Illusion 3 Able Software

Autostereogram Wikipedia. A random dot autostereogram encodes a 3. D scene which can be seen with proper viewing technique. Complete game play of Dogfight The Great War HD video made by Veedi. An autostereogram is a singleimage stereogram SIS, designed to create the visual illusion of a threedimensional scene from a twodimensional image. Dr Yan explores the Thatcher illusion a surprising effect created by manipulating images usually of faces and displaying them upsidedown. The Easiest Way To Create Your Own 3D Animations With This 3D Studio Professional Program 3D Max Maya Lightwave. ParticleIllusion.jpg' alt='Illusion 3 Able Software Teacher\u0027s Toolbox' title='Illusion 3 Able Software Teacher\u0027s Toolbox' />Illusion 3 Able Software IncClick on thumbnail to see full size image. Cross eyed vergence. Arrow indicates accommodation. Wall eyed parallel convergence. The top and bottom images produce a dent or projection depending on whether viewed with cross or wall eyed vergence. An autostereogram is a single image stereogram SIS, designed to create the visual illusion of a three dimensional 3. D scene from a two dimensional image. In order to perceive 3. D shapes in these autostereograms, one must overcome the normally automatic coordination between accommodation focus and horizontal vergence angle of ones eyes. The illusion is one of depth perception and involves stereopsis depth perception arising from the different perspective each eye has of a three dimensional scene, called binocular parallax. The simplest type of autostereogram consists of horizontally repeating patterns often separate images and is known as a wallpaper autostereogram. When viewed with proper convergence, the repeating patterns appear to float above or below the background. The well known Magic Eye books feature another type of autostereogram called a random dot autostereogram. One such autostereogram is illustrated above right. Illusion 3 Able Software 3ddoctorIn this type of autostereogram, every pixel in the image is computed from a pattern strip and a depth map. A hidden 3. D scene emerges when the image is viewed with the correct convergence. Autostereograms are similar to normal stereograms except they are viewed without a stereoscope. A stereoscope presents 2. D images of the same object from slightly different angles to the left eye and the right eye, allowing us to reconstruct the original object via binocular disparity. When viewed with the proper vergence, an autostereogram does the same, the binocular disparity existing in adjacent parts of the repeating 2. Illusion 3 Able Software' title='Illusion 3 Able Software' />D patterns. There are two ways an autostereogram can be viewed wall eyed and cross eyed. Most autostereograms including those in this article are designed to be viewed in only one way, which is usually wall eyed. Wall eyed viewing requires that the two eyes adopt a relatively parallel angle, while cross eyed viewing requires a relatively convergent angle. An image designed for wall eyed viewing if viewed correctly will appear to pop out of the background, while if viewed cross eyed it will instead appear as a cut out behind the background and may be difficult to bring entirely into focus. HistoryeditIn 1. British scientist Charles Wheatstone published an explanation of stereopsis binocular depth perception arising from differences in the horizontal positions of images in the two eyes. Winclone 4 here. He supported his explanation by showing pictures with such horizontal differences, stereograms, separately to the left and right eyes through a stereoscope he invented based on mirrors. When people looked at these flat, two dimensional pictures, they experienced the illusion of three dimensional depth. Between 1. David Brewster, a Scottish scientist, improved the Wheatstone stereoscope by using lenses instead of mirrors, thus reducing the size of the device. Brewster also discovered the wallpaper effect. He noticed that staring at repeated patterns in wallpapers could trick the brain into matching pairs of them as coming from the same virtual object on a virtual plane behind the walls. This is the basis of wallpaper style autostereograms also known as single image stereograms. In 1. H. W. Dove described cross eyed viewing as a stereoscope with a standard pair of stereoscopic images. In 1. Boris Kompaneysky6 published the first random dot stereogram containing an image of the face of Venus,7 intended to be viewed with a device. In 1. 95. 9, Bela Julesz,89 a vision scientist, psychologist and Mac. Arthur Fellow, invented the random dot stereogram while working at Bell Laboratories on recognizing camouflaged objects from aerial pictures taken by spy planes. At the time, many vision scientists still thought that depth perception occurred in the eye itself, whereas now it is known to be a complex neurological process. Julesz used a computer to create a stereo pair of random dot images which, when viewed under a stereoscope, caused the brain to see 3. D shapes. This proved that depth perception is a neurological process. Japanese designer Masayuki Ito, following Julesz, created a single image stereogram in 1. Swiss painter Alfons Schilling created a handmade single image stereogram in 1. Julesz. 1. 2 Having experience with stereo imaging in holography, lenticular photography, and vectography, he developed a random dot method based on closely spaced vertical lines in parallax. In 1. 97. 9, Christopher Tyler of Smith Kettlewell Institute, a student of Julesz and a visual psychophysicist, combined the theories behind single image wallpaper stereograms and random dot stereograms the work of Julesz and Schilling to create the first black and white random dot autostereogram also known as single image random dot stereogram with the assistance of computer programmer Maureen Clarke using Apple II and BASIC. This type of autostereogram allows a person to see 3. D shapes from a single 2. D image without the aid of optical equipment. In 1. 99. 1 computer programmer Tom Baccei and artist Cheri Smith created the first color random dot autostereograms, later marketed as Magic Eye. A computer procedure that extracts back the hidden geometry out of an autostereogram image was described by Ron Kimmel. In addition to classical stereo it adds smoothness as an important assumption in the surface reconstruction. How they workeditSimple wallpaperedit. This is an example of a wallpaper with repeated horizontal patterns. Each pattern is repeated exactly every 1. The illusion of the pictures lying on a flat surface a plane further back is created by the brain. Non repeating patterns such as arrows and words, on the other hand, appear on the plane where this text lies. Stereopsis, or stereo vision, is the visual blending of two similar but not identical images into one, with resulting visual perception of solidity and depth. In the human brain, stereopsis results from complex mechanisms that form a three dimensional impression by matching each point or set of points in one eyes view with the equivalent point or set of points in the other eyes view. Using binocular disparity, the brain derives the points positions in the otherwise inscrutable z axis depth. When the brain is presented with a repeating pattern like wallpaper, it has difficulty matching the two eyes views accurately. By looking at a horizontally repeating pattern, but converging the two eyes at a point behind the pattern, it is possible to trick the brain into matching one element of the pattern, as seen by the left eye, with another similar looking element, beside the first, as seen by the right eye. With the typical wall eyed viewing, this gives the illusion of a plane bearing the same pattern but located behind the real wall. The distance at which this plane lies behind the wall depends only on the spacing between identical elements. Autostereograms use this dependence of depth on spacing to create three dimensional images. Interactive Whiteboards Illusion of Choice Post. Interactive Whiteboards Illusion of Choice. In the first of two posts, heres a feature by feature comparison of Google Jamboard, Cisco Spark Board, and Microsoft Surface Hub. In the first of two posts, heres a feature by feature comparison of Google Jamboard, Cisco Spark Board, and Microsoft Surface Hub. Earlier this month Google announced the general availability of Jamboard, making it the third out of three collaboration Gargantuas to add an interactive whiteboard to its portfolio. I just posted a series of profiles detailing which companies sell what when it comes to these supersized tablets that aim to drive easels and dry erase markers out of the conference room. It turns out theres a whole cast of characters in this particular drama, from edgy start ups like Display Ten to projector industry stalwarts like Ricoh. Some, like NEC, are names we come across regularly here at No Jitter. Others, such as In. Focus, less so. But since Google competes mainly with Microsoft and since Jamboard is directly comparable to Ciscos Spark Board, lets zero in on these three behemoths of enterprise collaboration software and see how their interactive whiteboards compare. And how better to compare things like this than lining up their spec sheets Sadly, thats not so easy different sheets, different specs. But heres what Ive pieced together by combing through product docs, blogs, email exchanges, and webinars. Its a bit of a work in progress, so let me know if you see anything that needs correction. Note Ive only tried to compare the 5. Why complicate things further with different models, which tend to have slightly different specs Comparison of 5. Interactive Whiteboards from Google, Cisco, and Microsoft. Google Jamboard. Ciscco Spark Board. Microsoft Surface Hub. Price4,9. 99 5. Software. Customized version of Android Marshmallow. Spark Board OSWindows 1. Team. Processor. Nvidia Jetson TX1. Nvidia Jetson TX1. Intel Core i. 5Interface. Display resolution. Hz refresh rate. 4k, 1. Hz refresh rate. 10. Hz refresh rate. Stylus. Passive. Passive. Active. Camera. 10. FOV4k. 60, 8. 3 horizontal FOVTwo 1. FOVMicrophone. 2 omni directional stereo mics, plus a third integrated with camera. Speaker. Down facing. Front facing. Front facing. Sensors. Unknown. None camera used to detect motion2 passive IR motion sensors, ambient light sensor. Companion app. Yes. Yes. No. IOHDMI, USB Type C, 2 USB 3. SPDIF audio out. HDMI, 2 USB 3. Bluetooth currently inactivated2 USB 3. USB 2. 0, stereo out, RJ1. Display. Port video out. NFCYes. No. Yes. Network ports. Gigabit Ethernet, 8. Ethernet, 8. 02. 1. Gigabit Ethernet, 8. Screen mirroring. Google Cast. Intelligent Proximity. Miracast. Weight. Power consumption. W in use no sleep mode. W in use, 4. 5W in sleep mode. Unknown. Language support. English. English, Spanish, French, German.