Eye Pupil Detection Research Articles

Pupil Detection System Using Intensity Labeling Algorithm in Field Programmable Gate Array

Pupil detection techniques are an essential diagnostic technique in medical applications. Pupil detection becomes more complex because of the dynamic movement of the pupil region and it’s size. Eye-tracking is either the method of assessing the point of focus (where one sees) or the orientation of an eye relative to the head. An instrument used to control eye positions and eye activity is the eye tracker. As an input tool for human-computer interaction, eye trackers are used in research on the visual system, in psychology, psycholinguistics, marketing, and product design. Eye detection is one in all the applications in the image process. This is very important in human identification and it will improve today’s identification technique that solely involves the eye detection to spot individuals. This technology is still new, only a few domains are applying this technology as their medical system. The proposed work is developing an eye pupil detection method in real-time, stable, using an intensity labeling algorithm. The proposed hardware architecture is designed using the median filter, segmentation using the threshold process, and morphology to detect pupil shape. Finally, an intensity Labeling algorithm is done to locate an exact eye pupil region. A Real-time FPGA implementation is done by Altera Quartus II software with cyclone IV FPGA.

Eye pupil detection system using an ensemble of regression forest and fast radial symmetry transform with a near infrared camera

In this paper, we focus on pupil center detection in various video sequences that include head poses and changes in illumination. To detect the pupil center, we first find four eye landmarks in each eye by using cascade local regression based on a regression forest. Based on the rough location of the pupil, a fast radial symmetric transform is applied using the previously found pupil location to rearrange the fine pupil center. As the final step, the pupil displacement is estimated between the previous frame and the current frame to maintain the level of accuracy against a false locating result occurring in a particular frame. We generated a new face dataset, called Keimyung University pupil detection (KMUPD), with infrared camera. The proposed method was successfully applied to the KMUPD dataset, and the results indicate that its pupil center detection capability is better than that of other methods and with a shorter processing time.

A study on the possibility of implementing a real-time stereoscopic 3D rendering TV system

3D video has recently seen a massive increase in exposure in our lives. However, differences between the viewing and shooting conditions for a film lead to disparities between the reformed media and the original three-dimensional effect, which cause severe visual fatigue to viewers and result in headaches and dizziness. In this paper, a series of image processing algorithms are introduced to overcome these problems. The image processing pipeline is composed of four steps, eye-pupil detection, stereo correspondence computation, saliency map generation, and 3D warping. Each step is implemented in an S3DS-3D rendering system and its time complexity is measured. From the results, it was found that real-time stereoscopic 3D rendering is impossible using only a software implementation because SIFT and optical flow calculation requires a significant amount of time. Therefore, these two algorithm blocks should be implemented with hardware acceleration. Fortunately, active research is being conducted on these issues and real-time processing is expected to become available soon for applications beyond full-HD TV screens. In addition, it was found that saliency map generation and 3D warping blocks also need to be implemented in hardware for full-HD display although they do not have significant time complexity compared to SIFT and optical flow algorithm blocks.

A video-based eye pupil detection system for diagnosing bipolar disorder

Eye pupil detection systems have become increasingly popular in image processing and computer vision applications in medical systems. In this study, a video-based eye pupil detection system is developed for diagnosing bipolar disorder. Bipolar disorder is a condition in which people experience changes in cognitive processes and abilities, including reduced attentional and executive capabilities and impaired memory. In order to detect these abnormal behaviors, a number of neuropsychological tests are also designed to measure attentional and executive abilities. The system acquires the position and radius information of eye pupils in video sequences using an active contour snake model with an ellipse-fitting algorithm. The system also determines the time duration of the eye pupils looking at certain regions and the duration of making decisions during the neuropsychological tests. The tests are applied to 2 different groups consisting of people with bipolar disorder (bipolar group) and people without bipolar disorder (control group) in order to mathematically model the people with bipolar disorder. The mathematical modeling is performed by using the support vector machines method. It is a supervised learning method that analyzes data and recognizes patterns for classification. The developed system acquires data from the being tested and it classifies the person as bipolar or nonbipolar based on the learned mathematical model.

EDCircles: A real-time circle detector with a false detection control

We propose a real-time, parameter-free circle detection algorithm that has high detection rates, produces accurate results and controls the number of false circle detections. The algorithm makes use of the contiguous (connected) set of edge segments produced by our parameter-free edge segment detector, the Edge Drawing Parameter Free (EDPF) algorithm; hence the name EDCircles. The proposed algorithm first computes the edge segments in a given image using EDPF, which are then converted into line segments. The detected line segments are converted into circular arcs, which are joined together using two heuristic algorithms to detect candidate circles and near-circular ellipses. The candidates are finally validated by an a contrario validation step due to the Helmholtz principle, which eliminates false detections leaving only valid circles and near-circular ellipses. We show through experimentation that EDCircles works real-time (10–20ms for 640×480 images), has high detection rates, produces accurate results, and is very suitable for the next generation real-time vision applications including automatic inspection of manufactured products, eye pupil detection, circular traffic sign detection, etc.