This video shows the low power SCAMP3 vision chip system tracking and counting multiple objects at low power. The APRON environment was used to develop the tracking algorithm, and simulate it running on the device.
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 bug, but the algorithm will count multiple bugs. You can see the count in the top right.
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 with non-linear transformations and sobel being calculated.
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 doing, it looks nice none the less.
I should point out that the screen capture software used here is not recording at the full rate. This makes the video look sticky. The performance of this algorithm is limited by the frame-rate of the camera.
In benchmarks it takes approximately 2ns to update a neuron (2.0GHz Intel Core2 DUO)
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 see them adapt with the presentation of stimuli. Unlike a normal SOM, this approach limits the inhibitory radius, creating a patchy response layer.
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.
Check out this video of real-time optic flow being calculated in the APRON environment. The approach is a basic block-matching algorithm, but the APRON environment allows you to interactively analyse and debug the running algorithm.
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.
This video shows the low power SCAMP3 vision chip system tracking and counting multiple objects at low power. The APRON environment was used to develop the tracking algorithm, and simulate it running on the device.
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 bug, but the algorithm will count multiple bugs. You can see the count in the top right.
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 with non-linear transformations and sobel being calculated.
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 doing, it looks nice none the less.
I should point out that the screen capture software used here is not recording at the full rate. This makes the video look sticky. The performance of this algorithm is limited by the frame-rate of the camera.
In benchmarks it takes approximately 2ns to update a neuron (2.0GHz Intel Core2 DUO)
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 see them adapt with the presentation of stimuli. Unlike a normal SOM, this approach limits the inhibitory radius, creating a patchy response layer.
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.
Check out this video of real-time optic flow being calculated in the APRON environment. The approach is a basic block-matching algorithm, but the APRON environment allows you to interactively analyse and debug the running algorithm.
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.