Visual Network Rehash

Filename : Harold E. Edgerton bullet Apple 1991.89.14_1a.jpeg Original source: Harold E. Edgerton, .30 Bullet Piercing an Apple (1964) Image type : high synced photography, stroboscopy

Additional details : Back to well-debated objective claims during modern times, images finally succeded in revealing and being valued as proof - because, if not mere reproduction of reality, it still has a charge of veracity concerning the reality that most of humans share.

Images are usefull to show up an image out of non-visible state, it's powerful as a media that as it's ability to penatrate through the mind of the observer (fun enough to mention the now outdated magic bullet theory rooted in 30's behaviorism) and to be encapsulaed with non-tangible elements such as time and space that turns out to be quantified as observable informations.

Filename : Isoradial.jpg; courbes-flux-constant.jpg; TN-illumin4.jpg Original source:  Image of a spherical black hole with thin accretion disk, J.-P Luminet in Astronomy and Astrophysics, vol. 75, May 1979; dedicated blog article after Insterstellar's blackhole contreverse (2015) Image type : schemas

Filename : jean pierre lumineux les trous noirs 1992.jpg; alain riazuelo image simule trou noir stellaire 2006.jpg; vgpmstoxpwdenevum8h4; TN-wired.png; Original source: J.-P Luminet (1978), Alain Riazuelo (2006?), unidentified, DNeg for Christopher Nolan's Interstellar movie (2014). Image type : calculed simulations

Video name : Information Landscapes Original source: Visible Language Workshop. (c) MIT Media Lab. July 1994. Image type : video

Additional details : Muriel Cooper, Design Quarterly n° 142 « Computer and Design », 1989, p. 23.

Filename : Montage_20150110-1024x1024.jpg Original source: Rosetta blog ESA - January 10, 2015 Image type : stiched photography

Additional details : "This four-image mosaic comprises images taken from a distance of 27.5 km from the centre of Comet 67P/Churyumov-Gerasimenko on 10 January. The image resolution is 2.3 m/pixel and the individual 1024 x 1024 frames measure 2.4 km across. The mosaic is slightly cropped and measures 4.2 x 3.8 km.

Four image mosaic comprising images taken on 10 January 2015. Rotation and translation of the comet during the imaging sequence make it difficult to create an accurate mosaic, so always refer to the individual images before drawing conclusions about any strange structures or low intensity extended emission. Credits: ESA/Rosetta/NAVCAM – CC BY-SA IGO 3.0

The image shows the smaller lobe of the comet in the lower left corner, and the larger lobe to the upper right. The smaller lobe is actually in the foreground, and its rotation over the time elapsed between the first and the fourth frame is significant, making the mosaic particularly tricky.

Filename : 2000px-Correlation_examples2.svg Original source :  Wikipedia - May 2011 Image type : scatterplot

Filename : City size.png Original source :  MIT Technology Review- January 16, 2015 Image type : scatterplot

Filename : CCD.png; YCbCr.gif Original source :  wikipedia Image type : schemas with photo sample, animated model

Additionnal details : ”Y′CbCr is not an absolute color space; rather, it is a way of encoding RGB information. The actual color displayed depends on the actual RGB primaries used to display the signal. Therefore a value expressed as Y′CbCr is predictable only if standard RGB primary chromaticities are used."

Filename : 1105841711.LGL.2D.4000x4000.png, home-page-pic.png Original source : The OPTE project, 2005 and 2010 Image type : data modelisation 

Additionnal details : "This project was originally created to generate a picture (or map) of the Internet using a single computer and a single Internet connection to determine the relationship between every routable network on the Internet – in 24 hours. That was back in 2003-2004 when computing and networking technology were very limited, compared to what is possible today."

Filename : Camera37.gif, Camera69.gif Original source : Paulo Dias at IEETA/Universidade de Aveiro, Portugal - 15/01/2004 Image type : flowchart, snapshot of applying model

Additionnal details : Found by searching "algorithm" on Google Images.

Filename : btl_function.gif Original source : Code Project, 21 August 2003 Image type : tri-dimmensional model

Additionnal details : Found almost randomly by searching "algorthm" on Google Images, then used as result for algorithm representation.

Filename : world bande passante 77 tbits 2012.jpg Original source : n/a, various Image type : modeling

Filename : Principe_cbir.png Original source : Wikipedia, March 2006 Image type : schematic, diagram

Additionnal details : "Reverse image search is content-based image retrieval (CBIR) query technique that involves providing the CBIR system with a sample image that it will then base its search upon; in terms of information retrieval, the sample image is what formulates a search query. In particular, reverse image search is characterized by a lack of search terms. This effectively removes the need for a user to guess at keywords or terms that may or may not return a correct result. Reverse image search also allows users to discover content that is related to a specific sample image,popularity of an image, and discover manipulated versions and derivative works.

Reverse image search may be used to: Locate the source of an image Find higher resolution versions Discover webpages where the image appears Track down the content creator Get information about an image

Filename : Sparkle.png Original source : MIT Technology Review, January 9, 2015 Image type : annoted photographic samples

Additionnal details : "The ongoing revolution in image processing has produced yet another way to extract images from a complex environment.[..]

It turns out that there is a straightforward way to approach this. Zhang and co consider each piece of glitter to be a randomly oriented micromirror. So light from the picture hits a micromirror and is reflected to a sensor inside the camera.

That means there is a simple mapping from each pixel in the original picture to a sensor in the camera. The task is to determine that mapping for every pixel. “There exists a forward scrambling matrix, and in principle we can find its inverse and unscramble the image,” they say.

To find this unscrambling matrix, Zhang and co shine a set of test images at the glitter screen and record where the pixels in the original image end up in the camera.

Filename : 400x-brevet_fig5.jpg, 384x-brevet_fig2b.jpg, 384x-brevet_fig4b_english1.jpg,300x-image_formation.jpg, 600x-ASIFT_saint_marco.jpg, 300x600-adam_zoom10_front-adam_zoom1_77degR_ASIFT_202matches.jpg Original source : Guoshen Yu and Jean-Michel Morel, 2010/01/09 Image type : schema, photographic sample