Dan 'Staer' Harris

One of the problems I encountered while working on my Groupon client for WebOS was that parsing and displaying the dates found in the JSON API feed got a bit messy. Lots of substring calls, and string concatenation in JavaScript. Being a Python programmer I wanted something similar to strptime() and strftime() but for the JavaScript 'Date' object. After doing some looking around the internet I couldn't find a good, light weight (suitable for mobile), implementation.

extDate.js is my re-implementation of Python's strptime() and strftime() for the JavaScript 'Date' object. The implementation is a near 100% conversion of the Python methods save a few differences. A few other features were added as well as the ability to localize content (output in spanish, etc). See the included documentation for more information on how to use the extensions, the differences from Python's implementation, and the other enhancements I made.

Like many folks out there, I enjoy checking Groupon every once in a while to see what kind of deals they have going for my area. One of the things I noticed was that there was no good application for my mobile platform of choice, WebOS.

The API from Groupon looked simple enough and I had wanted to try out coding something for WebOS so I wrote grpn, a very lightweight but full functional Groupon client. The client makes use of the entire Groupon API, allowing favorites, discussions, groupon says and more. The app is open source and free of charge on the HP/Palm WebOS app catalog.

I received my MS in Computer Science from the Rochester Institute of Technology in January of 2008. I studied biologically inspired computer vision with Dr. Roger Gaborski, culminating in my thesis about a biologically inspired visual attention model.

With high denition, high resolution, technology becoming ever more popular, the vast amount of input available to modern object recognition systems can become overwhelming. Given an image taken from a high resolution digital camera, a target object may be very small in comparison to the entire image. Additionally, any non-target objects in the input are considered unnecessary data, or clutter. While many modern object recognition systems have been created to be over 90% accurate in the recognition task, adding large amounts of clutter to an input quickly degrades both the speed and accuracy of many models.

To reduce both the size and amount of clutter in an input, a biologically inspired focus of attention model is developed. Utilizing biologically inspired feature extraction techniques, a feature based saliency model is built and used to simulate the psychological concept of a \mental spotlight". The simulated \mental spotlight" searches through each frame of a video, focusing on small sub-regions of the larger input which are likely to contain important objects that need to be processed in further detail. Each of these interesting sub-regions are then able to be used as input by a modern object recognition system instead of raw camera data, increasing both the speed and accuracy of the recognition model.