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Wikitude Studio: Guidelines - Best practices with image targets

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In this slide deck Wikitude presents the learnings of reviewing thousands of sample images. The guide contains tips and tricks on which images produce the best results when it comes to image recognition.

More info: http://www.wikitude.com/ or contact us at studio@wikitude.com

Veröffentlicht in: Technologie, Kunst & Fotos
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Wikitude Studio: Guidelines - Best practices with image targets

  1. 1. Best practice with image targets
  2. 2. For good results using image recognition and tracking we suggest ...
  3. 3. For best results follow these tips: PREFERRED IMAGES HAVE: UNSUITABLE IMAGES HAVE: between 500 to 1000 pixels in each dimension Smaller dimensions than 500 pixels An aspect ration roughly square Rich contrast Larger than 1000 pixels as they do not provide more accurate results Evenly distributed textured areas Extreme aspect ratios Many corner like structures Large amounts of text Many repetitive patterns Large single-colored areas Color contrast only e.g. green to red edge)
  4. 4. Optimal Image Dimensions 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 Mock-ups only
  5. 5. Squarish aspect ratio 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. Mock-ups only
  6. 6. Low contrast images 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 Mock-ups only
  7. 7. Distribution of textured areas 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. Mock-ups only
  8. 8. 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.
  9. 9. Vector-based graphics 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. Mock-ups only
  10. 10. Images with a lot of text 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. Mock-ups only
  11. 11. Repetitive patterns 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 Mock-ups only
  12. 12. 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 usecases.
  13. 13. Devices with optimal performance
  14. 14. 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
  15. 15. Any more questions or feedback? Just contact us! studio@wikitude.com