PUBLIC MARKS with search algorithm

Estimating geographic information from an image is an excellent, difficult high-level computer vision problem whose time has come. The emergence of vast amounts of geographically-calibrated image data is a great reason for computer vision to start looking globally — on the scale of the entire planet! In this paper, we propose a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach. For this task, we will leverage a dataset of over 6 million GPS-tagged images from the Internet. We represent the estimated image location as a probability distribution over the Earth's surface. We quantitatively evaluate our approach in several geolocation tasks and demonstrate encouraging performance (up to 30 times better than chance). We show that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification.

Unfortunately, if the data store starts acting up, Google's code uses an exponential backoff algorithm that could cause the export process to grind to a halt (I never actually saw it recover, it just gets slower). Moreover, because export processes take days, you will need to run a nohup background process, but Google's export script requires an interactive login.

Here is my suggestion: spin up a dedicated EC2 instance, push your code and install Google App Engine linux, embed your username/password directly into the script, remove the exponential backoff, and then run the following

Comment exporter un gros jeu de données de Google App Engine ? C'est simple, allez à la concurrence chez Amazon et lancer l'export depuis là-bas, parce que là au moins, ça a une chance d'aboutir.

Ragnarök, our implementation of the HTML5 Parsing algorithm.

Since engineers are connected by their shared work, a tool that discovers connections in their work-related repositories can help.

Here we describe the Codebook framework for mining software repositories. It is flexible enough to address all of the problems identified by our survey with a single data structure (graph of people and artifacts) and a single algorithm (regular language reachability).

This is an implementation of the Knuth and Plass line breaking algorithm using JavaScript and the HTML5 canvas element. The goal of this implementation is to optimally set justified text in the new HTML5 canvas element, and ultimately provide a library for various line breaking algorithms in JavaScript.

The two of us see the world as a stream of color, and in 2009 we finally had a chance to draw the river in our heads. We began with a collection of photographs of the Boston Common taken from Flickr. Using an algorithm developed for the WIRED Anniversary visualization, our software calculated the relative proportions of different colors seen in photos taken in each month of the year, and plotted them on a wheel. The image below is an early sketch from the piece. Summer is at the top, with time proceeding clockwise.

We're seeing a lot of action in the key/value map/reduce world lately. On the one hand this is because simpler stuff scales more simply, and on the other because key/value and B-Tree stores map cleanly to some fundamental algorithms.