Natural Features and Image Ratings

An augmentable rating defines how well an image can be detected and tracked using the Vuforia SDK. This rating is displayed in the Target Manager and returned for each uploaded target via the web API.

The augmentable rating can range from 0 to 5 for any given image. The higher the augmentable rating of an image target, the stronger the detection and tracking ability it contains. A rating of zero indicates that a target is not tracked at all by the AR system, whereas a star rating of 5 indicates that an image is easily tracked by the AR system. 

Features

A feature is a sharp, spiked, chiseled detail in the image, such as the ones present in textured objects. The image analyzer represents features as small yellow crosses. Increase the number of these details in your image, and verify that the details create a non-repeating pattern.

A square contains four features for each one of its corners.

A circle contains no features as it contains no sharp or chiseled detail.

This object contains only two features for each sharp corner.
Note: According to the definition of a feature, soft corners and organic edges are not marked as features.

 

Uploaded Image

Analyzed Image

Star Rating

Image with small number of features

Image with high number of features

The augementable rating on the Target Manager hints at the problem:

Uploaded Image

Analyzed Image

Star Rating

Rating:   

Not enough features. More visual details are required to increase the total number of features.

Poor feature distribution. Features are present in some areas of this image but not in others. Features need to be distributed uniformly across the image.

Poor local contrast. The objects in this image need sharper edges or clearly defined shapes in order to provide better local contrast.  

Local contrast

Good or bad local contrast is often difficult to detect with your eye. Improve the contrast of the image in general, or choose an image with details that are more “edged.” Organic shapes, round details, blurred, or highly-compressed images often do not provide enough richness in detail to be detected and tracked properly.

 

Uploaded Image

Detail

Analyzed Image

Star Rating

Original Image

Enhanced Local Contrast

Strong Local Contrast Enhancement

This artwork shows a more practical example of how to improve the local contrast of the target. We use an image with two layers. In the foreground are a few multi-colored leaves. The background is a textured surface. The layers exist only in our graphic editor; when uploading to the Target Manager we always use a flattened image, e.g., PNG format. The uploaded image is 512x512 pixels in size, a little  bigger than the recommended minimum of 320 pixels.

At first sight the original image might have enough detail to function as a target. Unfortunately, uploading it to the Target Manager yields a very low rating of only one star. This results in poor tracking performance. Consecutive improvements allow improving the target quality to a five-star target, yielding superior detection and tracking performance.

Variation

Image

Applied Operation

Star Rating
(out of 5)

1

Original image intended to be used as a target. This image will result in poor quality, because not many features with good contrast can be found.

2

When changing the background of variation 1 to a more contrasting – in this case lighter background – the rating improves, since more contrasting features can be found in the image. Still the rating of 2 is unsatisfactory.

3

Let’s increase the contrast of the features in the foreground from variation 2. For this we increased the contrast of the foreground layer and also pulled down their brightness. With this we’d get an average result and robustness. We can do more.

4

We can further strengthen the features by applying a local contrast enhancement operation to variation 3. For details on this operation, see Local Contrast Enhancement in the Image Target Enhancement Tricks section. Note that to yield the expected tracking result, the printed target must be sharp, and focus must be set correctly in the application at runtime.

5

Another option to increase the local contrast of variation 2 is to further increase foreground/background balance. Here we use a white background. This operation is not always feasible, since it changes your original design. But you might consider this when creating or recommending an initial version.

6

To further improve, we can combine effects. Here we took variation 3 with a foreground that is already enhanced  and replaced the background with white. The total contrast  yielded a superb performance.

7

A different combination is to use the image shown in variation 5 and apply the local contrast enhancement operation as suggested in variation 4. The combined effect is also a five star target.

Feature distribution

The more balanced the distribution of the features in the image, the better the image can be detected and tracked. Verify that the yellow crosses are well-distributed across the entire image. Consider cropping the image to remove any areas without features.

 

Uploaded Image

Analyzed Image

Star Rating

Image features unevenly distributed throughout the target

Cropped image better feature distribution

 

Uploaded Image

Analyzed Image

Star Rating

Rating:  

Poor feature distribution. features are present in some areas of this image but not in others. Features need to be distributed uniformly across the image.

Poor local contrast. The objects in this image need sharper edges or clearly defined shapes to provide better local contrast.

Avoid organic shapes

Typically, organic shapes with soft or round details containing blurred or highly compressed aspects do not provide enough detail to be detected and tracked properly or not at all. They suffer from low feature count.

Uploaded Image

Analyzed Image

Star Rating

Rating:  

There are no features in this image because it lacks visual elements with sharp edges and high contrast. TheAR camera will fail to detect and track images that display these or similar characteristics.

 

Avoid repetitive patterns

Although some images contain enough features and good contrast, repetitive patterns hinder detection performance. For best results, choose an image without repeated motifs (even if rotated and scaled) or strong rotational symmetry. A checkerboard is an example of a repeated pattern that cannot be detected, since the 2x2 pairs of black and white squares look exactly the same and cannot be distinguished by the detector.

 

Uploaded Image

Analyzed Image

Star Rating

Rating: 

This image is not suitable for detection and tracking. You should consider an alternative image or significantly modify this one.

Although this image may contain enough features and good contrast, repetitive patterns hinder detection performance. For best results, choose an image without repeated motifs (even if rotated and scaled) or strong rotational symmetry.