Anamitra Makur

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In this paper, a four-parameter model for global motion in image sequences is proposed. The model is generalized and can accommodate global object motions besides the motions due to the camera movements. Only the PAN and the ZOOM global motions are considered because of their relatively more frequent occurrences in real video sequences. Besides the(More)
Recent dictionary training algorithms for sparse representation like K-SVD, MOD, and their variation are reminiscent of K-means clustering, and this letter investigates such algorithms from that viewpoint. It shows: though K-SVD is sequential like K-means, it fails to simplify to K-means by destroying the structure in the sparse coefficients. In contrast,(More)
This letter presents a variant of Orthogonal Matching Pursuit (OMP) method, called Backtracking-based Adaptive OMP (BAOMP), for compressive sensing and sparse signal reconstruction. As an extension of the OMP algorithm, the BAOMP method incorporates a simple backtracking technique to detect the previous chosen atoms' reliability and then deletes the(More)
The use of a suitable perceptual model is necessary to minimize the visual distortion in the marked images, because minor modification to the pixels can be perceptible since the pixels are either black or white. In this paper, a new perceptual model is proposed for binary images that is useful for data hiding applications. In our model, the distortion that(More)
This paper studies the feasibility and investigates various choices in the application of compressive sensing (CS) to object-based surveillance video coding. The residual object error of a video frame is a sparse signal and CS, which aims to represent information of a sparse signal by random measurements, is considered for coding of object error. This work(More)
A class of infinite impulse response (IIR) perfect reconstruction (PR) filterbank is obtained with an allpass delay chain and finite impulse response (FIR) matrices at the analysis side. Such a formulation leads to a filterbank that can be optimized to any desired response. For a first-order allpass, the synthesis bank becomes FIR. Design examples showing(More)
Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP) are two well-known recovery algorithms in compressed sensing. To recover a d-dimensional m-sparse signal with high probability, OMP needs O(m ln d) number of measurements, whereas BP needs only O(m ln d/m) number of measurements. In contrary, OMP is a practically more appealing algorithm due to its(More)
A novel approach for the lossless compression of monochrome images using switching theoretic techniques is presented. Bit planes of the image are divided into blocks and converted to Boolean switching functions. Compression is performed by finding a minimal representation for each switching function. The method compares well with JPEG in terms of(More)