Sharif Digital Repository

In this paper, we propose a new algorithm for occlusion handling for object tracking in the crowded video scenes. The algorithm exploits the properties of undecimated wavelet packet transform (UWPT) coefficients and texture analysis to track arbitrary objects. The algorithm is initialized by the user through specifying a region around the object of interest at the reference frame. Then, coefficients of the UWPT of the region construct a Feature Vector (FV) for every pixel in that region. Optimal search for the best match is then performed by using the generated FVs inside an adaptive search window. Adaptation of the search window is achieved by interframe texture analysis to find the... 

In this paper, we present a spatio-temporal event-based approach to video signal analysis and abstraction employing wavelet transform features. The video signal is assumed to be a sequence of overlapping independent visual components called events, which typically are temporally overlapping compact functions that describe temporal evolution of a given set of the spatial parameters of the video signal. We utilize event-based temporal decomposition technique to resolve the overlapping arrangement of the video signal that is known to be one of the main concerns in video analysis via conventional frame-based schemes. In our method, a set of spatial parameters, extracted from the video, is... 

The assessment of the hypnotic state of the brain is crucial to the process of an operation under general anaesthesia. A noninvasive method of quantifying depth of anaesthesia is through analysis of electroencephalogram (EEG). Among number of works done in the field, no single algorithm has been found exhibiting a precise measure in all of the hypnotic states. One can categorise algorithms as either a state-quantifier or a trend measure. State-quantifier algorithms can discriminate between different hypnotic states such as awake, light sedation, deep anaesthesia, etc. On the other hand, trend measure algorithms are employed to specify the short-term changes in hypnotic brain conditions,... 

This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, KPCA, and RBF Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform, PCA and KPCA. During the classification stage, the Neural Network (RBF) is explored to achieve a robust decision in presence of wide facial variations. At first derives a feature vector from a set of downsampled wavelet representation of face images, then the resulting PCA-based linear features and...