Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes the initial pre-processing steps used to follow the motions of the human eye in an eye tracking application. The central method models each pixel as a combination of either: a dark pupil pixel, bright highlight pixel, or a neutral pixel. Portable eye tracking involves tracking a subject's pupil over the course of a study.  describes very preliminary results from using a mixture model as a processing stage. Technical issues of using a mixture model are discussed. The pixel classifications from the mixture model re fed into a naive Bayes pupil tracker. Only low-level information is used for pupil identification. No motion tracking is performed, no belief propagation is performed, and no convolutions are computed. The algorithm is ll positioned for parallel implementations. The solution surmounts several technical challenges, and initial results are unexpectedly accurate. The technique shows good promise for incorporation into a system for automatic eye-to-scene calibration.
['Thomas B. Kinsman', 'Jeff B. Pelz']
I am writing a research paper on a idea Towards a subject-independent adaptive pupil tracker for automatic eye tracking calibration using a mixture model
[{"from": "human", "value": "I am writing a research paper on a idea Towards a subject-independent adaptive pupil tracker for automatic eye tracking calibration using a mixture model"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes the initial pre-processing steps used to follow the motions of the human eye in an eye tracking application. The central method models each pixel as a combination of either: a dark pupil pixel, bright highlight pixel, or a neutral pixel. Portable eye tracking involves tracking a subject's pupil over the course of a study.  describes very preliminary results from using a mixture model as a processing stage. Technical issues of using a mixture model are discussed. The pixel classifications from the mixture model re fed into a naive Bayes pupil tracker. Only low-level information is used for pupil identification. No motion tracking is performed, no belief propagation is performed, and no convolutions are computed. The algorithm is ll positioned for parallel implementations. The solution surmounts several technical challenges, and initial results are unexpectedly accurate. The technique shows good promise for incorporation into a system for automatic eye-to-scene calibration."}]