The field of view (FOV) in most head-mounted displays (HMDs) is no more than 60 degrees wide -- far narrower than our normal FOV of about 200 wide. This mismatch arises mostly from the difficulty and expense of building wide-FOV HMDs. Restricting a person's FOV, however, has been shown in real environments to affect people's behavior and degrade task performance. Previous work in virtual reality too has shown that restricting FOV to 50 or less in an HMD can degrade performance. I conducted experiments with a custom, wide-FOV HMD and found that performance is degraded even at the relatively high FOV of 112, and further at 48. The experiments used a prototype tiled wide-FOV HMD to measure performance in VR at up to 176 total horizontal FOV, and a custom large-area tracking system to establish new findings on performance while walking about a large virtua...

this paper we ask two questions. Do photoreceptor sensitivity (gain) changes explain the effects of uniform backgrounds on color appearance? If so, how do apparent receptor sensitivities depend on background light? At the turn of the century von Kries hypothesized that the background exerts its influence on appearance by altering photoreceptor sensitivity (von Kries 1905). Color appearance studies since von Kries have not decisively tested his hypothesis. In the earliest studies, several authors rejected receptor sensitivity changes (Burnham, Evans et al. 1952; MacAdam 1956; Wassef 1959). We will argue in the Discussion that their conclusions were undermined by not knowing the human cone spectral sensitivities and by unstable adaptation. In spite of these findings, many authors have assumed that receptor sensitivity changes affect the appearance of targets viewed on uniform backgrounds (Jameson and Hurvich 1972; Walraven 1976; Shevell 1978; Werner and Walraven 1982). Brainard and Wandell (Brainard and Wandell 1992) found evidence for receptor sensitivity control in more complex simulated illumination conditions. These conflicting findings led us to re-

This paper provides a comprehensive analysis of exactly what visual information about the world is embedded within a single image of an eye. It turns out that the cornea of an eye and a camera viewing the eye form a catadioptric imaging system. We refer to this as a corneal imaging system. Unlike a typical catadioptric system, a corneal one is flexible in that the reflector (cornea) is not rigidly attached to the camera. Using a geometric model of the cornea based on anatomical studies, its 3D location and orientation can be estimated from a single image of the eye. Once this is done, a wide-angle view of the environment of the person can be obtained from the image. In addition, we can compute the projection of the environment onto the retina with its center aligned with the gaze direction. This foveated retinal image reveals what the person is looking at. We present a detailed analysis of the characteristics of the corneal imaging system including field of view, resolution and locus of viewpoints. When both eyes of a person are captured in an image, we have a stereo corneal imaging system. We analyze the epipolar geometry of this stereo system and show how it can be used to compute 3D structure. The framework we present in this paper for interpreting eye images is passive and non-invasive. It has direct implications for several fields including visual recognition, human-machine interfaces, computer graphics and human affect studies.

Iris recognition is a relatively new and widely developing technology. The unique and distinct spatial patterns of the iris is used to create a digital signature for person identification. A common problem faced by systems is that of accurate segmentation of the region of interest (ROI). This paper discusses various texture analysis and pattern classification techniques for characterizing the ROI. 1.

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A novel iris segmentation approach for noisy iris is proposed in this paper. The proposed approach comprises of specular reflection removal, pupil localization, iris localization and eyelid localization. Reflection map computation is devised to get the reflection ROI of eye image using adaptive threshold technique. Bilinear interpolation is used to fill these reflection points in the eye image. Variant of edge-based segmentation technique is adopted to detect the pupil boundary from the eye image. Gradient based heuristic approach is devised to detect the iris boundary from the eye image. Eyelid localization is designed to detect the eyelids using the edge detection and curve fitting. Feature sequence combined into spatial domain segments the iris texture patterns properly. Empirical results show that the proposed approach is effective and suitable to deal with the noisy eye image for iris segmentation.

The idea of using the distinct spatial distribution of patterns in the human iris for person authentication is now a widely developing technology. Current systems rely on a set of basic assumptions in order to improve the accuracy and running time of the recognition process. The advent of a robust system implies a viable solution to a number of general problems. This paper focuses on a common yet difficult problem- the segmentation of eyelashes from iris texture. Tests give promising results when using grey level co-occurrence matrix (GLCM) approach.

Experiments with precision eye movement recordings show binocularly unequal saccades to be present under several stimulus conditions having as a common theme ongoing low velocities at the times of the saccades. Simulations using a model of eye muscles and eyeball dynamics reproduce these unequal saccades in quantitative agreement with the experimental findings. The model uses equal innervation for the saccades, and demonstrates a peripheral interaction between the muscle forces and the eye velocities to be the cause of the large inequality of the simulated binocular saccades. Thus, the simulations provide evidence that Hering’s law continues to describe the innervation patterns to corresponding muscles producing these binocularly unequal saccades found in the experimental situation.

Biometric identification systems recognize persons by a digital signature derived from a particular physiological attribute. One such attribute is the unique patterns that exist in the texture of an iris. These patterns provide sufficient information to uniquely identify an individual. Segmentation of the iris texture from an acquired digital image is not always accurate - the image contains noise elements such as skin, reflection and eyelashes that must be located and removed. We compare and contrast four texture description and two hybrid pattern classification methods for segmenting iris texture using a region based pattern classification approach. These techniques are evaluated by analyzing their segmentation accuracy.

In a simplified fashion, the motion of the eyeball in its orbit consists of rotations around a fixed point. Therefore, this motion can be described in terms of the Euler’s angles of rigid body dynamics. However, there is a physiological constraint in the motion of the eye which reduces to two its degrees of freedom, so that one of Euler’s angles is not an independent variable. This paper reviews the basic features of the kinematics of the eye and the laws governing its motion.