PUBLIC MARKS with search paper

This work measures 10 x 7.5 inches. It is signed and dated bottom right lightly in pencil, H. Stooshinoff 2011. There is a white border around the image, which will enhance framing. External paper size is 11 x 14 inches.

Here is the code i used to render the heightfield and generate the pdf.

repurposed e-paper development kit to display useful information on a door, such as a personal calendar, local weather, and recent news. It pulls bzip2'd images from a webserver that fetches and compresses the data to be displayed.

Calendar using the capillary action of the ink on the paper.

The Rhodia Webnotebook (nickednamed "Webbie") offers super smooth, fountain pen friendly, 90g French-milled Clairefontaine paper.

Most notable for its inclusion in the Kindel and other E-Readers, E-Paper has recently become very popular. But until very recently been out of reach to being used in personal projects. Luckily for us, SparkFun started selling and E-Paper display, and breakout board finally bringing this great technology to a place where we can slap it on the back of our Arduinos.

And if you like what you read and you decide you want to have a physical souvenir, you can buy the book and read it on paper.

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.