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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.

22 October 2009  |   Object Recognition (Nike logo)

This highly tailored solution works with only one pattern; it recognizes Nike logos of any sizes and colors. It's possible to recognize any number of Nike logos in any orientations at the same time, as well as to detect and recognize Nike logo under specific conditions – partially blocked logos and logos placed of crumpled surfaces like paper or fabric.

05 October 2009  |   Audience Measurement

This audience measurement solution measures demographics, impression, opportunity to see, vehicle traffic, dwell time and other standard audience measurement metrics. Sample application marks male faces with blue rectangles and female faces with red rectangles distinguishing those visitors who are actually looking at camera (bright color of rectangular for attention) and visitors not looking at camera but with visible faces (pale color for presence).

29 July 2009  |   Barcode recognition

This barcode recognition solution works with one-dimensional barcodes of any size and orientation including partially blocked barcodes.

13 May 2009  |   Tracking overlapping objects

This solution illustrates color-histogram-based object tracker in action. Sample application tracks people as moving blobs (“clouds” of moving pixels) and uniquely identifies them. When two or more blobs are overlapped, sample application merges them into one combined blob and marks it with IDs of all objects included. When one of objects separates from this blob sample application recognizes which one is out and re-arranges ID appropriately. This approach works pretty well in case of characteristic histograms.

28 April 2009  |   Object Detection (barcodes)

This is the demo of the jerry-built algorithm that finds barcode plates using Hough transform. Actually the video is mostly speaking for itself.

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