Research contributions in eye tracking, computer vision, and machine learning technologies
Academic papers and conference proceedings in computer vision, deep learning, and human-computer interaction
Algorithm to detect pupil in eye images using a deep neural network with contrastive self-attention mechanisms for improved accuracy in eye gaze tracking applications.
Novel deep learning approach for detecting and matching corneal reflections in eye tracking systems, improving accuracy and robustness of gaze estimation algorithms.
This research describes a head-mounted eye tracking system for a commercial virtual reality (VR) system. The system uses infrared LEDs that illuminate the eyes and cameras to capture eye images and a 3D gaze estimation model that uses the locations pupil center and corneal reflections in the eye images. It generates gaze estimates that are insensitive to headset slippage. A key contribution of the work is a novel method to determine the correspondence between the corneal reflections and the LEDs using a fully convolutional neural network (CNN) based on the UNET architecture, which correctly identifies and matches 91% of reflections in tests. The eye tracking system has an average gaze accuracy of 1.1°, which is at least 100% better than current VR eye tracking systems.
A simpler automated approach to detect diabetic retinopathy taking exudates into account. The method uses homogeneity of healthy areas rather than unhealthy areas, first extracting healthy areas such as blood vessels by entropy thresholding method and optic disc using sobel filter method, then employing thresholding to segment the exudates. The results show that the presented method performs better than previous proposed methods for segmentation of exudates.
A pulse oximeter device that measures pulse rate and blood oxygen levels in a non-invasive way without any need of calibration. The device uses digital computation power of the microcontroller leading to a simpler circuit design. Two Photo-plethysmographic (PPG) signals corresponding to Red and Infrared wavelengths are obtained from the sensor using an Arduino microcontroller, with signal processing carried out using MATLAB. Oxygen levels are determined using Beer Lambert Law while Pulse rate is calculated both in time and frequency domain.
A non-invasive hemoglobin-monitoring device that measures hemoglobin concentration without utilizing a drop of blood. The method uses the principle of photoplethysmography and Beer Lambert law to measure hemoglobin levels. Two PPG signals are obtained by shining lights corresponding to Red and IR wavelengths on the fingertip, and absorbance levels are computed. Path length is determined using the refractive index of hemoglobin and physical distance between source and detector, removing the need for any calibration.
Investigation of various signal processing techniques to improve the accuracy and reliability of calibration-free pulse oximetry systems for medical applications.
Intellectual property in eye tracking, computer vision, human-computer interaction, and healthcare technologies
Interaction methods for agent-based image editing using multi-modal input for enhanced user experience and precise semantic region selection.
Advanced interaction methods for cross-media configuration and virtual keyboard theming in modern computing environments.
Innovative method combining gaze tracking with pen interaction for precise cursor positioning and enhanced user interface control.
Advanced algorithm utilizing multiple cameras and single corneal reflection for precise gaze estimation in eye tracking systems.
Innovative algorithm that uses reflection from only one light source to estimate gaze direction, simplifying eye tracking hardware requirements.
Advanced algorithm using a 3D model approach with no corneal reflection for robust gaze estimation and calibration in eye tracking systems.
Innovative algorithm that predicts renal diseases using digital biomarkers collected from smartwatch devices for early detection and monitoring.
RGB-based eye tracking system that uses the significant eye of a person to determine gaze direction without requiring specialized hardware.
Eye tracking system designed for assisted device applications, enabling enhanced accessibility and interaction for users with disabilities.
Computer vision algorithm to analyze biological sample cards for quality assessment, reducing errors in medical testing and improving diagnostic accuracy.