OpenCV wrapping for Cassandra

Started in:2012
Contact person:Ralf Reulke, Kristian Manthey, Dominik Rueß
Staff involved:Kristian Manthey, Dominik Rueß, Silvio Tristram, Christian Kaptur, Sourabh Bodas, Konstantinos Amplianitis, Kevin Buchwinkler, Malte Müller-Rowold

The company external link Hella Aglaia Mobile Vision GmbH develops and distributes intelligent visual sensor systems. This also includes the software Cassandra, a tool for fast development of (prototypic) image processing algorithms. Cassandra is implemented in C++ and specializes on the automotive area. Cassandra has changed significantly for version 11: improved multi core support, better synchronization of data sources and sinks, realtime requirements can now be applied, since the system won't be cluttered by too much data and many more features have been implemented. Cassandra will also be released in a community edition. This edition allows academic associates and students to rapidly develop and test their own image processing algorithms. The outstanding feature of Cassandra is the lack of need for programming skills. But still, if you know C++, you can extend Cassandra with your own stations.

With the freely available third party library external link OpenCV users have a very powerful image processing tool. This library will be implemented in Cassandra 11. The task of the CV group is to reasonably implement OpenCV functions and classes to Cassandra stations. The problem is the different concept of OpenCV and Cassandra. The latter one makes use of data flow graphs. In contrast, OpenCV requires much more user input and control about parameters and the use of the classes.
The OpenCV modul core has already been ported to Cassandra by Hella Aglaia. The CV group has implemented the modules calib3d, features2d, video analysis and object detection. With the experience and knowledge of our group, we could add meaningful application scenarios and end user tutorials.

With this extension, Cassandra becomes interesting for general rapid image processing prototyping.
The software is available at this external link link.

First input image Second input image

Cassandra Stations

Matches result
Figure 1: Result of a Cassandra input graph. In the first row, two input images are displayed. One row below, a Cassandra graph is displayed. It computes significant points (feature points) in both images, tries to find corresponding points the two of them and displays these matches. The last row shows the result of the computation with the two input images.
To enlarge images, click on them.

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