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Article Citation - WoS: 40Citation - Scopus: 66Locally Adaptive Dct Filtering for Signal-Dependent Noise Removal(Springer, 2007) Oktem, Rusen; Egiazarian, Karen; Lukin, Vladimir V.; Ponomarenko, Nikolay N.; Tsymbal, Oleg V.This work addresses the problem of signal- dependent noise removal in images. An adaptive nonlinear filtering approach in the orthogonal transform domain is proposed and analyzed for several typical noise environments in the DCT domain. Being applied locally, that is, within a window of small support, DCT is expected to approximate the Karhunen- Loeve decorrelating transform, which enables effective suppression of noise components. The detail preservation ability of the filter allowing not to destroy any useful content in images is especially emphasized and considered. A local adaptive DCT filtering for the two cases, when signal-dependent noise can be and cannot be mapped into additive uncorrelated noise with homomorphic transform, is formulated. Although the main issue is signal-dependent and pure multiplicative noise, the proposed filtering approach is also found to be competing with the state-of-the-art methods on pure additive noise corrupted images.Article Citation - WoS: 10Citation - Scopus: 15An Rfid Based Indoor Tracking Method for Navigating Visually Impaired People(Tubitak Scientific & Technological Research Council Turkey, 2010) Oktem, Rusen; Aydin, ElifThis paper tackles the RFID based tracking problem in an obscured indoor environment. The proposed solution is an integral part of a navigation aid for guiding visually impaired people in a store. It uses RF signal strengths and is based on the Bayes Decision Theory. An observation vector is formed by received radio signal strength indication values, transmitted from three transmitters at distinct frequencies in the UHF band. The indoor area is divided into square grids, where each grid is considered as a class. The problem of tracking is expressed as classifying the observed radio signal strengths to the most likely class. A classification rule is formulated by incorporating a priori assumptions appropriate for the studied model. The proposed approach is tested in a laboratory environment. The results prove that the proposed approach is promising in tracking especially when the tracked person is guided by a system.
