Browsing by Subject "Discrete Cosine Transform(DCT)"

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Open Access
Moving region detection in compressed video
(Springer, 2004) Töreyin, B. U.; Çetin, A. Enis; Aksay, A.; Akhan, M. B.
In this paper, an algorithm for moving region detection in compressed video is developed. It is assumed that the video can be compressed either using the Discrete Cosine Transform (DOT) or the Wavelet Transform (WT). The method estimates the WT of the background scene from the WTs of the past image frames of the video. The WT of the current image is compared with the WT of the background and the moving objects are determined from the difference. The algorithm does not perform inverse WT to obtain the actual pixels of the current image nor the estimated background. In the case of DOT compressed video, the DC values of 8 by 8 image blocks of Y, U and V channels are used for estimating the background scene. This leads to a computationally efficient method and a system compared to the existing motion detection methods. © Springer-Verlag 2004.
Open Access
Recognition of vessel acoustic signatures using non-linear teager energy based features
(IEEE, 2016-10) Can, Gökmen; Akbaş, Cem Emre; Çetin, A. Enis
This paper proposes a vessel recognition and classification system based on vessel acoustic signatures. Teager Energy Operator (TEO) based Mel Frequency Cepstral Coefficients (MFCC) are used for the first time in Underwater Acoustic Signal Recognition (UASR) to identify platforms the acoustic noise they generate. TEO based MFCC (TEO-MFCC), being more robust in noisy conditions than conventional MFCC, provides a better estimation platform energy. Conventionally, acoustic noise is recognized by sonar oper-ators who listen to audio signals received by ship sonars. The aim of this work is to replace this conventional human-based recognition system with a TEO-MFCC features-based classification system. TEO is applied to short-time Fourier transform (STFT) of acoustic signal frames and Mel-scale filter bank is used to obtain Mel Teager-energy spectrum. The feature vector is constructed by discrete cosine transform (DCT) of logarithmic Mel Teager-energy spectrum. Obtained spectrum is transformed into cepstral coefficients that are labeled as TEO-MFCC. This analysis and implementation are carried out with datasets of 24 different noise recordings that belong to 10 separate classes of vessels. These datasets are partially provided by National Park Service (NPS). Artificial Neural Networks (ANN) are used as a classification method. Experimental results demonstrate that TEO-MFCC achieves 99.5% accuracy in classification of vessel noises. © 2016 IEEE.