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Tag Archives: Recreate

War Vets Recreate Photo 50 Years Later

December 30, 2019   Humor

Posted by Krisgo

 War Vets Recreate Photo 50 Years Later

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About Krisgo

I’m a mom, that has worn many different hats in this life; from scout leader, camp craft teacher, parents group president, colorguard coach, member of the community band, stay-at-home-mom to full time worker, I’ve done it all– almost! I still love learning new things, especially creating and cooking. Most of all I love to laugh! Thanks for visiting – come back soon icon smile War Vets Recreate Photo 50 Years Later


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c-ment pond to cement shoes, Trump’s attempt to recreate Camelot is more like Spamalot

June 18, 2019   Humor
 c ment pond to cement shoes, Trumps attempt to recreate Camelot is more like Spamalot

“The Clampetts may have been ignorant in some things but they were good people who always did the right thing. Trumps are just stupid, spoiled, greedy and evil, nothing like the Clampetts. Maybe more like Paris Hilton and Nicole Richie in the Simple Life but evil.”



With so many instances of trying to become American royalty, Trump has tried to manipulate his brand, hoping that his post-presidency will mythologically be identified as Kennedyesque. Mar-a-Lago is not Hyannis Port, and the new laundered money of post-soviet Russia is not like Joe Kennedy’s bootlegging.

This time he claims he “designed” a new color scheme for Air Force One, because he’s smarter than JFK and Raymond Loewy combined, even if the palette is lifted from his personal plane and the design references at least one historical iteration of United Airlines and American Airlines.

At the same time he decides to reference JFK’s FLOTUS, because isn’t Melania so much like Jackie Kennedy, even if Trump stupidly referenced her name from her second marriage.

The pathetic delusions of Trumpian unhingery continue as he seeks more diversions from his continuing errors, as if making himself seem more like JFK would absolve the more obvious office-fitness of #F*tNixon.

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Trump tells Fox he wants to redo Air Force One livery even though the iconic baby blue was “Jackie O.” He then says, “We have a new Jackie O. It’s called Melania.”

— Philip Rucker (@PhilipRucker) June 14, 2019

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The Trumps have been trying to sell their reign as modern day Camelot for quite some time. Melania’s Inauguration Day ensemble was a not-so-subtle request for people to start making the comparison. https://t.co/74giZvO4Jd

— Amanda Carpenter (@amandacarpenter) June 14, 2019


so innovative – Trump uses his personal plane’s colors



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JFK chose font for “UNITED STATES OF AMERICA” on fuselage of Air Force One because it resembled font of early printed version of Declaration of Independence: pic.twitter.com/LOQ9KgAnOd

— Michael Beschloss (@BeschlossDC) June 14, 2019

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Researchers develop AI that can re-create real-world lighting and reflections

August 16, 2018   Big Data

Ever shop online for carpet or fabric and wish you could tell what it looks like in real life? Thanks to researchers at the Massachusetts Institute of Technology’s Computer Science and Artificial Intelligence Lab (CSAIL) and Inria Sophia Antipolis in France, you’re one step closer to being able to experience that.

Today during the 2018 Siggraph conference in Vancouver, the team jointly presented “Single-Image SVBRDF Capture with a Rendering-Aware Deep Network,” a method for extracting the texture, highlights, and shading of materials in photographs and digitally recreating the environment’s lighting and reflection.

“[V]isual cues … allow humans to perceive material appearance in single pictures,” the researchers wrote. “Yet, recovering spatially-varying bi-directional reflectance distribution functions — the function of the four variables that defines how light is reflected at an opaque surface — from a single image based on such cues has challenged researchers in computer graphics for decades. We tackle [the problem] by training a deep neural network to automatically extract and make sense of these visual cues.”

The researchers started with samples — lots of them. They sourced a dataset of more than 800 “artist-created” materials, ultimately selecting 155 “high-quality” sets from nine different classes (paint, plastic, leather, metal, wood, fabric, stone, ceramic tiles, ground) and, after setting aside about a dozen to serve as a testing set, rendered them in a virtual scene meant to mimic a cellphone camera’s field of view (50 degrees) and flash.

It wasn’t enough to train a machine learning model, though, and so to amplify the materials dataset, the researchers used a cluster of 40 CPUs to mix and randomize their parameters. Ultimately, they generated 200,000 realistically-shaded, “widely diverse” materials.

 Researchers develop AI that can re create real world lighting and reflections

Above: The model didn’t always get it right.

The next step was model training. The team designed a convolutional neural network — a type of machine learning algorithm that roughly models the arrangement of neurons in the visual cortex — to predict four light maps corresponding to per-pixel normal (illumination values for each pixel on the rendered image), diffuse albedo (diffuse light reflected by a surface), specular albedo (mirror-like reflections of light waves), and specular roughness (the “glossiness” of reflections).

To minimize variability among the maps’ values, they formulated a “similarity metric” that compared renderings of the predicted maps against renderings of ground truth measurements. And to ensure consistency across output images, they introduced a second machine learning model that combined global illumination (i.e., light reflecting off the surface) information extracted from each pixel with local information — facilitating, the researchers wrote, the “back-and-forth exchange of information across distant image regions.”

They trained the network for 400,000 iterations and fed it 350 photos snapped with an iPhone SE and Nexus 5X, which were cropped to approximate the training data’s field of view. The result? The model performed rather well, successfully reproducing real-world reflections of light on metal, plastics, wood, paint, and other materials.

It wasn’t without its limitations, unfortunately. Hardware constraints limited it to images of 256 x 256 pixels, and it struggled to reproduce lighting and reflections from photos with low dynamic range. Still, the team noted that it generalized well to real photographs and showed, if nothing else, that “a single network can be trained to handle a large variety of materials.”

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