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12 Twitchy Stereoscopic Photographs

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Stereoscopic GIFs alternate between the left and right images of a stereogram, capturing the same image from two different vantage points positioned inches apart. Instead of layering the resulting images on top of one another to create a 3D effect, the artist turns them into animated GIFs, twitching ever so slightly from the minuscule difference in perspective. The resulting effect creates images that have a sense of depth and three dimensional quality to them, without the need for those annoying glasses. WARNING: Staring at the following images for more than a coffee break or two might cause serious damage to your workflow.

The next two are from Portland-based art director Matt Moore. You can check out his Tumblr here.









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pretty pictures
9 Exhilarating Close-Up Photos of Sharks
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Dive into the world of Shark, a new book by award-winning photographer Brian Skerry.

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technology
AI Algorithm Tells You the Ingredients in Your Meal Based on a Picture
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Your food photography habit could soon be good for more than just updating your Instagram. As Gizmodo reports, a new AI algorithm is trained to analyze food photos and match them with a list of ingredients and recipes.

The tool was developed by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). To build it, they compiled information from sites like All Recipes and Food.com into a database dubbed Recipe1M, according to their paper. With more than a million annotated recipes at its disposal, a neural network then sifted through each one, learning about which ingredients are associated with which types of images along the way.

The result is Pic2Recipe, an algorithm that can deduce key details about a food item just by looking at its picture. Show it a picture of a cookie, for example, and it will tell you it likely contains sugar, butter, eggs, and flour. It will also recommend recipes for something similar pulled from the Recipe1M database.

Pic2Recipe is still a work in progress. While it has had success with simple recipes, more complicated items—like smoothies or sushi rolls, for example—seem to confuse the system. Overall, it suggests recipes with an accuracy rate of about 65 percent.

Researchers see their creation being used as a recipe search engine or as a tool for situations where nutritional information is lacking. “If you know what ingredients went into a dish but not the amount, you can take a photo, enter the ingredients, and run the model to find a similar recipe with known quantities, and then use that information to approximate your own meal,” lead author Nick Hynes told MIT News.

Before taking the project any further, the team plans to present its work at the Computer Vision and Pattern Recognition Conference in Honolulu later this month.

[h/t Gizmodo]

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