A New Pupil Detection Algorithm Based on Multiple Angular Integral Projection Functions

2021 ◽  
Author(s):  
Petronela Bonteanu ◽  
Radu Gabriel Bozomitu ◽  
Arcadie Cracan ◽  
Gabriel Bonteanu
Keyword(s):  
Detection Algorithm ◽  
Integral Projection ◽  
Pupil Detection ◽  
Projection Functions

Accuracy and Fairness in Pupil Detection Algorithm

2021 ◽  
Author(s):  
Omkar N. Kulkarni ◽  
Vikram Patil ◽  
Vivek K. Singh ◽  
Pradeep K. Atrey
Keyword(s):  
Detection Algorithm ◽  
Pupil Detection

A new technique for improving pupil detection algorithm

2015 ◽  
Author(s):  
Radu Gabriel Bozomitu ◽  
Vlad Cehan ◽  
Robert Gabriel Lupu ◽  
Cristian Rotariu ◽  
Constantin Barabasa
Keyword(s):  
Detection Algorithm ◽  
New Technique ◽  
Pupil Detection ◽  
A New Technique

Pupil detection algorithm based on RANSAC procedure

2017 ◽  
Author(s):  
Radu Gabriel Bozomitu ◽  
Alexandru Pasarica ◽  
Robert Gabriel Lupu ◽  
Cristian Rotariu ◽  
Eugen Coca
Keyword(s):  
Detection Algorithm ◽  
Pupil Detection

An FPGA-Based Real-Time Human Eye Pupil Detection System Using E2V Smart Camera

2019 ◽  
Vol 16 (2) ◽  
pp. 649-654
Author(s):  
S. Navaneethan ◽  
N. Nandhagopal ◽  
V. Nivedita
Keyword(s):  
Real Time ◽  
Detection System ◽  
Detection Algorithm ◽  
Video Frame ◽  
Smart Camera ◽  
Verilog Hdl ◽  
Real Time System ◽  
Human Eye ◽  
Pupil Detection ◽  
Main Motive

Threshold based pupil detection algorithm was found tobe most efficient method to detect human eye. An implementation of a real-time system on an FPGA board to detect and track a human's eye is the main motive to obtain from proposed work. The Pupil detection algorithm involved thresholding and image filtering. The Pupil location was identified by computing the center value of the detected region. The proposed hardware architecture is designed using Verilog HDL and implemented on aAltera DE2 cyclone II FPGA for prototyping and logic utilizations are compared with Existing work. The overall setup included Cyclone II FPGA, a E2V camera, SDRAM and a VGA monitor. Experimental results proved the accuracy and effectiveness of the hardware realtime implementation as the algorithm was able to manage various types of input video frame. All calculation was performed in real time. Although the system can be furthered improved to obtain better results, overall the project was a success as it enabled any inputted eye to be accurately detected and tracked.

scholarly journals Dummy eye measurements of microsaccades: Testing the influence of system noise and head movements on microsaccade detection in a popular video-based eye tracker

2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Frouke Hermens
Keyword(s):  
Detection Algorithm ◽  
Stimulus Onset ◽  
Head Movements ◽  
Head Motion ◽  
Eye Tracker ◽  
Pupil Detection ◽  
Human Participant ◽  
Detection Mode ◽  
Automated Algorithms ◽  
The Many

Whereas early studies of microsaccades have predominantly relied on custom-built eye trackers and manual tagging of microsaccades, more recent work tends to use video-based eye tracking and automated algorithms for microsaccade detection. While data from these newer studies suggest that microsaccades can be reliably detected with video-based systems, this has not been systematically evaluated. I here present a method and data examining microsaccade detection in an often used video-based system (the Eyelink II system) and a commonly used detection algorithm (Engbert & Kliegl, 2003; Engbert & Mergenthaler, 2006). Recordings from human participants and those obtained using a pair of dummy eyes, mounted on a pair of glasses either worn by a human participant (i.e., with head motion) or a dummy head (no head motion) were compared. Three experiments were conducted. The first experiment suggests that when microsaccade measurements make use of the pupil detection mode, microsaccade detections in the absence of eye movements are sparse in the absence of head movements, but frequent with head movements (despite the use of a chin rest). A second experiment demonstrates that by using measurements that rely on a combination of corneal reflection and pupil detection, false microsaccade detections can be largely avoided as long as a binocular criterion is used. A third experiment examines whether past results may have been affected by possible incorrect detections due to small head movements. It shows that despite the many detections due to head movements, the typical modulation of microsaccade rate after stimulus onset is found only when recording from the participants’ eyes.

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