Masoud Alghoniemy

Geometric Invariance in image watermarking

Surviving geometric attacks in image watermarking is considered to be of great importance. In this paper, the watermark is used in an authentication context. Two solutions are being proposed for such a problem. Both geometric and invariant moments are used in the proposed techniques. An invariant watermark is designed and tested against attacks performed by StirMark using the invariant moments. On the other hand, an image normalization technique is also proposed which creates a normalized environment for watermark embedding and detection. The proposed algorithms have the advantage of being robust, computationally efficient, and no overhead needs to be transmitted to the decoder side. The proposed techniques have proven to be highly robust to all geometric manipulations, filtering, compression and slight cropping which are performed as part of StirMark attacks as well as noise addition, both Gaussian and salt & pepper.

Geometric distortion correction through image normalization

A new approach for geometric distortion correction based on image normalization is presented in this paper. By normalization we mean geometrically transforming the image into a standard form. The parameters by which the image is normalized are estimated from the geometric moments of the image. This paper presents a system in which the watermark is embedded and detected in the normalized image. The watermark can then be embedded and detected in the normalized image regardless of its size, orientation and flipping direction.

Image watermarking by moment invariants

We present a novel technique for watermarking digital images based on its moments. The watermark is composed of the mean of several functions of the second and third order moments designed to be invariant to scaling and orthogonal transformations this has the advantage of making the watermark image dependent. The watermarked image is a linear combination of the original image and a weighted nonlinear transformation of the original. The weight is computed such that the mean of the watermarked image invariants is a predefined number. Watermark detection is as simple as computing the moment invariants of the received image. Our approach has a guaranteed visual transparency up to a contrast modification. The proposed algorithm has proved to be highly robust to all geometric manipulations, filtering, compression and small cropping which are performed as part of StirMark attacks as well as noise addition, both Gaussian and salt and pepper.

Progressive quantized projection watermarking scheme

In this paper we present a new watermarking technique for digital images. Our approach modifies blocks of the image after projecting them onto certain directions. By quantizing the projected blocks to even and odd values we can represent the hidden information properly. The proposed algorithm does the modification progressively to ensure successful data extraction without any prior information being sent to the receiver side. In order to increase the robustness of our watermark to scaling and rotation attacks we also present a solution to recover the original size and orientation based on a training sequence which is inserted as part of the watermark.

Rhythm and periodicity detection in polyphonic music

We describe a novel approach for detecting perfect and imperfect periodicities in polyphonic music. The approach relies on beat and rhythm information extracted from the raw data after low-pass filtering. The beat and rhythm information is analyzed with a binary tree or trellis tree parsing depending on the length of the pauses in the underlying signal. This analysis yields accurate periodicity patterns at macro and micro scales. We illustrate the effectiveness of our approach using music segments from various cultures and explain its use in music classification and content-based retrieval.

User-defined music sequence retrieval

A system for retrieving a sequence of music excerpts or songs based on users and producers requirements is proposed in this paper. Our system provides a flexible way to retrieve music pieces based on its contents as well as user-defined constraints. The proposed system allows online users to extract a sequence of songs whose first and last tracks are known and at the same time the in-between songs have minimum inter-track differences and satisfy predefined requirements. We model the problem as a constrained minimum cost flow problem which leads to a binary integer linear program (BILP) that can be solved in a reasonable amount of time.

Reduced complexity bounded error subset selection

A reduced complexity version of the bounded error subset selection (BESS) algorithm is proposed. By relaxing the integer constraint in the original BESS algorithm, we show that the BESS problem can be reformulated as an ordinary linear program instead of an integer program with exponential worst-case complexity. We retain the sparseness of the representation in the modified BESS by weighting the dictionary with the minimum 2-norm solution of the subset selection problem corresponding to the BESS problem at hand. The proposed algorithm is compared to the basis pursuit, orthogonal matching pursuit, and the best orthogonal basis algorithms. It is shown that the proposed algorithm has a better packing property and an improved rate-distortion behavior.

A sparse solution to the bounded subset selection problem: a network flow model approach

We reformulate the problem of finding the sparsest representation of a given signal using an overcomplete dictionary as a bounded error subset selection problem. Specifically, the reconstructed signal is allowed to differ from the original signal by a bounded error. We argue that this bounded error formulation is natural in many applications, such as coding. Our novel formulation guarantees the sparsest solution to the bounded error subset selection problem by minimizing the number of nonzero coefficients in the solution vector. We show that this solution can be computed by finding the minimum cost flow path of an equivalent network. Integer programming is adopted to find the solution.

Personalized music distribution

New media distribution channels have created a strong need for digital media personalization that helps both users and producers get the most of media products. We concentrate on the customized music delivery issue. We propose an approach for constructing a sequence of tracks that satisfies user requirements and at the same time optimally exploits music catalogs. An optimal solution consists of examining all possible tracks enumeration in the database. This is clearly a combinatorial NP-hard problem. We use vector space concepts to formulate the constrained sequence retrieval problem as an integer program. Our experiments demonstrate the power of our approximation to the original problem in reducing the search space and producing valid solutions.

Scalable real-time energy-efficient EEG compression scheme for wireless body area sensor network

Abstract Recent technological advances in wireless body sensor networks have made it possible for the development of innovative medical applications to improve health care and the quality of life. By using miniaturized wireless electroencephalography (EEG) sensors, it is possible to perform ambulatory EEG recording and real-time healthcare applications. One master consideration in using such battery-powered wireless EEG monitoring system is energy constraint at the sensor side. The traditional EEG streaming approach imposes an excessive power consumption, as it transmits the entire EEG signals wirelessly. Therefore, innovative solutions to alleviate the total power consumption at the receiver are highly desired. This work introduces the use of the discrete wavelet transform and compressive sensing algorithms for scalable EEG data compression in wireless sensors in order to address the power and distortion constraints. Encoding and transmission power models of both systems are presented which enable analysis of power and performance costs. We then present a theoretical analysis of the obtained distortion caused by source encoding and channel errors. Based on this analysis, we develop an optimization scheme that minimizes the total distortion for different channel conditions and encoder settings. Using the developed framework, the encoder can adaptively tune the encoding parameters to match the energy constraint without performance degradation.