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Automated two-dimensional position measurements with computer vision
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View: Figures


Image of Fig. 1.
Fig. 1.

Image of six colored balls used to illustrate the effectiveness of the searching algorithm. From left to right they are yellow, black, red, orange, blue, and green.

Image of Fig. 2.
Fig. 2.

Original image processed to search for the yellow ball with RGB values of 255, 190, and 0, respectively. In these examples, .

Image of Fig. 3.
Fig. 3.

The original image is processed to search for the red ball using RGB values of 170, 10, and 30, respectively.

Image of Fig. 4.
Fig. 4.

Plot of the figure of merit (FOM) for various RGB color combinations near the color of the red and orange balls. The algorithm uses the original image in Fig. 1 and .

Image of Fig. 5.
Fig. 5.

Sample image of yellow croquette ball pendulum. The fishing line string holding the ball (not readily visible) is approximately long.

Image of Fig. 6.
Fig. 6.

Plot of the and position of the yellow pendulum as a function of time.

Image of Fig. 7.
Fig. 7.

Detected laser spot used in the Cavendish experiment.

Image of Fig. 8.
Fig. 8.

Geometry of the two-dimensional collision with rolling balls. The dashed lines represent the rolling axis as the ball moves. The solid ball is initially stationary.

Image of Fig. 9.
Fig. 9.

Computer observed position of the projectile (ball 1) and the target (ball 2) at various times. The plotted and errors are based on Eq. (4), which can overestimate the uncertainty.

Image of Fig. 10.
Fig. 10.

Projectile (ball 1) position as a function of time. Based on a linear regression, the ball’s initial velocity in the direction is .

Image of Fig. 11.
Fig. 11.

Ball 1 position as a function of time. The slope gives a velocity of .


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Automated two-dimensional position measurements with computer vision