The phase holds crucial information about image structures and features, but only the real part or the magnitude of the transform coefficients is often used for image processing applications. In this paper, a method for the feature extraction of images called Phase-based LBP is presented. Proposed method is based on the combination of phase of complex wavelet coefficients and the Local Binary Pattern operator (LBP). We also perform the comparative analysis about retrieval effectiveness of phase information of some complex wavelet transforms using for Phase-based LBP. Experimental results, achieved with the standard rotated Brodatz dataset, show the interest of this method comparing with another methods only based on the real part or the magnitude of the wavelet coefficients for texture image retrieval.
We propose and analyze a distributed switch and stay combining network with selection relaying with an aim to reduce the complexity at the destination as well as to make efficient use of the degrees of freedom of the channels by exploiting a limited feedback signal from the destination.In particular, whenever the currently connected link (either from the source or from the best relay) to the destination is not favorable to decoding, the destination will switch to the alternative link as per the rule of switch and stay combining.The performance of the proposed system is derived in terms of outage probability and achievable spectral efficiency. The analytic results show that the proposed system assisted by the selection relaying exhibits a higher spectral efficiency than incremental relaying with selection relaying at low SNR regime. Monte-Carlo simulations are performed to verify the analysis.Index Terms-switch and stay combining, selection relaying, incremental relaying, decode-and-forward, Rayleigh fading, out age probability.
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