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Computer Vision

Computer Vision is the most rapidly growing domain of Rhonda's expertise. Since 2007 Rhonda has been doing research and development in this area. As the result of continuous efforts Rhonda released two Audience Measurement products.

Rhonda also offers CV custom-oriented solutions in other domains such as barcodes, tools and pattern recognition.

Rhonda leverages modern CV-methods and mathematical approaches:

  • KDE, Mean shift or Running Gaussian Average methods to extract objects from background (depending on background scene).
  • Color based histograms and Mean Shift for object detection and tracking
  • Viola and Jones method for faces detection
  • Hidden Markov models and Neural Networks for faces recognition
  • RTP (MPEG4) or MJPEG over HTTP for streaming meta-data and video.

Note: Rhonda does not distribute its CV solutions in form of library or SDK. Instead, we prefer integrating our solutions into customer's platforms/SW by ourselves using one of three collaboration models.

19 April 2009  |   Taking snapshots with a moving PTZ camera

This solution uses two cameras – moving PTZ camera and static video camera. Sample application tracks moving people (using color-histogram-based tracker) on a video taken with static camera and targets PTZ camera to one of people for snap shooting. Since PTZ camera positioning takes some time predicting algorithm is used to forecast future position of moving person; it helps targeting PTZ cam more accurately.

18 April 2009  |   Object Recognition (playing card & $10 bill)

This object recognition solution is based on technique of extracting and matching characteristic points of objects with complicated texture. Sample application allows recognizing two types of objects – playing card (queen of hearts) and $10 bill. It's possible to recognize partially overlapped object under various view angles and orientation including partially overlapped objects.

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Do not hesitate to contact us for any question regarding our services. We will be glad to respond in detail.