Pixel 4D: The App that Lets You Enjoy Amazing 3D Scanning and Photogrammetry Features on Your Phone
4D-PIXEL is a smart wall that physically reacts to your voice and music and displays 3D letters. This interactive sculpture is a merging of electromagnetics, software and electronics. The dynamic of the 4D-PIXEL wall is made of hundreds of pixels which react to the dynamics in sound frequencies. This way there is a direct relation between human activity and the appearance of the surface; in this fusion between body and machine.
The Original 4D animated live wallpaper appDo you like 3D wallpapers? You'll love 4D for sure!Photographic quality backgrounds, become live with this new per pixel animation technology!This is a totally new approach of making real 4D animated live wallpapers!Forget poor quality live wallpapers that drain your battery, and get into a new magic world of amazing per pixel animated backgrounds!And all that at a minimum battery usage below 2% Tilt your device and witness the stunning 4D depth effect controlled by your device's motion sensors (gyroscope or accelerometer).With new 4D live wallpapers being submitted almost every day, we have a constantly growing collection that you can choose from!Choose between many categories such as Anime 4D wallpapers, Superheroes, Space and Galaxy scenes, Themes for girls, Nature, Super cars, Abstract, Dark, Animals, and many more different 4D backgrounds (many of them free).Transform your screen with cool wallpapers that fit your style, add live wallpapers to your favourites with one click, use our search engine to find what you want, find most popular 4D backgrounds, make your phone unique with this amazing 4D Depth Effect!Personalize even more by adjusting the effects strength and set the battery saving mode to minimize consumption at 1%You will never get bored again of your home screen!FEATURES:Support for portrait and landscape orientationsSmooth animationsMany different categories Battery efficient! New items added continuously! Embedded search engine! Fully customizable! The Original per pixel 4D animation app!We want to bring you the best experience we can, so we never put ads into our apps.Many 4D backgrounds are totally free and some others are premium, while some can be unlocked by watching rewarded videos. Support us by unlocking the Pro version. You only pay once to access all features - No hidden cost.Maybe one of the best live wallpapers collections.. Because 3D wallpapers are nice.. but 4D is Better!Get the Original per pixel animated live wallpaper app now! its FREE!
While the diffraction map implementation on the TEM enhances the spatial resolution in comparison to its SEM counterpart EBSD down to the few-nanometer scale, 4D STEM acquisition with a pixelated detector delivers improved yield and sensitivity when mapping crystalline domains and conducting relative orientation/texture analyses.
Super-resolution image reconstruction techniques play an important role for improving image resolution of lung 4D-CT. We presents a super-resolution approach based on fast sub-pixel motion estimation to reconstruct lung 4D-CT images. A fast sub-pixel motion estimation method was used to estimate the deformation fields between "frames", and then iterative back projection (IBP) algorithm was employed to reconstruct high-resolution images. Experimental results showed that compared with traditional interpolation method and super-resolution reconstruction algorithm based on full search motion estimation, the proposed method produced clearer images with significantly enhanced image structure details and reduced time for computation.
VISTA, Calif., Dec. 14, 2017 (GLOBE NEWSWIRE) -- CES 2018 - TetraVue, the leader in high definition 4D video LIDAR technology, today announced that the company will be demonstrating a game changing full-motion 4D video camera at CES 2018 in Las Vegas from January 9-12, in the North Hall, Booth 9130. For the first time, the multi-megapixel resolution and motion capture accuracy of digital video are combined with the range capability of LIDAR. TetraVue adds an entirely new fourth dimension of depth to digital video with the ability to capture accurate depth information at the pixel-level. This new camera technology is poised to transform markets including autonomous vehicles, machine vision, factory automation and the entertainment industry.
TetraVue 4D video technology merges capabilities of digital video and LIDAR with a radically different approach. TetraVue illuminates a scene with a non-visible, eye safe flash at up to thirty frames per second to capture full motion. Distance to each pixel in an image is determined simultaneously with a patented optical encoder coupled with a multi-megapixel CMOS image sensor. The camera outputs a greyscale high resolution image with depth information registered to each pixel. The technology is extensible to full color in the future.
With increased cinema animation, visual effects and the rapid merging of real and virtual worlds driven by augmented and virtual reality, more efficient means of digital content capture and processing are required. With depth per pixel knowledge, objects or scene backgrounds can be quickly manipulated or removed. With multiple cameras or by panning a single camera, motion accurate digital 3D models of actors, objects or scenes can be rapidly created. Accurate digital models of sets and scenes allow for more efficient previsualization and retargeting of assets.
We present recent results of the R&D for a novel 4D fast tracking system based on rad-hard pixel sensors and front-end electronics capable of reconstructing four dimensional particle trajectories in real time. Particularly relevant results are: i) timing resolution of 30 ps for 55 micron pitch 3D silicon pixel sensors measured in a recent beam test, ii) design and production of front-end electronics prototype chip, iii) a stub-based fast tracking algorithm implemented and tested in commercial FPGA using a pipelined architecture. Tracking performance for a 4D pixel detector for a future upgrade of the LHCb experiment will be also discussed.
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Superpixel segmentation of 2D image has been widely used in many computer vision tasks. However, limited to the Gaussian imaging principle, there is not a thorough segmentation solution to the ambiguity in defocus and occlusion boundary areas. In this paper, we consider the essential element of image pixel, i.e., rays in the light space and propose light field superpixel (LFSP) segmentation to eliminate the ambiguity. The LFSP is first defined mathematically and then a refocus-invariant metric named LFSP self-similarity is proposed to evaluate the segmentation performance. By building a clique system containing 80 neighbors in light field, a robust refocus-invariant LFSP segmentation algorithm is developed. Experimental results on both synthetic and real light field datasets demonstrate the advantages over the state-of-the-arts in terms of traditional evaluation metrics. Additionally the LFSP self-similarity evaluation under different light field refocus levels shows the refocus-invariance of the proposed algorithm.
This study was performed to investigate the detection performance of trapped air in dynamic chest radiography using 4D extended cardiac-torso (XCAT) phantom with a user-defined ground truth. An XCAT phantom of an adult male (50th percentile in height and weight) with a normal heart rate, slow-forced breathing, and diaphragm motion was generated. An air sphere was inserted into the right lung to simulate emphysema. An X-ray simulator was used to create sequential chest radiographs of the XCAT phantom over a whole respiratory cycle covering a period of 10 seconds. Respiratory changes in pixel value were measured in each grid-like region translating during respiration, and differences from a fully exhaled image were then depicted as color-mapping images, representing higher X-ray translucency (increased air) as higher color intensities. The detection performance was investigated using various sizes of air spheres, for each lung field and behind the diaphragm. In the results, respiratory changes in pixel value were decreased as the size of air sphere increased, depending on the lung fields. In color-mapping images, air spheres were depicted as color defects, however, those behind the diaphragm were not detectable. Smaller size sampling depicted the air spheres as island color defects, while larger ones yielded a limited signal. We confirmed that dynamic chest radiography was able to detect trapped air as regionally-reduced changes in pixel value during respiration. The reduction rate could be defined as a function of residual normal tissue in front and behind air spheres.