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m e paper describes a nonlinear operator, based on Frequency Domain Kurtosis (FDK), that is able to distinguish between transients (impulses and unsteady harmonic components) and stationary sinusoidal signals in background Gaussian noise. For problems involving signal detection in burst noise, filtering by an FDK operator may improve detection results by reducing noise variance and amplifiing narrow-band transient signals.To evaluate the FDK operator's eflciency, direrent processes were considered: impulsive noise and narrowband inteferences in background Gaussian noise and real electromagnetic non-Gaussian noise. The filter robustness is discussed.

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A system for interpretation of complex scenes is presented. The main characteristics of the system are: virtual multisensor input, knowledge based and multilevel architecture, and intensional approach. The specific application performed by the system is crowding evaluation in underground station environment, in order to detect dangerous situation, by using optical sensors. The multilevel architecture of the system is modelled as a probabilistic network of passing-message nodes. Each node corresponds to a virtual distributed processor that is used to obtain the probabilistic value of the locally detected crowding level. The network updating mechanism is presented. By using several Low Level algorithms suitable features are extracted from images. The virtual sensor models are described