Anamitra Makur

Warped discrete-Fourier transform: Theory and applications

In this paper, we advance the concept of warped discrete-Fourier transform (WDFT), which is the evaluation of frequency samples of the z-transform of a finite-length sequence at nonuniformly spaced points on the unit circle obtained by a frequency transformation using an allpass warping function. By factorizing the WDFT matrix, we propose an exact computation scheme for finite sequences using less number of operations than a direct computation. We discuss various properties of WDFT and the structure of the factoring matrices. Examples of WDFT for first- and second-order allpass functions is also presented. Applications of WDFT included are spectral analysis, design of tunable FIR filters, and design of perfect reconstruction filterbanks with nonuniformly spaced passbands of filters in the bank. WDFT is efficient to resolve closely spaced sinusoids. Tunable FIR filters may be designed from FIR prototypes using WDFT. In yet another application, warped PR filterbanks are designed using WDFT and are applied for signal compression.

Iterative least squares and compression based estimations for a four-parameter linear global motion model and global motion compensation

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 traditional least-squares estimation scheme, two more estimation schemes based on the minimization of the motion field bit rate and the global prediction error energy are proposed. Among the three estimation schemes, the iterative least-squares estimation is observed to be the best because of the least computational complexity, accuracy of the estimated parameters, and similar performance as with the other schemes. Four global motion compensation schemes including the existing pixel-based forward compensation are proposed. It is observed that backward compensation schemes perform similarly to the corresponding forward schemes except for having one frame delay degradation. The pixel based forward compensation is observed to have the best performance. A new motion vector coding scheme is proposed which has similar performance as the two-dimensional entropy coding but needs much less computation. Using the proposed coding scheme with the pixel-based forward compensation, we obtain 61.85% savings in motion field bit rate over the conventional motion compensation for the Tennis sequence.

Dictionary Training for Sparse Representation as Generalization of K-Means Clustering

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, MOD can be viewed as a parallel generalization of K-means, which simplifies to K-means without perturbing the sparse coefficients. Keeping memory usage in mind, we propose an alternative to MOD; a sequential generalization of K-means (SGK). While experiments suggest a comparable training performances across the algorithms, complexity analysis shows MOD and SGK to be faster under a dimensionality condition.

A compressive sensing approach to object-based surveillance video coding

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 proposes several techniques using two approaches- direct CS and transform-based CS. The techniques are studied and analyzed by varying the different trade-off parameters such as the measurement index, quantization levels etc. Finally we recommend an optimal scheme for a range of bitrates. Experimental results with comparative bitrates-vs-PSNR graphs for the different techniques are presented

Online adaptive radial basis function networks for robust object tracking

Visual tracking has been a challenging problem in computer vision over the decades. The applications of visual tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. In this paper, we present a novel online adaptive object tracker based on fast learning radial basis function (RBF) networks. Pixel based color features are used for developing the target/object model. Here, two separate RBF networks are used, one of which is trained to maximize the classification accuracy of object pixels, while the other is trained for non-object pixels. The target is modeled using the posterior probability of object and non-object classes. Object localization is achieved by iteratively seeking the mode of the posterior probability of the pixels in each of the subsequent frames. An adaptive learning procedure is presented to update the object model in order to tackle object appearance and illumination changes. The superior performance of the proposed tracker is illustrated with many complex video sequences, as compared against the popular color-based mean-shift tracker. The proposed tracker is suitable for real-time object tracking due to its low computational complexity.

Lossy compression of encrypted image by compressive sensing technique

The problem of lossy compression of encrypted image data is considered in this paper. A method is proposed to achieve compression of the encrypted image data based on com-pressive sensing technique. Joint decoding/decryption is proposed with the modified basis pursuit decoding method to take care of encryption. Simulation results are provided to demonstrate the compression results of the proposed compression method based on compressive sensing.

Variable dimension vector quantization based image watermarking

In this paper, we present a watermarking method based on variable dimension vector quantization for hiding information in images. Watermark bits are embedded in the dimension information of the variable dimension reconstruction blocks of the cover or input image. Watermark extraction does not require the existence of the original image for oblivious watermarking, while a variation of the scheme for cover escrow watermarking is also presented to increase robustness. Experimental results show the effectiveness of the proposed scheme and this scheme gives comparable capacity with the existing schemes.

The existence and design of functional observers for two-dimensional systems

Abstract This paper is concerned with functional observers for two-dimensional (2D) systems described by the Fornasini–Marchesini local state-space second model. A necessary and sufficient condition is given for the existence of asymptotic functional observers. A more tractable necessary condition for the existence of asymptotic functional observers is also presented, as it helps exclude those 2D systems for which there is no asymptotic functional observer. A constructive design method is then proposed for the design of asymptotic functional observers using a rank condition on the given system matrices and a linear matrix inequality (LMI) technique. Two illustrative examples are provided to demonstrate the feasibility and effectiveness of the proposed method.

Distributed source coding based encryption and lossless compression of gray scale and color images

Compression of encrypted data is possible by using distributed source coding. In this paper, we consider the encryption, followed by lossless compression of gray scale and color images. We propose to apply encryption on the prediction errors instead of directly applying on the images and use distributed source coding for compressing the cipher texts. The simulation results show that by using the proposed technique comparable compression gains, with compression ratios varying from 1.5 to 2.5 can be achieved despite encryption.

Non-fragile H 2 and H ∞ filter designs for polytopic two-dimensional systems in Roesser model

This paper is concerned with the problem of non-fragile H2 and H∞ filter designs for two-dimensional (2-D) discrete systems in Roesser model with polytopic uncertainties. The filters to be designed are assumed to be with additive norm-bounded coefficient variations which reflect the imprecision in filter implementation. The complicated filter design problem is successfully tackled by using the slack variable technique and imposing a structural restriction on the slack matrix. Explicit expressions of the non-fragile H2 and H∞ filters are given in terms of solutions to a set of linear matrix inequalities (LMIs). An illustrative example is provided to demonstrate the feasibility and effectiveness of the proposed method.