The work of Friedman is summerized by Bennie Mol in De KIJK February 24, 2021 Martijn Wisse his bioinspired robotics is inspired by Friedman's work, which claims that learning the minimalisation of surprise is.It would be interesting to see if I could solve Excercise 01, to draw a virtual reality cube on a chessboard, with ros2-tf instead of Matlab.

When I have time, I should read chapter 5 (Deep Learning).

And the basic set of 2D geometric image transformation at page 169.

I also like the binary image morphology example at page 138, including equations on the next page.

Also like the color-space example on page 99.

The book has some nice overviews of computer vision algorithms from the 70s, 80s, 90s, 00s and 10s.

As literature he not only recomment chapter 4 of his book, but also Computer Vision: Algorithms and Applications (2nd ed, 2021).

In his first lecture Davide nicely introduces the biological background and some history.

Recent version of the slides can be found at the course Vision Algorithms for Mobile Robotics (2021).

For the course Autonomous Mobile Robots I always used the slides from Davide Scaramuzza (2012).

The only case study which is open is Network homphily.

Seems that I finished Using Python for Research course nearly completely.Most of my material was based on Vision Algorithms for Mobile Robotics (Fall 2021).The latest presentation on Autonomous Mobile Robots seems to be Spring 2017.As application example I used the coins example from Harvey Rhody.For the most part, I followed Elli Angelopoulu's lecture on the Hough Transform, who actually used some slides from Jeremey Wyatt.Found this page with a nice example of the overlapping circles in Hough-space:.

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