An automated calibration method for non-see-through head mounted displays

Accurate calibration of a head mounted display (HMD) is essential both for research on the visual system and for realistic interaction with virtual objects. Yet, existing calibration methods are time consuming and depend on human judgements, making them error prone, and are often limited to optical see-through HMDs. Building on our existing approach to HMD calibration Gilson et al. (2008), we show here how it is possible to calibrate a non-see-through HMD. A camera is placed inside a HMD displaying an image of a regular grid, which is captured by the camera. The HMD is then removed and the camera, which remains fixed in position, is used to capture images of a tracked calibration object in multiple positions. The centroids of the markers on the calibration object are recovered and their locations re-expressed in relation to the HMD grid. This allows established camera calibration techniques to be used to recover estimates of the HMD display's intrinsic parameters (width, height, focal length) and extrinsic parameters (optic centre and orientation of the principal ray). We calibrated a HMD in this manner and report the magnitude of the errors between real image features and reprojected features. Our calibration method produces low reprojection errors without the need for error-prone human judgements

View-based modelling of human visual navigation errors

View-based and Cartesian representations provide rival accounts of visual navigation in humans, and here we explore possible models for the view-based case. A visual “homing” experiment was undertaken by human participants in immersive virtual reality. The distributions of end-point errors on the ground plane differed significantly in shape and extent depending on visual landmark configuration and relative goal location. A model based on simple visual cues captures important characteristics of these distributions. Augmenting visual features to include 3D elements such as stereo and motion parallax result in a set of models that describe the data accurately, demonstrating the effectiveness of a view-based approach

View-based approaches to spatial representation in human vision

In an immersive virtual environment, observers fail to notice the expansion of a room around them and consequently make gross errors when comparing the size of objects. This result is difficult to explain if the visual system continuously generates a 3-D model of the scene based on known baseline information from interocular separation or proprioception as the observer walks. An alternative is that observers use view-based methods to guide their actions and to represent the spatial layout of the scene. In this case, they may have an expectation of the images they will receive but be insensitive to the rate at which images arrive as they walk. We describe the way in which the eye movement strategy of animals simplifies motion processing if their goal is to move towards a desired image and discuss dorsal and ventral stream processing of moving images in that context. Although many questions about view-based approaches to scene representation remain unanswered, the solutions are likely to be highly relevant to understanding biological 3-D vision

No single, stable 3D representation can explain pointing biases in a spatial updating task

People are able to keep track of objects as they navigate through space, even when objects are out of sight. This requires some kind of representation of the scene and of the observer's location but the form this might take is debated. We tested the accuracy and reliability of observers' estimates of the visual direction of previously-viewed targets. Participants viewed 4 objects from one location, with binocular vision and small head movements then, without any further sight of the targets, they walked to another location and pointed towards them. All conditions were tested in an immersive virtual environment and some were also carried out in a real scene. Participants made large, consistent pointing errors that are poorly explained by any stable 3D representation. Any explanation based on a 3D representation would have to posit a different layout of the remembered scene depending on the orientation of the obscuring wall at the moment the participant points. Our data show that the mechanisms for updating visual direction of unseen targets are not based on a stable 3D model of the scene, even a distorted one

On the Two-View Geometry of Unsynchronized Cameras

We present new methods for simultaneously estimating camera geometry and time shift from video sequences from multiple unsynchronized cameras. Algorithms for simultaneous computation of a fundamental matrix or a homography with unknown time shift between images are developed. Our methods use minimal correspondence sets (eight for fundamental matrix and four and a half for homography) and therefore are suitable for robust estimation using RANSAC. Furthermore, we present an iterative algorithm that extends the applicability on sequences which are significantly unsynchronized, finding the correct time shift up to several seconds. We evaluated the methods on synthetic and wide range of real world datasets and the results show a broad applicability to the problem of camera synchronization.Comment: 12 pages, 9 figures, Computer Vision and Pattern Recognition (CVPR) 201

Comparison of view-based and reconstruction-based models of human navigational strategy

There is good evidence that simple animals such as bees use view-based strategies to return to a familiar location but humans could use a 3D reconstruction to achieve the same goal. Assuming some noise in the storage and retrieval process, these two types of strategy give rise to different patterns of predicted errors in homing. We describe an experiment that can help distinguish between these models. Participants wore a head mounted display to carry out a homing task in immersive virtual reality. They viewed three long thin vertical poles and had to remember where they were in relation to the poles before being transported (virtually) to a new location in the scene from where they had to walk back to the original location. The experiment was conducted in both a rich-cue scene (a furnished room) and a sparse scene (no background and no floor or ceiling). As one would expect, in a rich-cue environment the overall error was smaller and in this case the ability to separate the models was reduced. However, for the sparse-cue environment the view-based model outperforms the reconstruction-based model. Specifically, the likelihood of the experimental data is similar to the likelihood of samples drawn from the view-based model (but assessed under both models) while this is not true for samples drawn from the reconstruction-based model

Stable Segmentation of 2D Curves

Institute of Perception, Action and BehaviourThis thesis deals with the specific subproblem of converting sequences of two-dimensional data points into higher level descriptions in terms of curved shape primitives. This is an important member of the wider family of segmentation problems, as rawcurve data are readily available from edge-detection processes, but higher level processes—such as the extraction of geometric invariants or 2D object recognition—require the higher level descriptions. The overriding principle of the work is that if the segmentation process is accurate and repeatable, the later stages of processing are greatly simplified