Abstract: This paper presents a method for identification
of a linear time invariant (LTI) autonomous all pole system
using singular value decomposition. The novelty of this paper
is two fold: First, MUSIC algorithm for estimating complex
frequencies from real measurements is proposed. Secondly,
using the proposed algorithm, we can identify the coefficients
of differential equation that determines the LTI system by
switching off our input signal. For this purpose, we need only
to switch off the input, apply our complex MUSIC algorithm
and determine the coefficients as symmetric polynomials in the
complex frequencies. This method can be applied to unstable
system and has higher resolution as compared to time series
solution when, noisy data are used. The classical performance
bound, Cramer Rao bound (CRB), has been used as a basis for
performance comparison of the proposed method for multiple
poles estimation in noisy exponential signal.
Abstract: Key frame extraction methods select the most
representative frames of a video, which can be used in different areas
of video processing such as video retrieval, video summary, and video
indexing. In this paper we present a novel approach for extracting key
frames from video sequences. The frame is characterized uniquely by
his contours which are represented by the dominant blocks. These
dominant blocks are located on the contours and its near textures.
When the video frames have a noticeable changement, its dominant
blocks changed, then we can extracte a key frame. The dominant
blocks of every frame is computed, and then feature vectors are
extracted from the dominant blocks image of each frame and arranged
in a feature matrix. Singular Value Decomposition is used to calculate
sliding windows ranks of those matrices. Finally the computed ranks
are traced and then we are able to extract key frames of a video.
Experimental results show that the proposed approach is robust
against a large range of digital effects used during shot transition.