Eye--Movements

Even during fixation, the eye is rarely still, as miniature eye movements continue to occur within fixational periods of the eye. These miniature movements are referred to as fixational eye movements. Microsaccades are one of the three types of fixational eye movements that have been identified. Microsaccades have been attributed to different visual processes/phenomena such as fixation stability, perceptual fading, and multistable perception. Still, debates surrounding the functional role of microsaccades in vision ensued, as many of the findings from earlier microsaccade reports contradict one another and the polarity in the field caused by these debates led many to believe that microsaccades do not hold a necessary/specialized role in vision. To gain a deeper understanding of microsaccades and its relevance in vision, we sought out to assess the role of microsaccades in bistable motion perception in our behavioral/eye-tracking study. Observers participated in an eye-tracking experiment where they were asked to complete a motion discrimination task while viewing a bistable apparent motion stimuli. The collected eye-tracking data was then used to train a classification model to predict directions of illusory motion perceived by observers. We found that small changes in gaze position during fixation, occurring within or outside microsaccadic events, predicted the direction of motion pattern imposed by the motion stimuli. Our findings suggest that microsaccades and fixational eye movements are correlated with motion perception and that miniature eye movements occurring during fixation may have relevance in vision.

The dual issues of extracting and tracking eye features from video images are addressed in this dissertation. The proposed scheme is different from conventional intrusive eye movement measuring system and can be implemented using an inexpensive personal computer. The desirable features of such a measurement system are low cost, accuracy, automated operation, and non-intrusiveness. An overall scheme is presented for which a new algorithm is forwarded for each of the function blocks in the processing system. A new corner detection algorithm is presented in which the problem of detecting corners is solved by minimizing a cost function. Each cost factor captures a desirable characteristic of the corner using both the gray level information and the geometrical structure of a corner. This approach additionally provides corner orientations and angles along with corner locations. The advantage of the new approach over the existing corner detectors is that it is able to improve the reliability of detection and localization by imposing criteria related to both the gray level data and the corner structure. The extraction of eye features is performed by using an improved method of deformable templates which are geometrically arranged to resemble the expected shape of the eye. The overall energy function is redefined to simplify the minimization process. The weights for the energy terms are selected based on the normalized value of the energy term. Thus the weighting schedule of the modified method does not demand any expert knowledge for the user. Rather than using a sequential procedure, all parameters of the template are changed simultaneously during the minimization process. This reduces not only the processing time but also the probability of the template being trapped in local minima. An efficient algorithm for real-time eye feature tracking from a sequence of eye images is developed in the dissertation. Based on a geometrical model which describes the characteristics of the eye, the measurement equations are formulated to relate suitably selected measurements to the tracking parameters. A discrete Kalman filter is then constructed for the recursive estimation of the eye features, while taking into account the measurement noise. The small processing time allows this tracking algorithm to be used in real-time applications. This tracking algorithm is suitable for an automated, non-intrusive and inexpensive system as the algorithm is capable of measuring the time profiles of the eye movements. The issue of compensating head movements during the tracking of eye movements is also discussed. An appropriate measurement model was established to describe the effects of head movements. Based on this model, a Kalman filter structure was formulated to carry out the compensation. The whole tracking scheme which cascades two Kalman filters is constructed to track the iris movement, while compensating the head movement. The presence of the eye blink is also taken into account and its detection is incorporated into the cascaded tracking scheme. The above algorithms have been integrated to design an automated, non-intrusive and inexpensive system which provides accurate time profile of eye movements tracking from video image frames.

When motion occurs in a scene, the quality of video degrades due to motion smear, which results in a loss of contrast in the image. The characteristics of the human vision system when smooth pursuit eye movements occur are different from those when the eye fixates on an object such as a video screen during motion. Smooth pursuit eye movements dominate in the presence of dynamic stimuli. In the presence of smooth pursuit eye movements, the contrast sensitivity for increasing target velocities shifts toward lower spatial frequencies. The sensitivity for low spatial frequencies during motion is higher than for a stationary case. This dissertation will propose a method to improve the perceptual quality of video using temporal enhancement prefiltering technique based on the characteristics of Smooth Pursuit Eye Movements (SPEM). The resulting technique closely matches the characteristics of the human visual system (HVS). When motion occurs, the eye tracks the moving targets in a scene as opposed to fixating on any portion of the scene. Hence, psychophysical studies of smooth pursuit eye movements were used as a basis to design the temporal filters. Results of experiments show that temporal enhancement results in improved quality by increasing the apparent sharpness of the image sequence. In this dissertation, a study of research describing how motion affects the image quality at the camera lens and the human eye is presented. This dissertation uses that research to develop a temporal enhancement technique to improve the quality of video degraded by motion.