INVESTIGATION OF FILTERING AND OBJECTS DETECTION ALGORITHMS FOR A MULTIZONE IMAGE SEQUENCE

The problem of detecting objects on a sequence of images with a complex structure is considered. Optimal and quasi-optimal algorithms for processing multidimensional images have been synthesized and investigated. Improved detection efficiency has been obtained by adequately describing real data using doubly stochastic random fields. The possibility of describing Earth remote sensing data using doubly stochastic models is investigated. The possibility of obtaining significant gains when filtering satellite material and detecting extended objects on it due to the adaptive structure of such models and processing time sequence of multizone images as a single multidimensional dataset is shown. The gains for filtering algorithms in the error variance are about 80% comparing single frame processing, and the gains for detecting algorithms in the signal/noise ratio are about 70% comparing single frame processing

Reduced Complexity Optimal Hard Decision Fusion under Neyman-Pearson Criterion

Distributed detection is an important part of many of the applications like wireless sensor networks, cooperative spectrum sensing in the cognitive radio network. Traditionally optimal non-randomized hard decision fusion rule under Neyman Pearson(NP) criterion is exponential in complexity. But recently [4] this was solved using dynamic programming. As mentioned in [4] that decision fusion problem exhibits semi-monotonic property in a special case. We use this property in our simulations and eventually apply dynamic programming to solve the problem with further reduced complexity. Further, we study the e�ect of using multiple antennas at FC with reduced complexity rule