How to detect speedily and effectively if the interested moving targets exist in the view field is a problem concerned by many researchers in the field of image processing and targets recognition in the underwater intelligent monitoring system. Underwater environment is very complicated. There are a mass of inorganic material and various organic substances which are different size, shape, and performance. We must extract the interesting targets from complex background. Researchers have proposed many methods, for example M estimation, character matching method and main motion estimation based on light flow. However, these methods have some shortcomings. They are complicated transform, large computation, ineffectively extracting moving targets from complex background. We analyze in detail characteristics of underwater images and select an appropriate threshold level to binarize the difference image of image sequence. Based on the shape of object formed by movement of targets in the difference image, a linear level is defined. The actual sizes of the original moving targets are inferred from linear level and area of the formed objects. Then we segment every part of the binary image into single object in one image by image segmentation algorithm. Finally we can realize speedy detection for moving targets by some characteristics which are convenient to detecting targets under water.
When we use a traditional adaptive line enhancer (ALE) algorithm to detect an underwater moving target, there are two disadvantages
- Ability to suppress colored Gaussian noise is low
- Lower the SNR, worse the performance of the ALE algorithm
In order to greatly overcome these disadvantages, we take full advantage of the capability of higher order cumulants to alleviate the effect of colored Gaussian noise and develop a fourth order cumulant non diagonal slice based adaptive dynamic line enhancer (FOCNDSBADLE) algorithm and fourth order cumulant diagonal slice based adaptive dynamic line enhancer (FOCDSBADLE) algorithm. The adaptive filtering coefficients of these algorithms are indirectly updated by the instantaneous fourth order cumulant slices. It is shown that these slices are comprised of noiseless sinusoids. if the input signals are comprised of sinusoids corrupted by Gaussian noise. Therefore these algorithms are fit to handle highly colored Gaussian noise. Simulation tests are carried out using the measured data radiated by the underwater moving target. Simulation results have shown that the FOCNDSBADLE algorithm and FOCDSBADLE algorithm outperform the ALE algorithm and that the FOCNDSBADLE algorithm outperforms the FOCDSBADLE algorithm in the case of Gaussian noise.