- Next Five Years for Robots — plausible summary of the near future progression, taken from Helen Greiner’s DEMOlabs talk.
- Raspberry Pi Compute Module — a Raspberry Pi shrunk down to fit on a SODIMM with onboard memory, whose connectors you can customise for your own needs. (via Makezine)
- Behavioural Economics and Public Policy (Financial Times) — interesting how A/B trials revealed that implementations of Cialdini’s social proof didn’t test as well as non-social-proof persuasive techniques. More useful than something that claims to be the right answer is knowing when you’re closer to the right answer. (via Mind Hacks)
- Halide Language — open source programming language designed to make it easier to write high-performance image processing code on modern machines. Its current front end is embedded in C++. Compiler targets include x86/SSE, ARM v7/NEON, CUDA, Native Client, and OpenCL.
ENTRIES TAGGED "image processing"
Our Robot Future, Embeddable Pi, Behavioural Economics Not Solved Problem, and Imagine Processing Language
Understanding Image Processing, Sharing Data, Fixing Bad Science, and Delightful Dashboard
- 2D Image Post-Processing Techniques and Algorithms (DIY Drones) — understanding how automated image matching and processing tools work means you can also get a better understanding how to shoot your images and what to prevent to get good matches.
- Scientists Need to Learn to Share — despite science’s reputation for rigor, sloppiness is a substantial problem in some fields. You’re much more likely to check your work and follow best data-handling practices when you know someone is going to run your code and parse your data.
- METRICS — Meta-Research Innovation Center at Stanford. John Ioannidis has a posse: connecting researchers into weak science, running conferences, creating a “journal watch”, and engaging policy makers. (says The Economist)
- Grafana — elegant dashboard for graphite (the realtime data graphing engine).
Jan Erik Solem describes elements and useful tools for computer vision
In this interview, Jan Erik Solem, author of the upcoming book "Programming Computer Vision with Python," describes the uses for some common operations, and choices programmers have.
AR Theme Park, Digital Citizenship, Simulating Faces, and Reverse-Engineering Pixels
- South Korean Kinect+RFID Augmented Reality Theme Park — Sixty-five attractions over seven thematic stages contribute to the experience, which uses 3D video, holograms and augmented reality to immerse guests. As visitors and their avatars move through the park, they interact with the attractions using RFID wristbands, while Kinect sensors recognize their gestures, voices and faces. (via Seb Chan)
- Digital Citizenship — computers in schools should be about more than teaching more than just typing to kids, they should know how to intelligently surf, to assess the quality of their sources, to stay safe from scammers and bullies, to have all the training they need to be citizens in an age when life is increasingly lived online. (via Pia Waugh)
- Simulating Anatomically Accurate Facial Expressions (University of Auckland) — video of a talk demonstrating biomechanical models which permit anatomically accurate facial models.
- Depixelizing Pixel Art (Microsoft Research) — this is totally awesome: turning pixel images into vector drawings, which of course can be smoothly scaled. (via Bruce Sterling)