Realization of precise depth perception with coarse integral volumetric imaging

In this paper realization of precise depth perception using coarse integral volumetric imaging (CIVI) is discussed. CIVI is a 3D display technology that combines multiview and volumetric solutions by introducing multilayered structure to integral imaging. Since CIVI generates real images optically, optical distortion can cause distortion of 3D space to be presented. To attain presentation of undistorted 3D space with CIVI, the authors simulate the optics of CIVI and propose an algorithm to show undistorted 3D space by compensating the optical distortion on the software basis. The authors also carry out psychophysical experiments to verify that vergence-accommdation conflict is reduced and depth perception of the viewer is improved by combining multiview and volumetric technologies

Coarse integral imaging without pseudo image

Coarse integral imaging (CII), where each elemental lens is large enough to cover pixels far more than the number of views, can show clear floating 3D image when distortion is corrected. One of the major problems left to be solved for CII is suppression of pseudo images that appear around the right image to be presented. In this paper we propose two methods to suppress pseudo images. We first propose use of a lens array with a small F number. When a lens array composed of elemental lenses whose F number is small is set in front of the display panel, pseudo images can be erased by total internal reflection on the outskirt of the large aperture lens because the angle of incidence of the light ray that generates pseudo images becomes larger. The second method we propose is use of a lens array behind the display panel paired with segmented backlight. When convex lenses are set in front of the backlight with limited aperture, leak of ray out to adjacent elemental lenses can be avoided. Since the backlight area is reduced, this method can also reduce consumption of electric power without diminishing brightness of the right image

Holoscopic 3D imaging and display technology: Camera/ processing/ display

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHoloscopic 3D imaging “Integral imaging” was first proposed by Lippmann in 1908. It has become an attractive technique for creating full colour 3D scene that exists in space. It promotes a single camera aperture for recording spatial information of a real scene and it uses a regularly spaced microlens arrays to simulate the principle of Fly’s eye technique, which creates physical duplicates of light field “true 3D-imaging technique”. While stereoscopic and multiview 3D imaging systems which simulate human eye technique are widely available in the commercial market, holoscopic 3D imaging technology is still in the research phase. The aim of this research is to investigate spatial resolution of holoscopic 3D imaging and display technology, which includes holoscopic 3D camera, processing and display. Smart microlens array architecture is proposed that doubles spatial resolution of holoscopic 3D camera horizontally by trading horizontal and vertical resolutions. In particular, it overcomes unbalanced pixel aspect ratio of unidirectional holoscopic 3D images. In addition, omnidirectional holoscopic 3D computer graphics rendering techniques are proposed that simplify the rendering complexity and facilitate holoscopic 3D content generation. Holoscopic 3D image stitching algorithm is proposed that widens overall viewing angle of holoscopic 3D camera aperture and pre-processing of holoscopic 3D image filters are proposed for spatial data alignment and 3D image data processing. In addition, Dynamic hyperlinker tool is developed that offers interactive holoscopic 3D video content search-ability and browse-ability. Novel pixel mapping techniques are proposed that improves spatial resolution and visual definition in space. For instance, 4D-DSPM enhances 3D pixels per inch from 44 3D-PPIs to 176 3D-PPIs horizontally and achieves spatial resolution of 1365 × 384 3D-Pixels whereas the traditional spatial resolution is 341 × 1536 3D-Pixels. In addition distributed pixel mapping is proposed that improves quality of holoscopic 3D scene in space by creating RGB-colour channel elemental images

Modern lithographic techniques applied to stereographic imaging

The main aim of the research has been to produce and evaluate a high-quality diffusion screen to display projected film and television images. The screens have also been found to effectively de-pixelate LCD arrays viewed at a magnification of approximately 4x. The production process relies on the formation of localized refractive index gradients in a photopolymer. The photopolymer, specially formulated and supplied by Du Pont, is exposed to actinic light through a precision contact mask to initiate polymerization within the exposed areas. As polymerization proceeds, a monomer concentration gradient exists between the exposed and unexposed regions allowing the monomer molecules to diffuse. Since the longer polymer chains do not diffuse as readily, the molecular concentration of the material, which is related to its refractive index, is then no longer uniform. The generation of this refractive index profile can, to some extent, be controlled by careful exposure of the photopolymer through the correct mask so that the resulting diffusion screen can be tailored to suit specific viewing requirements. [Continues.

Panoramic, large-screen, 3-D flight display system design

The report documents and summarizes the results of the required evaluations specified in the SOW and the design specifications for the selected display system hardware. Also included are the proposed development plan and schedule as well as the estimated rough order of magnitude (ROM) cost to design, fabricate, and demonstrate a flyable prototype research flight display system. The thrust of the effort was development of a complete understanding of the user/system requirements for a panoramic, collimated, 3-D flyable avionic display system and the translation of the requirements into an acceptable system design for fabrication and demonstration of a prototype display in the early 1997 time frame. Eleven display system design concepts were presented to NASA LaRC during the program, one of which was down-selected to a preferred display system concept. A set of preliminary display requirements was formulated. The state of the art in image source technology, 3-D methods, collimation methods, and interaction methods for a panoramic, 3-D flight display system were reviewed in depth and evaluated. Display technology improvements and risk reductions associated with maturity of the technologies for the preferred display system design concept were identified

Depth measurement in integral images.

The development of a satisfactory the three-dimensional image system is a constant pursuit of the scientific community and entertainment industry. Among the many different methods of producing three-dimensional images, integral imaging is a technique that is capable of creating and encoding a true volume spatial optical model of the object scene in the form of a planar intensity distribution by using unique optical components. The generation of depth maps from three-dimensional integral images is of major importance for modern electronic display systems to enable content-based interactive manipulation and content-based image coding. The aim of this work is to address the particular issue of analyzing integral images in order to extract depth information from the planar recorded integral image. To develop a way of extracting depth information from the integral image, the unique characteristics of the three-dimensional integral image data have been analyzed and the high correlation existing between the pixels at one microlens pitch distance interval has been discovered. A new method of extracting depth information from viewpoint image extraction is developed. The viewpoint image is formed by sampling pixels at the same local position under different micro-lenses. Each viewpoint image is a two-dimensional parallel projection of the three-dimensional scene. Through geometrically analyzing the integral recording process, a depth equation is derived which describes the mathematic relationship between object depth and the corresponding viewpoint images displacement. With the depth equation, depth estimation is then converted to the task of disparity analysis. A correlation-based block matching approach is chosen to find the disparity among viewpoint images. To improve the performance of the depth estimation from the extracted viewpoint images, a modified multi-baseline algorithm is developed, followed by a neighborhood constraint and relaxation technique to improve the disparity analysis. To deal with the homogenous region and object border where the correct depth estimation is almost impossible from disparity analysis, two techniques, viz. Feature Block Pre-selection and “Consistency Post-screening, are further used. The final depth maps generated from the available integral image data have achieved very good visual effects

Holoscopic 3D image depth estimation and segmentation techniques

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonToday’s 3D imaging techniques offer significant benefits over conventional 2D imaging techniques. The presence of natural depth information in the scene affords the observer an overall improved sense of reality and naturalness. A variety of systems attempting to reach this goal have been designed by many independent research groups, such as stereoscopic and auto-stereoscopic systems. Though the images displayed by such systems tend to cause eye strain, fatigue and headaches after prolonged viewing as users are required to focus on the screen plane/accommodation to converge their eyes to a point in space in a different plane/convergence. Holoscopy is a 3D technology that targets overcoming the above limitations of current 3D technology and was recently developed at Brunel University. This work is part W4.1 of the 3D VIVANT project that is funded by the EU under the ICT program and coordinated by Dr. Aman Aggoun at Brunel University, West London, UK. The objective of the work described in this thesis is to develop estimation and segmentation techniques that are capable of estimating precise 3D depth, and are applicable for holoscopic 3D imaging system. Particular emphasis is given to the task of automatic techniques i.e. favours algorithms with broad generalisation abilities, as no constraints are placed on the setting. Algorithms that provide invariance to most appearance based variation of objects in the scene (e.g. viewpoint changes, deformable objects, presence of noise and changes in lighting). Moreover, have the ability to estimate depth information from both types of holoscopic 3D images i.e. Unidirectional and Omni-directional which gives horizontal parallax and full parallax (vertical and horizontal), respectively. The main aim of this research is to develop 3D depth estimation and 3D image segmentation techniques with great precision. In particular, emphasis on automation of thresholding techniques and cues identifications for development of robust algorithms. A method for depth-through-disparity feature analysis has been built based on the existing correlation between the pixels at a one micro-lens pitch which has been exploited to extract the viewpoint images (VPIs). The corresponding displacement among the VPIs has been exploited to estimate the depth information map via setting and extracting reliable sets of local features. ii Feature-based-point and feature-based-edge are two novel automatic thresholding techniques for detecting and extracting features that have been used in this approach. These techniques offer a solution to the problem of setting and extracting reliable features automatically to improve the performance of the depth estimation related to the generalizations, speed and quality. Due to the resolution limitation of the extracted VPIs, obtaining an accurate 3D depth map is challenging. Therefore, sub-pixel shift and integration is a novel interpolation technique that has been used in this approach to generate super-resolution VPIs. By shift and integration of a set of up-sampled low resolution VPIs, the new information contained in each viewpoint is exploited to obtain a super resolution VPI. This produces a high resolution perspective VPI with wide Field Of View (FOV). This means that the holoscopic 3D image system can be converted into a multi-view 3D image pixel format. Both depth accuracy and a fast execution time have been achieved that improved the 3D depth map. For a 3D object to be recognized the related foreground regions and depth information map needs to be identified. Two novel unsupervised segmentation methods that generate interactive depth maps from single viewpoint segmentation were developed. Both techniques offer new improvements over the existing methods due to their simple use and being fully automatic; therefore, producing the 3D depth interactive map without human interaction. The final contribution is a performance evaluation, to provide an equitable measurement for the extent of the success of the proposed techniques for foreground object segmentation, 3D depth interactive map creation and the generation of 2D super-resolution viewpoint techniques. The no-reference image quality assessment metrics and their correlation with the human perception of quality are used with the help of human participants in a subjective manner

Size and Shape Determination of Riprap and Large-sized Aggregates Using Field Imaging

Riprap rock and large-sized aggregates are extensively used in transportation, geotechnical, and hydraulic engineering applications. Traditional methods for assessing riprap categories based on particle weight may involve subjective visual inspection and time-consuming manual measurements. Aggregate imaging and segmentation techniques can efficiently characterize riprap particles for their size and morphological/shape properties to estimate particle weights. Particle size and morphological/shape characterization ensure the reliable and sustainable use of all aggregate skeleton materials at quarry production lines and construction sites. Aggregate imaging systems developed to date for size and shape characterization, however, have primarily focused on measurement of separated or non-overlapping aggregate particles. This research study presents an innovative approach for automated segmentation and morphological analyses of stockpile aggregate images based on deep-learning techniques. As a project outcome, a portable, deployable, and affordable field-imaging system is envisioned to estimate volumes of individual riprap rocks for field evaluation. A state-of-the-art object detection and segmentation framework is used to train an image-segmentation kernel from manually labeled 2D riprap images in order to facilitate automatic and user-independent segmentation of stockpile aggregate images. The segmentation results show good agreement with ground-truth validation, which entailed comparing the manual labeling to the automatically segmented images. A significant improvement to the efficiency of size and morphological analyses conducted on densely stacked and overlapping particle images is achieved. The algorithms are integrated into a software application with a user-friendly Graphical User Interface (GUI) for ease of operation. Based on the findings of this study, this stockpile aggregate image analysis program promises to become an efficient and innovative application for field-scale and in-place evaluations of aggregate materials. The innovative imaging-based system is envisioned to provide convenient, reliable, and sustainable solutions for the on-site quality assurance/quality control (QA/QC) tasks related to riprap rock and large-sized aggregate material characterization and classification.IDOT-R27-182Ope

Real-time GPU-accelerated Out-of-Core Rendering and Light-field Display Visualization for Improved Massive Volume Understanding

Nowadays huge digital models are becoming increasingly available for a number of different applications ranging from CAD, industrial design to medicine and natural sciences. Particularly, in the field of medicine, data acquisition devices such as MRI or CT scanners routinely produce huge volumetric datasets. Currently, these datasets can easily reach dimensions of 1024^3 voxels and datasets larger than that are not uncommon. This thesis focuses on efficient methods for the interactive exploration of such large volumes using direct volume visualization techniques on commodity platforms. To reach this goal specialized multi-resolution structures and algorithms, which are able to directly render volumes of potentially unlimited size are introduced. The developed techniques are output sensitive and their rendering costs depend only on the complexity of the generated images and not on the complexity of the input datasets. The advanced characteristics of modern GPGPU architectures are exploited and combined with an out-of-core framework in order to provide a more flexible, scalable and efficient implementation of these algorithms and data structures on single GPUs and GPU clusters. To improve visual perception and understanding, the use of novel 3D display technology based on a light-field approach is introduced. This kind of device allows multiple naked-eye users to perceive virtual objects floating inside the display workspace, exploiting the stereo and horizontal parallax. A set of specialized and interactive illustrative techniques capable of providing different contextual information in different areas of the display, as well as an out-of-core CUDA based ray-casting engine with a number of improvements over current GPU volume ray-casters are both reported. The possibilities of the system are demonstrated by the multi-user interactive exploration of 64-GVoxel datasets on a 35-MPixel light-field display driven by a cluster of PCs. ------------------------------------------------------------------------------------------------------ Negli ultimi anni si sta verificando una proliferazione sempre più consistente di modelli digitali di notevoli dimensioni in campi applicativi che variano dal CAD e la progettazione industriale alla medicina e le scienze naturali. In modo particolare, nel settore della medicina, le apparecchiature di acquisizione dei dati come RM o TAC producono comunemente dei dataset volumetrici di grosse dimensioni. Questi dataset possono facilmente raggiungere taglie dell’ordine di 10243 voxels e dataset di dimensioni maggiori possono essere frequenti. Questa tesi si focalizza su metodi efficienti per l’esplorazione di tali grossi volumi utilizzando tecniche di visualizzazione diretta su piattaforme HW di diffusione di massa. Per raggiungere tale obiettivo si introducono strutture specializzate multi-risoluzione e algoritmi in grado di visualizzare volumi di dimensioni potenzialmente infinite. Le tecniche sviluppate sono “ouput sensitive” e la loro complessità di rendering dipende soltanto dalle dimensioni delle immagini generate e non dalle dimensioni dei dataset di input. Le caratteristiche avanzate delle architetture moderne GPGPU vengono inoltre sfruttate e combinate con un framework “out-of-core” in modo da offrire una implementazione di questi algoritmi e strutture dati più flessibile, scalabile ed efficiente su singole GPU o cluster di GPU. Per migliorare la percezione visiva e la comprensione dei dati, viene introdotto inoltre l’uso di tecnologie di display 3D di nuova generazione basate su un approccio di tipo light-field. Questi tipi di dispositivi consentono a diversi utenti di percepire ad occhio nudo oggetti che galleggiano all’interno dello spazio di lavoro del display, sfruttando lo stereo e la parallasse orizzontale. Si descrivono infine un insieme di tecniche illustrative interattive in grado di fornire diverse informazioni contestuali in diverse zone del display, così come un motore di “ray-casting out-of-core” basato su CUDA e contenente una serie di miglioramenti rispetto agli attuali metodi GPU di “ray-casting” di volumi. Le possibilità del sistema sono dimostrate attraverso l’esplorazione interattiva di dataset di 64-GVoxel su un display di tipo light-field da 35-MPixel pilotato da un cluster di PC