Informatica

Basic motion structures of crowd aggregation and crowd dispersion are defined and a novel method for identifying these crowd behaviours is proposed. Based on integral optical flow, background and foreground are separated and intensive motion region is obtained. Crowd motion is analysed at pixel-level statistically for each frame to obtain quantity of pixels moving toward or away from each position and their comprehensive motion at each position. Regional motion indicators are computed and regional motion maps are formed to describe motions at region-level. Crowd behaviours are identified by threshold segmentation of regional motion maps.

Fuzzy C-Means (FCM) algorithm is one of the commonly preferred fuzzy algorithms for image segmentation applications. Even though FCM algorithm is sufficiently accurate, it suffers from the computational complexity problem which prevents the usage of FCM in real-time applications. In this work, this convergence problem is tackled through the proposed Modified FCM (MFCM) algorithm. In this algorithm, several clusters among the input data are formed based on similarity measures and one representative data from each cluster is used for FCM algorithm. Hence, this methodology minimizes the convergence time period requirement of the conventional FCM algorithm to higher extent. This proposed approach is experimented on Magnetic Resonance (MR) brain tumor images. Experimental results suggest promising results for the MFCM algorithm in terms of the performance measures.

A practical method for segmentation and estimation of model parameters of processes is proposed in this paper. A pseudo-stationary random process with instantly changing properties is divided into stationary segments. Every segment is described by an autoregressive model. A maximum likehood method is used for segmentation of the random process and estimation of unknown model parameters. An example with simulated data is presented.