLS & RANSAC notebook)
  • Ex4a: vignetting

  • Hough transform (Hough transform notebook)
    • (m,b)

      “>(𝑚,𝑏)(m,b) parameter space

    • (ρ,θ)

      “>(𝜌,𝜃)(ρ,θ) parameter space

  • Ex4b: Hough circles

  • BRDF
  • Pinhole camera
  • Digital camera
  • The human eye

  • 2D->2D transformations (notebook)
  • 3D->3D transformations
  • 3D->2D transformations (3D projections)
    • Perspective projection
    • Orthographic projection
  • What is camera calibration?
  • Camera extrinsics
  • Camera intrinsics
  • Full camera matrix
  • Calibration methods and distortions

  • What and why we need features detection?
  • Feature detection
    • Blob detection
    • Harris corner detection (Harris notebook)
    • SIFT detector
  • Feature description
    • Template matching
    • HOG
    • SIFT descriptor
  • SIFT feature matching (SIFT notebook)
  • Panoramas

  • Structure from motion
  • Epipolar geometry
    • Essential matrix
    • Fundamental matrix
    • Estimating the fundamental matrix
  • Camera rectification
  • Triangulation
  • Stereo matching
  • Other 3D sensors

Special price for early birds until 10.10.2020 🚀

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