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    An ultra low-voltage Gm-C filter for video applications

    , Article Proceedings of the 2003 IEEE International Symposium on Circuits and Systems, Bangkok, 25 May 2003 through 28 May 2003 ; Volume 1 , 2003 , Pages I561-I564 ; 02714310 (ISSN) Mehrmanesh, S ; Vahidfar, M. B ; Aslanzadeh, H. A ; Atarodi, M ; Sharif University of Technology
    2003
    Abstract
    A new, ultra low-voltage, linear CMOS OTA will be described. A 4th order, 18 MHz low pass Butterworth Gm-C filter has been designed with this new OTA stage for video applications. In this filter a new, fully digital approach has been used for frequency tuning. The THD of the filter for input signal 0.5 VPPis better than -60 dB. All of circuits are designed based on 0.25 um CMOS process technology with a single 1-volt power supply  

    Application of 3D-wavelet statistics to video analysis

    , Article Multimedia Tools and Applications ; Volume 65, Issue 3 , 2013 , Pages 441-465 ; 13807501 (ISSN) Omidyeganeh, M ; Ghaemmaghami, S ; Shirmohammadi, S ; Sharif University of Technology
    2013
    Abstract
    Video activity analysis is used in various video applications such as human action recognition, video retrieval, video archiving. In this paper, we propose to apply 3D wavelet transform statistics to natural video signals and employ the resulting statistical attributes for video modeling and analysis. From the 3D wavelet transform, we investigate the marginal and joint statistics as well as the Mutual Information (MI) estimates. We show that marginal histograms are approximated quite well by Generalized Gaussian Density (GGD) functions; and the MI between coefficients decreases when the activity level increases in videos. Joint statistics attributes are applied to scene activity grouping,... 

    Low rank and sparse decomposition for image and video applications

    , Article IEEE Transactions on Circuits and Systems for Video Technology ; Volume 30, Issue 7 , 2020 , Pages 2046-2056 Zarmehi, N ; Amini, A ; Marvasti, F ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Abstract
    The matrix decomposing into a sum of low-rank and sparse components has found extensive applications in many areas including video surveillance, computer vision, and medical imaging. In this paper, we propose a new algorithm for recovery of low rank and sparse components of a given matrix. We have also proved the convergence of the proposed algorithm. The simulation results with synthetic and real signals such as image and video signals indicate that the proposed algorithm has a better performance with lower run-time than the conventional methods. © 1991-2012 IEEE