Category Archives: Examples
Apologies for the boring bits in this video, the bugs just won’t do what I tell them to. Anyway, the task was to use a SCAMP3 device to count bugs entering the field of the camera. I only own one … Continue reading
A quick video showing some of the Playstation Eye capabilities being used in APRON. The screen capture software is a little slow (and consumes a great deal of CPU), but frame rates at QVGA can easily reach 180 FPS, even … Continue reading
APRON is capable of performing spiking neural network simulations. Here we see 6 interconnected layers of Izhikevich neurons, with various projections between the layers. The input stimulus is from a webcam. Although I’ve no idea what the model is actually … Continue reading
APRON can also handle colour images. Those extra two dimensions of data can be really handy for visualisation and segmentation. Check out the video below. Also shown are some more features of the APRON environment.
Here is a video of APRON executing a self organising map. It’s trivialised to highlight certain features of the APRON simulation environment. The main feature here, is that APRON can explode developing receptive fields implemented with LinkMaps, so you can … Continue reading
APRON is really good at array processing, and sometimes it can be used for applications other than image processing. Check out the video below, showing the iterative calculation of a Mandelbrot set.
Sometimes, it is very useful to interact with the simulation. Sliders can be used for numerous reasons, from controlling coefficients to displaying values in real-time. // Tutorial 2 – Sliders // TUTORIALS ARE DESIGNED TO BE READ AND STEPPED THROUGH … Continue reading
This tutorial introduces the concept of registers. // Tutorial 1 – Registers // TUTORIALS ARE DESIGNED TO BE READ AND STEPPED THROUGH IN THE // APRON SIMULATOR. THEY WILL NOT DO INTERESTING THINGS IF RAN! // (c) David R W … Continue reading
The code below is a good starting point for looking at the basics of APRON syntax. APRON is an interpreted functional script, where all commands are of the form y = f(x1, x2, x3). Each line of APRON code directly … Continue reading
The following demo video shows APRON (with the aid of a CUDA plug-in) learning and tracking an object selected by the user. The algorithm behind this is quite naive, but shows surprising robustness to rotation.