Rhonda Software

Highest quality full cycle software development.

Expert in areas of Computer Vision, Multimedia, Messaging, Networking and others. Focused on embedded software development. Competent in building cross-platform solutions and distributed SW systems.

Offer standalone custom solutions as well as integration of existing products. Opened for outsourcing services.

Visit us at: http://www.rhondasoftware.com

Object recognition (“instruments recognition”)

Posted on : 01-02-2010 | By : Alex | In : Demo, Demo video, Demo videos, YouTube

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tools.avi

This object recognition algorithm is based on own pattern-matching algorithm. The algorithm is able to recognize pre-trained objects which are defined with special set of templates.

USD banknotes recognition

Posted on : 15-12-2009 | By : Yuri Vashchenko | In : Demo, Demo video, Demo videos, OpenCV, YouTube

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The currency recognition demo application works under Windows XP, Intel P4 3GHz. Quality of recognition: 85%. The solution is cross-platform. The application was tested on Linux, ARM11 and on Linux/Windows, Intel Atom.

Barcode recognition (mobile platform)

Posted on : 25-11-2009 | By : Alex | In : Demo, Demo video, Demo videos, YouTube

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Interesting details can be found at: http://www.computer-vision-software.com/blog/2009/10/barcode

FAQ: OpenCV Haartraining

Posted on : 10-11-2009 | By : rhondasw | In : OpenCV

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Hi All, before posting your question, please look at this FAQ carefully! Also you can read OpenCV haartraining article.  If you are sure, there is no answer to your question, feel free to post comment.  Also please, put comments about improvement of this post.  This post will be updated, if needed.

Detect attention, please!

Posted on : 09-11-2009 | By : rhondasw | In : OpenCV

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Nowadays, different audience measurement systems become more and more popular. They are used in active advertising, for gathering statistics, etc. One of the key features of these smart systems is attention detection.  For advertisers, for instance,  it seems very  important to know, how much attention commercial attracts. In this article, I will describe attention detector module, used in our Audience Measurement system.

Object Recognition (Nike logo)

Posted on : 22-10-2009 | By : Alex | In : Demo, Demo video, Demo videos, OpenCV, YouTube

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nike.avi

This is a demo video of the invariant orientation and scale fast object detection algorithm. The algorithm is a robust in cases when the object is deformed a little :-)

Barcode recognition demo

Posted on : 06-10-2009 | By : Ivan Dyukov | In : Demo

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I would like to represent an executable demo  which was described at  http://www.computer-vision-software.com/blog/2009/07/barcode-recognition/.

This is demo application for Rhonda barcode recognition library. It’s cross-platform library written on C++ language. It was tested on ARM Cortex-A8, ARM11 and x86 platforms.

Average recognition time on 640×480 frames:

P4 – 3GHz:  25ms

Audience Measurement (face tracker, gender recognition, attention recognition, etc)

Posted on : 05-10-2009 | By : Alex | In : Demo, Demo video, Demo videos, OpenCV, YouTube

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am-3.avi

This is a demo video of Rhonda Audience Measurement system (MyAudience product, www.MyAudience.com).

Cross-platform solution for getting MJPEG stream from AXIS ip-camera (AXIS 211M)

Posted on : 29-08-2009 | By : Andrew Chen | In : OpenCV

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This paper describes how-to get MJPEG stream from AXIS ip-camera in your C++ application. My approach is a cross-platform solution and much better than solution from http://www.computer-vision-software.com/blog/2009/04/how-to-get-mjpeg-stream-from-axis-ip-cameras-axis-211m-and-axis-214-ptz-as-camera-device-in-opencv-using-directshow/.

Barcode recognition

Posted on : 29-07-2009 | By : Alex | In : Demo, Demo video, Demo videos, YouTube

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barcodes-2.avi

Description

Here you can find a demo of the barcode detection and recognition routine. The current version is set up to detect a barcode labels mostly oriented horizontally and vertically. The routine processes each frame of the video stream and scans it trying to detect a barcode starting position, relying on the appearance specific of the barcode labels. As long as a potential starting position detected the routine applies the set of the image filters to increase the readability of the scanned window. Then recognition algorithm tries both to read and validate the barcode label starting from the detected point. You may see for yourself that such combination of detection and recognition algorithms works pretty well.
This demo works with UPC-A and EAN-13 barcode types.