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Page 1: Wikitude Studio: Guidelines - Best practices with image targets

Best practice with image targets

Page 2: Wikitude Studio: Guidelines - Best practices with image targets

For good results using image recognition and tracking we suggest ...

Page 3: Wikitude Studio: Guidelines - Best practices with image targets

For best results follow these tips:

between 500 to 1000 pixels in each dimension

An aspect ration roughly square

Rich contrast

Evenly distributed textured areas

Many corner like structures

Smaller dimensions than 500 pixels

Larger than 1000 pixels as they do not provide more accurate results

Extreme aspect ratios

Large amounts of text

Many repetitive patterns

Large single-colored areas

Color contrast only e.g. green to red edge)

PREFERRED IMAGES HAVE:

UNSUITABLE IMAGES HAVE:

Page 4: Wikitude Studio: Guidelines - Best practices with image targets

Optimal Image Dimensions

Mock-ups only

Optimal images are sized between 500 and 1000 pixels in each dimension

Small images do not contain enough graphical information to extract so called feature points. The uniqueness, amount and distribution of features points are the key indicators for good detection and tracking quality

Larger images do not improve the tracking quality

Page 5: Wikitude Studio: Guidelines - Best practices with image targets

Squarish aspect ratio

Mock-ups only

Ideal images have an aspect ration around 1:1

Other aspect ratios like 3:4, 2:3 up to 16:9 will perform good as well

Panorama images or other images with extreme aspect ratios won’t deliver the optimal tracking performance

Tip: Try to crop the most prominent squarish part of your image and use only this as target image.

Page 6: Wikitude Studio: Guidelines - Best practices with image targets

Low contrast images

Mock-ups only

Images with high local contrast and large amount of rich textured areas is best suited for reliable detection and tracking

Color contrast only (i.e. green to red edge) appears as high contrast to the human eye but is not discriminative to computer vision algorithms as they are operating on grayscale images

Tip: For low contrast images, try to increase the contrast of your target image with an image editing tool like Gimp or PhotoShop to improve detection and tracking quality

Page 7: Wikitude Studio: Guidelines - Best practices with image targets

Distribution of textured areas

Mock-ups only

Images with evenly distributed textured areas are good candidates for reliable detection and tracking

This might be the hardest part to be in control of and often can’t be changed.

Tip: Try to crop the most prominent part of your image and use only this as target image.

Page 8: Wikitude Studio: Guidelines - Best practices with image targets

Images with whitespace

Single-colored areas or smooth color transitions often found in backgrounds do not exhibit graphical information suitable for detection and tracking.

Tip: Try to crop the most prominent part of your image and use only this as target image.

Page 9: Wikitude Studio: Guidelines - Best practices with image targets

Vector-based graphics

Mock-ups only

Logos and vector-based graphics usually consist of very few areas with high local contrast and textured structures and are therefore hard to detect and track.

Tip: Try to add additional elements to the graphic like your logotype or any other specific elements, which can go along with your graphic.

Page 10: Wikitude Studio: Guidelines - Best practices with image targets

Images with a lot of text

Mock-ups only

Images consisting primarily of large areas of text are hard to detect and track.

Tip: Try to have at least some graphical material and images next to your text for your target image.

Page 11: Wikitude Studio: Guidelines - Best practices with image targets

Repetitive patterns

Mock-ups only

Repetitive patterns exhibit the same graphical information information at each feature point and therefore cannot be localized reliably (first image)

Images with slightly irregular structures can convey a similar information to the target audience while providing enough unique feature points to be detected (second image)

Tip: Try a different selection of your image including non pattern parts or use images with irregular patterns

By Andrew Craigie

Page 12: Wikitude Studio: Guidelines - Best practices with image targets

About the Star rating

The star rating you see when you upload your image is only a first estimation of how well we expect your target to work.

Even images with a low initial rating (like 0 or 1 stars) may work fairly well.

An image does not need 3 stars to work well. A 2‑star rating is already very good and will deliver good results in the most conditions.

Tip: Try out your image even if it gets an initial 0 star rating. Images with a 2-star do not need any more optimization for the most use-cases.

Page 13: Wikitude Studio: Guidelines - Best practices with image targets

Devices with optimal performance

Page 14: Wikitude Studio: Guidelines - Best practices with image targets

Devices with optimal performance

General characteristics of well suited devices• Devices with a dual core CPU and support for NEON instructions• Devices that are under two years old

Android devices like:• Samsung S2, S3, S4• Sony Xperia Z• Google Nexus 4, Galaxy Nexus

iOS devices like:• iPhone 4S, iPhone 5• iPod Touch 5th generation• iPad 2 – 4, iPad mini, iPad Air

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Just contact us!

[email protected]

Any more questions or feedback?


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