Knowledge Base

Dynamic Subtraction

Aim

To detect the subject, while compensating for temporal changes in the background.

Examples

• Detect a rat in a water maze, where other detection methods do not work, usually because of the effect of waves in the pool.
• Detect a mouse in a PhenoTyper or home cage with bedding material, where the animal's activity (e.g. digging) changes the appearance of the background.
• All cases where lighting changes slowly.

How Dynamic Subtraction Works

Like with Static subtraction, EthoVision XT compares each sampled image with a reference image, with the important difference that the reference image is updated regularly. This compensates for temporal changes in the background. See How the Reference Image Is Updated in Dynamic Subtraction.

Procedure

1. In the Detection Settings pane, click Advanced, then Method. Select Dynamic subtraction.
2. Click the Background button. The Reference Image window opens with the image that is currently used as background. The aim is to obtain a reference image that does not contain images of the animals you want to track. To do so, follow the instructions on the screen in consecutive order. If A fails, move on to B; if that fails, move on to C. See Optimize the Reference Image.
3. From the Subject color list, select one of the options from the list, depending on the color of the subject you want to track.
4. Move the slider next to Bright/Dark to select the range of the contrast between subject and background.
5. Move the slider next to Frame weight or enter the value in the appropriate field to specify how the reference image is updated (range 0–100%). In typical situations, a value between 1–5 gives a good result.

Important: As much of the animal's body as possible must be detected for good tracking. See Advanced Detection Settings: Subject Contour to optimize body detection.

Frame Weight

• Select a low value if you want a large number of past images to contribute to each reference image. As a result, changes in the background are diluted over many images. Choose a low value when the background changes slowly.
• Select a high value if you want a small number of past images to contribute to each reference image. As a result, changes in the background are captured over a short time. Choose a high value when the background changes rapidly, for example when the subject is very active and moves the bedding material around.
• If you select 0 as Frame weight, the reference image is not updated. This is the same as using Static Subtraction.
• If you select 100, each sample gets its own reference image with no contribution from past images. In most cases a Frame weight of 100 does not give good detection, because the subject itself is often removed from the detected image when it moves slowly or sits still.

Tip: To find the optimal Frame weight, set a value and carry out one or more trials. Evaluate whether the tracking was satisfactory. If not, increase or decrease the setting by 20% and try again.

Differencing

Aim

To detect the subject, while compensating for spatial and temporal changes in the background.

How Differencing Works

Like with Dynamic subtraction, the Differencing method updates the reference image over time. Differencing makes a statistical (probabilistic) comparison between each pixel in the reference image and the pixels of the current image. The statistical comparison uses the variance in the contrast between the current and reference image to calculate the probability that each pixel is the subject.

The Differencing method takes more processor load than the subtraction methods. Therefore, when using Differencing, make sure your computer meets the system requirements.

Procedure

1. In the Method section of the Detection Settings window, select Differencing.
2. Click the Background button. The Reference Image window opens with the image that is currently used as background. The aim is to obtain a reference image that does not contain images of the animals you want to track. To do so, follow the instructions on the screen in consecutive order. If A fails, move on to B; if that fails, move on to C. See Optimize the Reference Image.
3. From the Subject color list, select one of the options from the list, depending on the color of the subject you want to track.
4. If necessary, adjust the position of the Sensitivity slider and change the option selected in the Background Changes list.
   • The Sensitivity slider determines what difference in contrast from the background is seen as the animal. For an image with good contrast, there is no need to change the slider. For images with less contrast, adjust the position of the slider to the right or left until the subject is properly detected.
   • In the Background Changes list, select options that reflect how fast the background changes. For example, a cage with bedding might change a lot because of animals kicking around the bedding material. In this case, to prevent changes in the background from interfering with detection, select Medium fast or faster. Usually, Medium slow works just fine.

Important: As much as possible of the animal's body must be detected for good tracking.