A visible watermarking technique to provide copyright protection for portrait images is proposed in this paper. The proposal is focused on real-world applications where a portrait image is printed and illegitimately used for commercial purposes. It is well known that this is one of the most difficult challenges to prove ownership through current watermark techniques. We propose an original approach which avoids the deficiencies of typical watermarking methods in practical scenarios by introducing a smart process to automatically detect the most suitable region of the portrait image, where the visible watermark goes unnoticed to the naked eye of a viewer and is robust enough to remain visible when printed. The position of the watermark is determined by performing an analysis of the portrait image characteristics taking into account several conditions of their spatial information together with human visual system properties. Once the location is set, the watermark embedding process is performed adaptively by creating a contrast effect between the watermark and its background. Several experiments are performed to illustrate the proper functioning of the proposed watermark algorithm on portrait images with different characteristics, including dimensions, backgrounds, illumination and texture, with the conclusion that it can be applied in many practical situations.

This letter propose a fast frequency domain adaptive filter algorithm (FADF) for applications in which large order adaptive filters are required. Proposed FADF algorithm reduces the block delay of conventional FADF algorithms allowing a more efficient selection of the fast Fourier Transform (FFT) size. Proposed FADF algorithm also provides faster convergence rates than conventional FBAF algorithms by using a near-optimum convergence factor derived by using the FFT. Computer simulations using white and colored signals are given to show the desirable features of proposed scheme.

In the last few years analog adaptive filters have been a subject of active research because they have the ability to handle in real time much higher frequencies, with a smaller size and lower power consumption that their digital counterparts. During this time several analog adaptive filter algorithms have been reported in the literature, almost all of them use the continuous time version of the least mean square (LMS) algorithm. However the continuous time LMS algorithm presents the same limitations than its digital counterpart, when operates in noisy environments, although their convergence rate may be faster than the digital versions. This fact suggests the necessity of develop analog versions of recursive least square (RLS) algorithm, which in known to have a very low sensitivity to additive noise. However a direct implementation of the RLS in analog way would require a considerable effort. To overcome this problem, we propose an analog RLS algorithm in which the adaptive filter coefficients vector is estimated by using a fully connected network that resembles a Hopfield network. Theoretical and simulations results are given which show that the proposed and conventional RLS algorithms have quite similar convergence properties when they operate with the same sampling rate and signal-to-noise ratio.

An alaryngeal speech enhancement system is proposed to improve the intelligibility and quality of speech signals generated by an artificial larynx transducer (ALT). Proposed system identifies the voiced segments of alaryngeal speech signal, by using pattern recognition methods, and replaces these by their equivalent voiced segments of normal speech. Evaluation results show that proposed system provides a fairly good improvement of the quality and intelligibility of ALT generated speech.

This paper presents a facial expression recognition algorithm based on segmentation of a face image into four facial regions (eyes-eyebrows, forehead, mouth and nose). In order to unify the different results obtained from facial region combinations, a modal value approach that employs the most frequent decision of the classifiers is proposed. The robustness of the algorithm is also evaluated under partial occlusion, using four different types of occlusion (half left/right, eyes and mouth occlusion). The proposed method employs sub-block eigenphases algorithm that uses the phase spectrum and principal component analysis (PCA) for feature vector estimation which is fed to a support vector machine (SVM) for classification. Experimental results show that using modal value approach improves the average recognition rate achieving more than 90% and the performance can be kept high even in the case of partial occlusion by excluding occluded parts in the feature extraction process.

We present the Fuzzy Directional (FD) filter to remove impulse noise from corrupted colour images. Simulation results have shown that the restoration performance is better in comparison with other known filters.