Difference between revisions of "Pupil tracking"
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*Issues: The hairline on the forehead and the lips are often large peaks, so these must be taken into account. | *Issues: The hairline on the forehead and the lips are often large peaks, so these must be taken into account. | ||
| − | Vertical Projection | + | ==== Vertical Projection ==== |
*Goal: Find the peaks near the edges of the face to define the left and right parts of the region. | *Goal: Find the peaks near the edges of the face to define the left and right parts of the region. | ||
*Issues: This is more consistent although making sure that the peaks are at least a certain distance apart adds consistency. | *Issues: This is more consistent although making sure that the peaks are at least a certain distance apart adds consistency. | ||
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| + | === Homomorphic Filtering === | ||
| + | |||
| + | Homomorphic filtering performs filtering on an image by simultaneously normalizing the brightness and creating more contrast. This is accomplished using a high-pass filter on the natural log of the image. | ||
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| + | |||
| + | For the eye tracking problem, the homomorphic filter is able to separate the useful information of the image from the artifacts created by non-uniform lighting conditions. The filtering removes the artifacts, leaving a clearer picture of the eye region. | ||
Revision as of 18:04, 17 December 2012
Contents
Background
- This research was conducted by Tommy Powers and Cameron Fleming in the Spring Semester and Fall Semester of 2012 at Washington University in Saint Louis. It was part of the Undergraduate Research Program in the Preston M. Green Department of Electrical and Systems Engineering. This project was overseen by Dr. Arye Nehorai, Ed Richter, and Phani Chavali.
Project Overview
- The goal of this pupil tracking project is to provide a method of communication using eye movement for people with Amyotrophic Lateral Sclerosis (ALS) who are unable to control voluntary muscle movement in their limbs. The project will be developed in two main phases:
- Develop an algorithm to track the movement of the pupils.
- Develop hardware in order to capture the movement of the eyes so that it may then be processed.
Our work focused on the first task in order to construct a robust pupil finding algorithm. We tried a variety of ways to track the pupils and in this report we focus on the most robust of these.
Eye Tracking Steps
The eye tracking algorithm uses the following detailed steps:
- Eye Region Extraction
- Homomorphic Filtering
- Eye Tracking
- Kalman Filtering
Eye Region Extraction
This portion uses the horizontal and vertical projections of the gradient to find the biggest changes in intensity. These changes correspond to edges in the image.
Horizontal Projection
- Goal: Use peaks near the eyebrows and the bottom of the eye to define the top and bottom of the region.
- Issues: The hairline on the forehead and the lips are often large peaks, so these must be taken into account.
Vertical Projection
- Goal: Find the peaks near the edges of the face to define the left and right parts of the region.
- Issues: This is more consistent although making sure that the peaks are at least a certain distance apart adds consistency.
Homomorphic Filtering
Homomorphic filtering performs filtering on an image by simultaneously normalizing the brightness and creating more contrast. This is accomplished using a high-pass filter on the natural log of the image.
For the eye tracking problem, the homomorphic filter is able to separate the useful information of the image from the artifacts created by non-uniform lighting conditions. The filtering removes the artifacts, leaving a clearer picture of the eye region.
