1 / 17

Mobile feature-cloud panorama construction for image recognition applications

Mobile feature-cloud panorama construction for image recognition applications Miguel Bordallo, Jari Hannuksela, Olli silvén Machine Vision Group University of Oulu. Contents. Introduction Image recognition applications Comparison of image-based context retrieval methods

kaden
Download Presentation

Mobile feature-cloud panorama construction for image recognition applications

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Mobile feature-cloudpanoramaconstruction for imagerecognitionapplications Miguel Bordallo, Jari Hannuksela, Olli silvén Machine Vision Group University of Oulu

  2. Contents • Introduction • Image recognition applications • Comparison of image-based context retrieval methods • Context retrieval from video analysis • System design • Application flow • Automatic start • Image registration • Moving-objects detection • Quality assesment • Performance analysis • Conclusions

  3. Image-based context retrieval applications • Point Your Camera to an object (landmark, poster) • Take a Picture • Get context information and display it

  4. Mobile context retrieval applications Snaptell Google googles Kooaba Nokia Point & Find

  5. Image recognition approaches • Transmission of compressed still image • Needs lots of storage in server • Image size implies large amount of data transmitted • Compression artifacts diminish quality • Features extracted from still images • Amount of features needed not know beforehand • No feedback. Re-takes needed often • Two dimensional representation • Feature-cloud extracted from video frames • Videos contain lots of information • Most of it redundant • Image registration is easy • Smaller motions between frames • some frames can be discarded without losing information • Videos can capture wide angle scenes. • 3D world is better represented

  6. Still image vs. Video based

  7. Constructing a feature-cloud Frame #1 Frame #16 Frame #31 Frame #46 Frame #61

  8. System design

  9. Application flow (client side)

  10. Automatic start of the application • Recognizing characteristic motion patterns • Holding phone like a camera • Panning back and forth • Reduces perceived latencies

  11. Interactive capture When frame is suitable for recognition (high quality), the user receives feedback and instructions VGA video analysis Motion estimation system calculates shift, rotation and scale in real time

  12. Feature extraction & Image registration • Feature extraction based on CHoG features • Compressed Histogram of Gradients • Block Matching • Best Linear Unbiased Estimator • Compute registration parameters in real time to send to the server: • Shift, rotation and change of scale

  13. Moving-objects detection Object-detection ON Object-detection OFF

  14. Moving-objects detection Object-detection ON Object-detection OFF The features corresponding to a moving object are not sent to the server Not-valid features are transmitted to the server

  15. Quality assesment Server receives only the features corresponding to high quality frames

  16. Performance comparison

  17. Summary • Improve results in 3 dimensional environments • Interactivity • Detection of moving objects • Image quality assesment • Bigger field of view • Reduce the communications need between clients and server • Bandwidth reduction • Reduce the workload of the servers

More Related