Fast and robust image processing algorithms based on approximations to order statistics

H. C. Palm

doi:10.1117/12.2304336

30 April 2018 Fast and robust image processing algorithms based on approximations to order statistics

H. C. Palm

Proceedings Volume 10648, Automatic Target Recognition XXVIII; 1064807 (2018) https://doi.org/10.1117/12.2304336
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

A test system with four cameras in the infrared and visual spectra is under development at FFI (The Norwegian Defence Research Establishment). The system can be mounted on a high speed jet aircraft, but may also be used in a land-based version. It can be used for image acquisition as well as for development and test of automatic target recognition (ATR) algorithms. The sensors on board generate large amounts of data, and the scene may be rather cluttered or include anomalies (e.g. sun glare). This means we need image processing and pattern recognition algorithms which are robust, fast (real-time), and able to handle complex scenes. Algorithms based on order statistics are known to be robust and reliable. However, they are in general computationally heavy, and thus often unsuitable for real time applications. But approximations to order statistics do exist. Median of medians is one example. This is a technique where an approximation of the median of a sequence is found by first dividing the sequence in subsequences, and then calculating median (of medians) recursively. The algorithm is very efficient, the processing time is of order O(n). By utilizing such techniques for estimating image statistics, the computational challenge can be overcome. In this paper we present strategies for how approximations to order statistics can be applied for developing robust and fast algorithms for image processing, especially visualization and segmentation.

Citation Download Citation

H. C. Palm "Fast and robust image processing algorithms based on approximations to order statistics", Proc. SPIE 10648, Automatic Target Recognition XXVIII, 1064807 (30 April 2018); https://doi.org/10.1117/12.2304336

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KEYWORDS

Image segmentation

Visualization

Image processing algorithms and systems

Statistical analysis

Image processing

Algorithm development

Infrared imaging

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