Adopting multiview pixel mapping for enhancing quality of holoscopic 3D scene in parallax barriers based holoscopic 3D displays

The Autostereoscopic multiview 3D Display is robustly developed and widely available in commercial markets. Excellent improvements are made using pixel mapping techniques and achieved an acceptable 3D resolution with balanced pixel aspect ratio in lens array technology. This paper proposes adopting multiview pixel mapping for enhancing quality constructed holoscopic 3D scene in parallax barriers based holoscopic 3D displays achieving great results. The Holoscopic imaging technology mimics the imaging system of insects, such as the fly, utilizing a single camera, equipped with a large number of micro-lenses, to capture a scene, offering rich parallax information and enhanced 3D feeling without the need of wearing specific eyewear. In addition pixel mapping and holoscopic 3D rendering tools are developed including a custom built holoscopic 3D displays to test the proposed method and carry out a like-to-like comparison.This work has been supported by European Commission under Grant FP7-ICT-2009-4 (3DVIVANT). The authors wish to ex-press their gratitude and thanks for the support given throughout the project

Performance Analysis of a Novel GPU Computation-to-core Mapping Scheme for Robust Facet Image Modeling

Though the GPGPU concept is well-known in image processing, much more work remains to be done to fully exploit GPUs as an alternative computation engine. This paper investigates the computation-to-core mapping strategies to probe the efficiency and scalability of the robust facet image modeling algorithm on GPUs. Our fine-grained computation-to-core mapping scheme shows a significant performance gain over the standard pixel-wise mapping scheme. With in-depth performance comparisons across the two different mapping schemes, we analyze the impact of the level of parallelism on the GPU computation and suggest two principles for optimizing future image processing applications on the GPU platform

Photon counting compressive depth mapping

We demonstrate a compressed sensing, photon counting lidar system based on the single-pixel camera. Our technique recovers both depth and intensity maps from a single under-sampled set of incoherent, linear projections of a scene of interest at ultra-low light levels around 0.5 picowatts. Only two-dimensional reconstructions are required to image a three-dimensional scene. We demonstrate intensity imaging and depth mapping at 256 x 256 pixel transverse resolution with acquisition times as short as 3 seconds. We also show novelty filtering, reconstructing only the difference between two instances of a scene. Finally, we acquire 32 x 32 pixel real-time video for three-dimensional object tracking at 14 frames-per-second.Comment: 16 pages, 8 figure

Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network

Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery.We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool formapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of practical remotely sensed imagery at the sub pixel scale

Co-Registration of Optically Sensed Images and Correlation (COSI-Corr): an Operational Methodology for Ground Deformation Measurements

Recent methodological progress, Co-Registration of Optically Sensed Images and Correlation, outlined here, makes it possible to measure horizontal ground deformation from optical images on an operational basis, using the COSI-Corr software package. In particular, its sub-pixel capabilities allow for accurate mapping of surface ruptures and measurement of co-seismic offsets. We retrieved the fault rupture of the 2005 Mw 7.6 Kashmir earthquake from ASTER images, and we also present a dense mapping of the 1992 Mw 7.3 Landers earthquake of California, from the mosaicking of 30 pairs of aerial images

Characterization of Thin p-on-p Radiation Detectors with Active Edges

Active edge p-on-p silicon pixel detectors with thickness of 100 $\mu$m were fabricated on 150 mm Float zone silicon wafers at VTT. By combining measured results and TCAD simulations, a detailed study of electric field distributions and charge collection performances as a function of applied voltage in a p-on-p detector was carried out. A comparison with the results of a more conventional active edge p-on-n pixel sensor is presented. The results from 3D spatial mapping show that at pixel-to-edge distances less than 100 $\mu$m the sensitive volume is extended to the physical edge of the detector when the applied voltage is above full depletion. The results from a spectroscopic measurement demonstrate a good functionality of the edge pixels. The interpixel isolation above full depletion and the breakdown voltage were found to be equal to the p-on-n sensor while lower charge collection was observed in the p-on-p pixel sensor below 80 V. Simulations indicated this to be partly a result of a more favourable weighting field in the p-on-n sensor and partly of lower hole lifetimes in the p-bulk.Comment: 23 pages, 16 figures, 1 tabl