Circle Track Analyzer 3.6 [PORTABLE] ➞

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Circle Track Analyzer 3.6

In the moving head condition, the data recording is made while the eye tracker records the movement of the head. The head movement includes not only translational but also angular movements. The eye movement data can be remapped to the original position in time, so that all the recorded data can be used for the correction.

Canny edge detection uses a 2D grayscale-intensity matrix to generate the gradient for each pixel. Thus, the edge strength is expressed by the intensity at each pixel. The Canny edge detection function in OpenCV uses a value between two thresholds, a lower threshold parameter called minVal and an upper threshold called maxVal. The gradient is a measure of the spatial directional change of intensity. Therefore, the smaller the values are for both minVal and maxVal, the stronger the detected circles. However, when the values of minVal and maxVal are small, the returned circles may be too small to be counted accurately and become outliers. Using an accumulator as an adder, this technique can be expressed as follows: when the accumulator value of pixel x and y is positive, then the estimated radius (R) is r:
R2 = 2*r*r
R1 = r
x = R2/2
y = R1/2
r = R1/x – R2/y

The ratio of the maximum and minimum radius determines the resolutions of the detected circles. At least, the resolution of the detected circles must be equal to or greater than the radius of the calibration cross itself. For example, if the radius of the calibration cross is 30 pixels, the ratio of the radius to the accumulated pixels must be at least 30, i.e., the resolution of the detected circles should be at least 30 pixels. Since the resolution of the detected circles is the ratio of their radius to the accumulated pixels, the resolution of the detected circles directly depends on the resolution of the accumulated pixels.

The circles with different sizes can be used for the course retest or training. For example, the subject retest may not have much influence on the calibration of the fixation cross. The course of some patient study may also be covered by a series of repeated retests, such as the FA, PD, DR and RMT training course. In this case, the possible influence of these retests is not taken into account when the parameters of the training course are calculated.
As shown above, the correction parameters can be directly used in the analyzer. In addition, they can be exported to a variety of formats, such as Excel, SPSS, MaxQuant Analysis and pClamp. In the field of pilot and flight simulation training, the correction parameters are mostly used to calculate the screen parameters (e.g., screen, screen center and virtual horizon) for teaching the pilot to fly in a vehicle. The correction parameters calculated using the head movement parameters may be useful to measure the quality of the calibration by comparing the pilot’s performance to published charts of previous training courses.
In this condition, the subject stares at a fixation point in the center of a screen and the movements of the eyes are recorded with an eye tracker during a specified period. This period can be the whole recording period (calibration), or it can be a specified period before and after the calibration period. The calibration period, which is usually 5 to 10 minutes, is usually used for the calibration of the configuration of an eye-tracking device in a laboratory. In the case of continuous recording, the entire recording period is usually used for correction. If the purpose of the continuous recording is for measuring the movement of the eyes, the data can be divided into periods for each data point, e.g., 15 sec for ROI and 5 sec for sampling.
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