Kalman filters are an important technique for building fault-tolerance into a wide range of systems, including real-time imaging. From a software engineering perspective, however, it is not easy to build Kalman filters. Each has to custom designed and most software engineers are not sufficiently grounded in the necessary systems theory to perform this design. The contributions of this paper, therefore, are a set of recipes for implementation of the Kalman filter to a variety of real-time imaging settings, the presentation of a set of object-oriented requirements, and a design for a class of Kalman filters suitable for real-time image processing. First, we describe the Kalman filter and motivate its use as a mechanism for fault-tolerant computing and sensor fusion. Next, the details of using Kalman filters in imaging applications are discussed and several associated algorithms presented. Then, the advantages of using object-oriented specification, design and languages for the implementation of Kalman filters are explored. Finally, we present a specification and design for a class of Kalman filters, which is suitable for coding. This work extends significantly upon that first appearing in 2003 at an SPIE conference (Laplante and Neill, proceedings of the real-time imaging conference, SPIE, Santa Clara, January 2003, pp. 22-29).
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering