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
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.
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.
Interesting details can be found at: http://www.computer-vision-software.com/blog/2009/10/barcode
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 
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
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.
OpenCV’s standart cascades allow to detect faces and eyes. I wanted to create cascade in similar way to detect another objects: pringles or plate for example. I found some material in Net how to use OpenCV training tools, also I investigated training tool’s source code myself to found out, what training parameters can be tuned.
For training, I needed thousands of images, containing my object with different lightning conditions and perspectives. After trying to find required number of pictures with Google , I understood, that it’s really difficult task =). So I decided to take video with my object, then I wrote simple program to crop object from video, frame by frame. In such way, I generated about 3000 positive samples (cropped images with my object). Resolution varied from 50×50 to 100×100. The advantage of this method – you get many samples with different reflections, illuminations and backgrounds. It’s very important, that all these images “features” are various!
Description
This is a demo of object recognition technique based on extraction and matching of characteristic points on objects with evident texture. This technique allows recognizing object by various angles and in case of partial occlusions. The demo clip is self-explanatory – obviously it works just fine.
