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Two great examples of using Computer Vision to beat Super Hexagon.
Super Hexagon is a really hard game. The goal of Super Hexagon is to control a small triangle which circles around a central hexagon (which occasionally collapses into a pentagon or square in the hexagon and hyper hexagon difficulty) attempting to avoid contact with incoming "walls".
First example from Valentin Trimaille's Super Hexagon bot:
Ray Casting Wall Detection
The point is that a bot for this game makes a really nice image processing project to start learning OpenCV: simple shapes but lots of human disturbing effects, fast-paced game meaning real-time is required, very simple controls: rotate CW or CCW... (full article)
Second example from Shaun LeBron's Super Hexagon Unwrapper:
This project is written in Python. It employs Computer Vision algorithms provided by SimpleCV to establish a reference frame in the image. Then it warps (or "unwraps") the image based on that reference frame, using OpenGL fragment shaders... (github code) (full explanation)
From Yale's OpenHand Project:
This project intends to establish a series of open-source hand designs, and through the contributions of the open-source user community, result in a large number of useful design modifications and variations available to researchers.
Based on the original SDM Hand, the Model T is the OpenHand Project's first released hand design, initially introduced at ICRA 2013. the four underactuated fingers are differentially coupled through a floating pulley tree, allowing for equal force output on all finger contacts.
Based on our lab's work with iRobot and Harvard on the iHY hand, which won the DARPA ARM program, the Model O replicates the hand topology common to several commercial hands, including ones from Barrett, Robotiq, and Schunk (among others). A commercial version of this hand is currently for sale by RightHand Robotics... (homepage)
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