Proceedings Article | 1 September 1995
Proc. SPIE. 2561, Signal and Data Processing of Small Targets 1995
KEYWORDS: Infrared imaging, Edge detection, Environmental monitoring, Statistical analysis, Detection and tracking algorithms, Data modeling, Sensors, Image processing, Signal processing, Algorithm development
In signal processing applications it is common to assume a Gaussian problem in the design of optimal signal processors. However, non-Gaussian processes do arise in many situations. When the possibility of a non-Gaussian problem is encountered, the question as to which probability distributions should be utilized in a specific situation for modeling the data needs to be answered. In practice, the underlying probability distributions are not known a priori. Consequently, an assessment must be made by monitoring the environment to subdivide for each patch. In this paper, an automatic statistical characterization and partitioning of environments process, previously used on simulated data, is applied to real data of an IR image. Two separate procedures are used to determine all homogeneous patches and subpatches in the IR image. The first procedure, referred to as the mapping procedure, is used to separate contiguous homogeneous regions by segregating between their power levels. The second procedure, referred to as the statistical procedure, separates contiguous homogeneous regions by segregating between their probabilistic data distributions. The latter procedure makes use of Ozturk algorithm, a newly developed algorithm for analyzing random data. Furthermore, the statistical procedure identifies suitable approximations to the probability density function for each homogeneous patch and determines the location of outliers. Convergence of the procedures is controlled by an expert system shell.
