Half a croissant, on a plate, with a sign in front of it saying '50c'
h a l f b a k e r y
Clearly this is a metaphor for something.

idea: add, search, annotate, link, view, overview, recent, by name, random

meta: news, help, about, links, report a problem

account: browse anonymously, or get an account and write.



Specular Reflection Image Enhancement

Reconstruct unseen parts of a scene by analysing reflections and refractions in the image
  [vote for,

We've all seen Rick Deckard and apparently the people on CSI enhancing images to a ludicrous extent, enabling the image to reveal objects on the other sides of walls and under the floorboards or whatever. All of this is clearly ludicrous. At the same time I still think my "gainy image compression" (see link) would work to some extent. I also think this would.

I was recently perusing a picture of myself from a house I lived in a few years ago and noted that because I happened to know a specular reflective surface, to wit a metallic zip fastener, was facing a scene including a carpet, a piano stool, upright piano and street scene through a window, I was able to glean which teeth were reflecting which objects. At the same time the lenses on a pair of glasses reflected a much more easily enhanced image of the same scene.

Just now, I went into the kitchen to note the presence of a chrome milk jug, kettle, cutlery, a wine glass, metallic draining board and sink reflected from the aforesaid chrome jug, and was able to infer from those reflections and refractions while facing away from the window that there was a window with a pair of curtains, some kind of garden and a partly clouded sky.

Therefore, it seems to me, and don't ask me how, that if something like raytracing could be performed in reverse in order to reconstruct scenes which are not obviously in the image and away from which the camera is facing. I can vaguely suggest how but there would need to be a mechanisable bit which I have little idea how to approach.

It amounts to computer vision and then some. The specular reflections in an image clearly depend on the angle of the surfaces from which they reflect. If there's a way of identifying areas of reflection and specular reflection and calculating those, the likes of refraction through transparent media and off glass, metal and smooth surfaces of other kinds could be used to reconstruct the unseen parts of a photographic image. The same applies to video, but in this case the job may be easier because movement would give clues as to the shapes and angles concerned.

I realise this is a big vague blob of an idea with a few little details worked out round the edges but surely this could be done one day, couldn't it?

nineteenthly, Jul 23 2017

Reflectoscope Reflectoscope
[theircompetitor, Jul 23 2017]

Gainy image compression Gainy_20image_20compression
Aimless self-promotion [nineteenthly, Jul 23 2017]

Camera Sees around Corners http://www.popularm...ees-around-corners/
Similar specular goings on here, with lasers [Zeuxis, Jul 24 2017]


       I think a truly specular reflection carries very little information. However, there are plenty of instances of people recovering images from reflections - for instance, in a high-resolution photo of a face, you can recover an image of the photographer from the reflections in the subject's eyes.
MaxwellBuchanan, Jul 23 2017


       "Gimme a hard copy right there".   

8th of 7, Jul 23 2017

       Must've been the glitter wot did it, [ of ].
nineteenthly, Jul 23 2017

       <Cambodian woman>   

       "Not fish ... snake scale !!"   

       </Cambodian woman>
8th of 7, Jul 23 2017

       Over the last few months, I've been considering on and off (well, twice, once a few months ago and the second time the other day) the issue of robots understanding mirrors and other reflective objects. There's the well-known mirror test that's used on animals (and has been used on at least one robot model, which has a specific optical signal that it uses to differentiate the image of itself in the mirror from another similar robot it sees) but there are more basic levels than that. Here's what I've been thinking about:   

       Level 0.5: The robot can be told that something is a mirror, and then understands that objects that appear to be behind the mirror actually aren't where they appear, and that it should not attempt to interact with them physically through the mirror.   

       Level 1: The robot is capable of identifying a mirror (or other reflective object) in its visual field, and understands that a mirror is something that distorts its visual view of the world, in that objects that appear to be behind the mirror actually aren't where they appear, and that it should not attempt to interact with them physically through the mirror.   

       Level 2: The robot understands that things it sees in the mirror correspond to objects on this side of the mirror, and can learn information about objects via the mirror that it could not learn by direct line-of- sight observation (e.g. the appearances of backs of objects, the locations of hidden objects).   

       Higher levels that I forget the order and details of since thinking them up: The robot recognizes itself in the mirror without having to use a special signal. The robot can coordinate its actions by viewing itself/the environment via the mirror. The robot understands the visual effects of curved mirrors, dirty mirrors, partially reflective mirrors, etc., and how to work with them.   

       Similar comprehension levels could be defined for refractive objects, periscopes, etc.
notexactly, Jul 23 2017

       [Level 0.1] Robot drives through the mirror. Says "Oh, no. Not again!"   

       [Level 5.0] Robot says " Do you think my assembly looks too big in this?"
Ling, Jul 23 2017

       Mirror mirror on the wall, who's the Faradayest of them all? (+)   

       [Level 2.0] Robot drives through the mirror being carried by two men ... then hits a fruit cart ... a rack of clothes ... a pile of empty cardboard boxes ... some flimsy wooden crates full of live chickens ...
8th of 7, Jul 24 2017


back: main index

business  computer  culture  fashion  food  halfbakery  home  other  product  public  science  sport  vehicle