A novel rat head gaze determination system based on optomotor responses

<div><p>The optomotor response of animals is commonly used to measure their visual performance, e.g., rats of different genetically altered strains or various drug tests. With the presentation of stimuli using computer screens or projectors, the common idea focuses on measuring the eye movement or head and/or body movement to characterize changes of the head gaze. However, traditional methods rely on either the invasive fixation of animals, or the judgment of a human observer who reports the stimulus-tracking movements. In this paper, we propose a novel head gaze determination system to automatically track the head movement of rats without artificial markers. The experiments were done to demonstrate the process of optimizing parameters in image processing. As a result, the head angle curve of the proposed method is consistent with that of ground-truth data annotated manually according to predefined rules. Hence, the proposed method provides a simple, convenient, and objective solution to automatically generate the head gaze orientations from massive amounts of recorded data for further visual performance analysis.</p></div

Curvatures and normals of the points on the contour.

<p>Step length of neighboring points: 16.</p

Optimization of <i>γ</i> for gamma correction.

<p>A: MSE of determination results by the proposed method. B: Standard deviation of determination error. This experiment operates on a dataset with ground truth by manual annotation. It contains 1500 captured images with head rotations over three dimensions of pitch, yaw, and roll. The <i>γ</i> to minimize MSE is 3.3, while the saturation of standard deviation is reached at 2.6. This paper use the value of 3.3 as the optimal <i>γ</i> for following experiments.</p

Determination results of head angle between proposed method and ground truth.

<p>This experiment operates on a dataset with ground truth by manual annotation. It contains 1500 captured images with head rotations over three dimensions of pitch, yaw, and roll. The red curve is the optimal determination results with our proposed method, with MSE of 6.73. The blue curve is the ground truth by manual annotation.</p

Performance with Opening & Erosion versus without Opening & Erosion.

<p>This experiment operates on a dataset with ground truth by manual annotation. It contains 1500 captured images with head rotations over three dimensions of pitch, yaw, and roll. The red curve is the determination error with Opening & Erosion, with MSE of 6.73. The parameters are optimized separately: kernal of 3 × 3 and iteration times of 3 for Opening; kernal of 3 × 3 and iteration times of 2 for Erosion. The green curve is the determination error without Opening & Erosion, with MSE of 14.45.</p

An example of the head gaze determination results.

<p>The red line is the extracted body contour within the ROI. The blue arrow is the extracted head vector with the aid of four supporting points: two symmetrical points on the contour (in blue), their mid-point (in green) and the nose point (in green). All the points, lines, and text in this example image are automatically generated by the implementation program of the proposed method.</p

Illustration of image preprocessing steps.

<p>A: Original image (Resolution: 640 × 480). B: Gamma correction (<i>A</i> = 1.0, <i>γ</i> = 3.3; The highlighted rectangle denotes the manually selected ROI. C: Binarization (threshold detected automatically with Otsu’s method). D: Contour extraction. The red boundary is the extracted contour.</p

Optimization of blurring kernel size.

<p>A: MSE of determination results by the proposed method. B: Standard deviation of determination error. Blurring kernel size here means the side length of the square kernel. This experiment operates on a dataset with ground truth by manual annotation. It contains 1500 captured images with head rotations over three dimensions of pitch, yaw, and roll. The minimum of MSE is reached at 9, while the saturation of standard deviation is reached at 5.</p

An example of sinusoidal grating pattern: Purple and black.

<p>Gratings are designed to move clockwise, anti-clockwise, or stay stationary.</p

Comparison of pixel intensity value distribution on the entire image and ROI.

<p>A: Histogram of the entire image. B: Histogram of the ROI. Since all the images are captured under similar light conditions, this comparison is based on a single frame as an example.</p