Showing 4 results for Filtering
A. Abadpour, S. Kasaei,
Volume 1, Issue 3 (7-2005)
Abstract
A robust skin detector is the primary need of many fields of computer vision,
including face detection, gesture recognition, and pornography filtering. Less than 10 years
ago, the first paper on automatic pornography filtering was published. Since then, different
researchers claim different color spaces to be the best choice for skin detection in
pornography filtering. Unfortunately, no comprehensive work is performed on evaluating
different color spaces and their performance for detecting naked persons. As such,
researchers usualy refer to the results of skin detection based on the work doen for face
detection, which underlies different imaging conditions. In this paper, we examine 21 color
spaces in all their possible representations for pixel-based skin detection in pornographic
images. Consequently, this paper holds a large investigation in the field of skin detection,
and a specific run on the pornographic images.
M. H. Savoji, S. Chehrehsa,
Volume 10, Issue 3 (9-2014)
Abstract
Gaussian Mixture Models (GMMs) of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made on the nature or stationarity of the noise. No voice activity detection (VAD) or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equations whose solutions lead to the first estimates of speech and noise power spectra. The noise source is also identified and the input SNR estimated in this first step. These first estimates are then refined using approximate but explicit MMSE and MAP estimation formulations. The refined estimates are then used in a Wiener filter to reduce noise and enhance the noisy speech. The proposed schemes show good results. Nevertheless, it is shown that the MAP explicit solution, introduced here for the first time, reduces the computation time to less than one third with a slight higher improvement in SNR and PESQ score and also less distortion in comparison to the MMSE solution.
G. Alipoor,
Volume 13, Issue 4 (12-2017)
Abstract
Performance of the linear models, widely used within the framework of adaptive line enhancement (ALE), deteriorates dramatically in the presence of non-Gaussian noises. On the other hand, adaptive implementation of nonlinear models, e.g. the Volterra filters, suffers from the severe problems of large number of parameters and slow convergence. Nonetheless, kernel methods are emerging solutions that can tackle these problems by nonlinearly mapping the original input space to the reproducing kernel Hilbert spaces. The aim of the current paper is to exploit kernel adaptive filters within the ALE structure for speech signal enhancement. Performance of these nonlinear algorithms is compared with that of their linear as well as nonlinear Volterra counterparts, in the presence of various types of noises. Simulation results show that the kernel LMS algorithm, as compared to its counterparts, leads to a higher improvement in the quality of the enhanced speech. This improvement is more significant for non-Gaussian noises.
M. Ghayeni,
Volume 15, Issue 4 (12-2019)
Abstract
In this paper, the new approach for the transmission reliability cost allocation (TRCA) problem is proposed. In the conventional TRCA problem, for calculating the contribution of each user (generators & loads or contracts) in the reliability margin of each transmission line, the outage analysis is performed for all system contingencies. It is obvious that this analysis is very time-consuming for large power systems. This paper suggests that this calculation should be done only for major contingencies. To do this, at first, the contingency filtering technique (CFT) is introduced based on the new economic indices that quantify the severity of each contingency to determine the critical contingencies. Then the results of contingency filtering are used in the TRCA problem. The simulation results are reported for the IEEE 118-bus test system. The obtained results show that by application of CFT in TRCA problem, the simulation time is greatly reduced, but the percentage of error remains within an acceptable limit.
