Javed I. Khan

Bio: Javed I. Khan is an academic researcher from Kent State University. The author has contributed to research in topics: Transcoding & The Internet. The author has an hindex of 14, co-authored 155 publications receiving 895 citations. Previous affiliations of Javed I. Khan include University of Hawaii at Manoa & Florida State University College of Arts and Sciences.

Eye movement prediction by Kalman filter with integrated linear horizontal oculomotor plant mechanical model

Abstract: The goal of this paper is to predict future horizontal eye movement trajectories within a specified time interval. To achieve this goal a linear horizontal oculomotor plant mechanical model is developed. The model consists of the eye globe and two extraocular muscles: lateral and medial recti. The model accounts for such anatomical properties of the eye as muscle location, elasticity, viscosity, eye-globe rotational inertia, muscle active state tension, length tension and force velocity relationships. The mathematical equations describing the oculomotor plant mechanical model are transformed into a Kalman filter form. Such transformation provides continuous eye movement prediction with a high degree of accuracy. The model was tested with 21 subjects and three multimedia files. Practical application of this model lies with direct eye gaze input and interactive displays systems as a method to compensate for detection, transmission and processing delays.

Eye movement prediction by oculomotor plant Kalman filter with brainstem control

Abstract: Our work addresses one of the core issues related to Human Computer Interaction (HCI) systems that use eye gaze as an input. This issue is the sensor, transmission and other delays that exist in any eye tracker-based system, reducing its performance. A delay effect can be compensated by an accurate prediction of the eye movement trajectories. This paper introduces a mathematical model of the human eye that uses anatomical properties of the Human Visual System to predict eye movement trajectories. The eye mathematical model is transformed into a Kalman filter form to provide continuous eye position signal prediction during all eye movement types. The model presented in this paper uses brainstem control properties employed during transitions between fast (saccade) and slow (fixations, pursuit) eye movements. Results presented in this paper indicate that the proposed eye model in a Kalman filter form improves the accuracy of eye movement prediction and is capable of a real-time performance. In addition to the HCI systems with the direct eye gaze input, the proposed eye model can be immediately applied for a bit-rate/computational reduction in real-time gaze-contingent systems.

Kalman filtering in the design of eye-gaze-guided computer interfaces

Abstract: In this paper, we design an Attention Focus Kalman Filter (AFKF) - a framework that offers interaction capabilities by constructing an eye-movement language, provides real-time perceptual compression through Human Visual System (HVS) modeling, and improves system's reliability. These goals are achieved by an AFKF through identification of basic eye-movement types in real-time, the prediction of a user's perceptual attention focus, and the use of the eye's visual sensitivity function and eye-position data signal de-noising.

Partial prefetch for faster surfing in composite hypermedia

Abstract: In this paper we present a prefetch technique, which incorporates a scheme similar to data streaming to minimize the response-lag. Unlike previous all or none techniques, we propose partial prefetch where the size of the lead segment is computed optimally so that only a minimum but sufficient amount of data is prefetched and buffered. The remaining segment is fetched if and only when the media is traversed. Thus, it delivers content without any increase in perceived response delay, and at the same time drastically minimizes unnecessary pre-load. The paper presents the scheme in the context of surfing in composite multimedia documents. It presents the technique and optimization scheme used for stream segmentation backed by analytical model and statistical simulation. We report remarkable increase in the responsiveness of web systems by a factor of 2-15 based on the specific situation.

A symbol recognition system

Abstract: A hierarchical neural network approach is presented for the automatic conversion of image documents (ACID), which specifically describes a prototype symbol recognition system (SRS) for automatic computer processing of electrical engineering drawings. This approach achieves a significant reduction of human involvement in the symbol model encoding and recognition processes in contrast to such traditional approaches based on thinning, line tracing, and other structural feature extraction techniques. A set of image intensity moments, which are invariant to geometric transformations, is used as features. A hierarchical neural classifier demonstrates faster and more accurate capabilities for model encoding and recognition. The test results from hand-drawn images by using templates achieves a recognition rate of 98.5% on training symbols and 89% on test symbols. >

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …