Real-time analysis of video signals

Many practical and experimental systems employing image processing techniques have been built by other workers for various applications. Most of these systems are computer-based and very few operate in a real time environment. The objective of this work is to build a microprocessor-based system for video image processing. The system is used in conjunction with an on-line TV camera and processing is carried out in real time. The enormous storage requirement of digitized TV signals and the real time constraint suggest that some simplification of the data must take place prior to any viable processing. Data reduction is attained through the representation of objects by their edges, an approach often adopted for feature extraction in pattern recognition systems. A new technique for edge detection by applying comparison criteria to differentials at adjacent pixels of the video image is developed and implemented as a preprocessing hardware unit. A circuit for the generation of the co-ordinates of edge points is constructed to free the processing computer of this task, allowing it more time for on-line analysis of video signals. Besides the edge detector and co-ordinate generator the hardware built consists of a microprocessor system based on a Texas Instruments T.US 9900 device, a first-in-first-out buffer store and interface circuitry to a TV camera and display devices. All hardware modules and their power supplies are assembled in one unit to provide a standalone instrument. The problem chosen for investigation is analysis of motion in a visual scene. Aspects of motion studied concern the tracking of moving objects with simple geometric shapes and description of their motion. More emphasis is paid to the analysis of human eye movements and measurement of its point-of-regard which has many practical applications in the fields of physiology and psychology. This study provides a basis for the design of a processing unit attached to an oculometer to replace bulky minicomputer-based eye motion analysis systems. Programs are written for storage, analysis and display of results in real time

The effect of postoperative keratometry on visual acuity after corneal refractive laser surgery

PURPOSE: To determine if there is a relationship between eyes with flat corneas (as defined by calculated postoperative keratometry values of <38D) undergoing either LASIK (Laser-assisted in Situ Keratomileusis), LASEK (Laser-assisted Subepithelial Keratectomy), or PRK (Photorefractive Keratectomy) corneal refractive surgery and loss of 1 or more lines of postoperative BCVA, and if there is an advantage to undergoing either LASIK or ASA in eyes meeting flat cornea criteria. METHODS: A retrospective analysis of 191 candidate eyes with calculated postoperative keratometry values <38D were identified and matched by manifest refraction and surgery type to 191 control eyes with calculated postoperative keratometry values ≥38D. Both candidate groups and control groups were further stratified into subgroups based on degree of calculated postoperative keratometry. Candidate subgroups: Subgroup 1a (K<35D), Subgroup 2a (K=35-35.99D), Subgroup 3a (K=36-36.99D), and Subgroup 4a (K=37-37.99D). Control subgroups: Subgroup 1b (K=38-38.99D), Subgroup 2b (K=39-39.99D), Subgroup 3b (K=40-40.99D) and Subgroup 4b (K≥41D). All patients had undergone corneal refractive eye surgery procedures LASIK, LASEK, or PRK at Boston Eye Group/Boston Laser in Brookline MA between December 2008 and November 2016. All LASIK flaps were created using the femtosecond laser IntraLase iFS60 Laser (Abbott Medical Optics Inc.). All surface ablation procedures were performed using the excimer lasers VISX STAR S4 IR Excimer Laser System (Abbot Medical Optics Inc.) or WaveLight EX500 Excimer Laser (Alcon Laboratories Inc.). Visual acuity outcomes measuring preoperative and postoperative BCVA and loss of BCVA were recorded as part of the patient’s medical chart and were statistically analyzed to determine correlations. RESULTS: Our data showed no significant differences between overall candidate (K<38D) and control (K≥38D) group mean preoperative BCVA (p<0.23) or mean postoperative BCVA (p<0.13). A total of 15 out of 191 (7.9%) candidate eyes lost 1 or more lines of BCVA in comparison to 23 total control eyes (12.0%) that lost 1 or more lines of BCVA postoperatively. When evaluating subgroup data, Candidate Subgroup 1a (K<35D) showed a significant (p<0.02) decrease in BCVA when compared to other candidate subgroups. Additionally, Control Subgroup 1b (K=38=38.99D) and Control Subgroup 2b (39-39.99D) showed a significant (p<0.001 and p<0.02 respectively) decrease in BCVA compared to other control subgroups. A total of 231 total candidate and control eyes underwent LASIK and a total of 151 total candidate and control eyes underwent ASA. Overall, 17 out of the 231 (7.4%) eyes undergoing LASIK lost BCVA compared to the 21 out of 151 (13.9%) eyes undergoing ASA that lost BCVA which was significant (p<0.04). CONCLUSION: This study did not find evidence to support that the overall flat cornea group (K<38D) lost postoperative BCVA when compared to a control group of eyes with normal keratometry values. However, our data indicated that when the candidate group was stratified by degree of corneal curvature, patients with very flat corneas (K<35D) may be at increased risk of losing BCVA though further studies are needed. Additionally, eyes undergoing ASA may be at increased risk of losing BCVA though further studies are needed.2018-07-11T00:00:00

Models for gaze tracking systems

One of the most confusing aspects that one meets when introducing oneself into gaze tracking technology is the wide variety, in terms of hardware equipment, of available systems that provide solutions to the same matter, that is, determining the point the subject is looking at. The calibration process permits generally adjusting nonintrusive trackers based on quite different hardware and image features to the subject. The negative aspect of this simple procedure is that it permits the system to work properly but at the expense of a lack of control over the intrinsic behavior of the tracker. The objective of the presented article is to overcome this obstacle to explore more deeply the elements of a video-oculographic system, that is, eye, camera, lighting, and so forth, from a purely mathematical and geometrical point of view. The main contribution is to find out the minimum number of hardware elements and image features that are needed to determine the point the subject is looking at. A model has been constructed based on pupil contour and multiple lighting, and successfully tested with real subjects. On the other hand, theoretical aspects of video-oculographic systems have been thoroughly reviewed in order to build a theoretical basis for further studies