Selective rendering for efficient ray traced stereoscopic images

Depth-related visual effects are a key feature of many virtual environments. In stereo-based systems, the depth effect can be produced by delivering frames of disparate image pairs, while in monocular environments, the viewer has to extract this depth information from a single image by examining details such as perspective and shadows. This paper investigates via a number of psychophysical experiments, whether we can reduce computational effort and still achieve perceptually high-quality rendering for stereo imagery. We examined selectively rendering the image pairs by exploiting the fusing capability and depth perception underlying human stereo vision. In ray-tracing-based global illumination systems, a higher image resolution introduces more computation to the rendering process since many more rays need to be traced. We first investigated whether we could utilise the human binocular fusing ability and significantly reduce the resolution of one of the image pairs and yet retain a high perceptual quality under stereo viewing condition. Secondly, we evaluated subjects' performance on a specific visual task that required accurate depth perception. We found that subjects required far fewer rendered depth cues in the stereo viewing environment to perform the task well. Avoiding rendering these detailed cues saved significant computational time. In fact it was possible to achieve a better task performance in the stereo viewing condition at a combined rendering time for the image pairs less than that required for the single monocular image. The outcome of this study suggests that we can produce more efficient stereo images for depth-related visual tasks by selective rendering and exploiting inherent features of human stereo vision

Characteristics of flight simulator visual systems

The physical parameters of the flight simulator visual system that characterize the system and determine its fidelity are identified and defined. The characteristics of visual simulation systems are discussed in terms of the basic categories of spatial, energy, and temporal properties corresponding to the three fundamental quantities of length, mass, and time. Each of these parameters are further addressed in relation to its effect, its appropriate units or descriptors, methods of measurement, and its use or importance to image quality

Stream network analysis and geomorphic flood plain mapping from orbital and suborbital remote sensing imagery application to flood hazard studies in central Texas

The author has identified the following significant results. Development of a quantitative hydrogeomorphic approach to flood hazard evaluation was hindered by (1) problems of resolution and definition of the morphometric parameters which have hydrologic significance, and (2) mechanical difficulties in creating the necessary volume of data for meaningful analysis. Measures of network resolution such as drainage density and basin Shreve magnitude indicated that large scale topographic maps offered greater resolution than small scale suborbital imagery and orbital imagery. The disparity in network resolution capabilities between orbital and suborbital imagery formats depends on factors such as rock type, vegetation, and land use. The problem of morphometric data analysis was approached by developing a computer-assisted method for network analysis. The system allows rapid identification of network properties which can then be related to measures of flood response

Trying to break new ground in aerial archaeology

Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth’s atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research: the bias we impart to the visual record as a result of selective coverage. While the total coverage approach outlined here may not altogether eliminate survey bias, it can vastly increase the amount of useful information captured during a single reconnaissance flight while mitigating the discriminating effects of observer-based, on-the-fly target selection. Furthermore, the information contained in this paper should make it clear that with current technology it is feasible to do so. This can radically alter the basis for aerial prospection and move landscape archaeology forward, beyond the inherently biased patterns that are currently created by airborne archaeological prospection

A study of image quality for radar image processing

Methods developed for image quality metrics are reviewed with focus on basic interpretation or recognition elements including: tone or color; shape; pattern; size; shadow; texture; site; association or context; and resolution. Seven metrics are believed to show promise as a way of characterizing the quality of an image: (1) the dynamic range of intensities in the displayed image; (2) the system signal-to-noise ratio; (3) the system spatial bandwidth or bandpass; (4) the system resolution or acutance; (5) the normalized-mean-square-error as a measure of geometric fidelity; (6) the perceptual mean square error; and (7) the radar threshold quality factor. Selective levels of degradation are being applied to simulated synthetic radar images to test the validity of these metrics

Visual glue

technical reportOne key function of graphics systems is to present information about the 3-D structure of modeled environments. For real-time simulations, conveying a sense of contact between touching surfaces and relative position and motion between proximate objects is particularly critical. Neither stereo nor occlusion cues are completely effective for such fine judgments. Conventional wisdom often argues that shadows play a critical role. Less often, it is argued that interreflection also contributes to the sense that two surfaces are touching. This paper explores the actual utility of shadows and interreflection in signaling contact and suggests how this information can be exploited in real-time rendering systems to glue objects to surfaces

Out of body experiences: a practice-led evaluation of the shifting boundaries shared by analogue films and their digital counterparts

Phd ThesisThis thesis provides in-depth analysis of my practice-led PhD and the methods used to focus on key areas of research - namely exploring the shifting perceptual parameters revealed when analogue films are transferred to digital formats. With this process audio-visual content previously locked inside film’s decaying form is resurrected as immaterial code within a malleable frame. My work utilised this spectral quality to examine different layers of film representation, observing its inner structure, while also stepping back to contemplate its content from a self-reflexive distance. These multiple viewpoints introduced unique spaces within which to study the analogue past from a digital perspective: The filmstrip’s mechanically regulated motion seamlessly combines still images, sound and light into analogue interpretations of space-time. My work digitally desynchronised these elements, revealing the structural gaps between them while also suggesting their merger with a new perceptual model. Moving beyond internal film worlds to the boundaries they share with the physical viewing space, another layer of disjointed separation was introduced by producing screens that struggled to contain film content within their frames. Stepping back further, these screens occupied a space caught between the fixed viewpoint of a cinema and the multiple perspectives allowed by gallerybased installations. The shifting frame of these hybrid spaces created an oscillation between passive submersion within, and analytical distance from mediated worlds. By unmooring and offsetting the precise alignment between film structure, screens and viewing spaces, my practice revealed overlapping edges and disjointed spaces within which media from different eras interacted. This opened up new areas of research that fed directly into my theoretical studies (the thesis layout itself shifts outwards, from media structures to viewing spaces). This approach enabled me to produce a substantial body of work, iii offering an original contribution to this field

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

JUST NOTICEABLE DIFFERENCE SURVEY OF COMPUTER GENERATED IMAGERY USING NORMAL MAPS

Normal maps are widely used as a resource-efficient means of simulating detailed topology on 3D surfaces in the gaming, simulation, and film industries. However, as surface mesh density increases, it is unknown at what level of density these increases become no longer perceivable, and whether normal maps significantly affect this threshold. This study examined at what point participants were unable to discern differences between one level of mesh density and another using an adapted staircase model. Participants identified this threshold for five different organic character models. The averages of each of these thresholds were taken and compared against the results of a control group, which observed the same models without normal maps. The study found that the average threshold for discerning differences in level of detail occurred in the 3,000 to 14,000 polygon range for normal mapped models, and the 240,000 to 950,000 range for the control group. This analysis suggested that normal maps have a significant impact on the viewer\u27s ability to discern differences in detail, and that developing graphics beyond the range of 3,000 to 14,000 polygons is unnecessary for organic character models when normal maps are used