Community Research and Development Information Service - CORDIS

PIPS: parallel image processing system

The effort spent in the parallelization of nonlinear image processing algorithms resulted in the implementation of the general purpose parallel image processing system (PIPS). The system is designed for current parallel distributed memory systems and comprises implementations of some hundreds of image processing operations which are fully scalable and portable. Moreover, high efficiencies have been achieved within a broad range of applications. The system is ideally suited to speed up scientific and industrial image processing applications in terms of software development cycles and computational power.

The great variety of parallel distributed memory systems require the use of several abstraction concepts for the system independent software design. The systems differ especially in the layout of the communication facilities between the processing elements. Message passing systems as an abstraction of the physical interconnection scheme offer some portability. In order to achieve scalable and efficient solutions one has to consider the a priori knowledge that is available and include it in the concrete realization of the software. This has been done for a great amount of linear and nonlinear image processing algorithms that form together with the required abstraction layers and infrastructure the general purpose PIPS. Thereby special emphasis was taken for flexible data distribution layout, the minimization of communication demands and data transfer times and the modularity of the system that enables the fast integration of new applications.

The main application areas of the system are: iterative algorithms for image restoration (used for image restoration of the optical distortion of the Hubble Space Telescope); a tomographic analysis of the three-dimensional temperature distribution of approximately rotational symmetric flames; restoration of distorted images with additive noise; extraction of translational and rotational invariant features for object recognition purposes; image segmentation techniques based on watershed algorithms.


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