Lightness, Brightness, and Transparency in Optical See-Through Augmented Reality

Augmented reality (AR), as a key component of the future metaverse, has leaped from the research labs to the consumer and enterprise markets. AR optical see-through (OST) devices utilize transparent optical combiners to provide visibility of the real environment as well as superimpose virtual content on top of it. OST displays distinct from existing media because of their optical additivity, meaning the light reaching the eyes is composed of both virtual content and real background. The composition results in the intended virtual colors being distorted and perceived transparent. When the luminance of the virtual content decreases, the perceived lightness and brightness decrease, and the perceived transparency increases. Lightness, brightness, and transparency are modulated by one physical dimension (luminance), and all interact with the background and each other. In this research, we aim to identify and quantify the three perceptual dimensions, as well as build mathematical models to predict them. In the first part of the study, we focused on the perceived brightness and lightness with two experiments: a brightness partition scaling experiment to build brightness scales, and a diffuse white adjustment experiment to determine the absolute luminance level required for diffuse white appearances on 2D and 3D AR stimuli. The second part of the research targeted at the perceived transparency in the AR environment with three experiments. The transparency was modulated by the background Michelson contrast reduction in either average luminance or peak-to-peak luminance difference to investigate, and later illustrated, the fundamental mechanism evoking transparency perception. The first experiment measured the transparency detection thresholds and confirmed that contrast sensitivity functions with contrast adaptation could model the thresholds. Subsequently, the transparency perception was investigated through direct anchored scaling experiment by building perceived transparency scales from the virtual content contrast ratio to the background. A contrast-ratio-based model was proposed predicting the perceived transparency scales. Finally, the transparency equivalency experiment between the two types of contrast modulation confirmed the mechanism difference and validated the proposed model

Visual analytics of multidimensional time-dependent trails:with applications in shape tracking

Lots of data collected for both scientific and non-scientific purposes have similar characteristics: changing over time with many different properties. For example, consider the trajectory of an airplane travelling from one location to the other. Not only does the airplane itself move over time, but its heading, height and speed are changing at the same time. During this research, we investigated different ways to collect and visualze data with these characteristics. One practical application being for an automated milking device which needs to be able to determine the position of a cow's teats. By visualizing all data which is generated during the tracking process we can acquire insights in the working of the tracking system and identify possibilites for improvement which should lead to better recognition of the teats by the machine. Another important result of the research is a method which can be used to efficiently process a large amount of trajectory data and visualize this in a simplified manner. This has lead to a system which can be used to show the movement of all airplanes around the world for a period of multiple weeks

NASA Tech Briefs, September 1992

Topics include: Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

A Multiple-Systems Approach in the Symbolic Modelling of Human Vision

For most of the thirty years or so of machine vision research, activity has been concentrated mainly in the domain of metric-based approaches: there has been negligible attention to the psychological factors in human vision. With the recent resurgence of interest in neural systems, that is now changing. This thesis discusses relevant aspects of basic visual neuroanatomy, and psychological phenomena, in an attempt to relate the concepts to a model of human vision and the prospective goals of future machine vision systems. It is suggested that, while biological vision is complex, the underlying mechanisms of human vision are more tractable than is often believed. We also argue here that the controversial subject of direct vision plays a crucial role in natural vision, and we attempt to relate this to the model. The recognition of massive parallelism in natural vision has led to proposals for emulating aspects of neural networks in technology. The systems model developed in this work demonstrates software-simulated cellular automata (CAs) in the role of mainly low-level image processing. It is shown that CAs are able to efficiently provide both conventional and neurally-inspired vision functions. The thesis also discusses the use of Prolog as the means of realising higher level image understanding. The symbolic processing developed is basic, but is nevertheless sufficient for the purposes of the present. demonstrations. Extensions to the concepts can be easily achieved. The modular systems approach adopted blends together several ideas and processes, and results in a more robust model of human vision that is able to translate a noisy real image into an accessible symbolic form for expert-domain interpretation