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With the increased use of smart devices, digital cameras and abundance of memory in the devices, the pictures of the same scenes have been taken several times, resulting in a number of images consisting of the same or very similar content in memory. Manually selecting the good ones is time-consuming as well as error prone. In this paper, the features of the images in the data sets were extracted and...
Existing methods for smart data reduction are typically sensitive to outlier data that do not follow postulated data models. We propose robust censoring as a joint approach unifying the concepts of robust learning and data censoring. We focus on linear inverse problems and formulate robust censoring through a sparse sensing operator, which is a non-convex bilinear problem. We propose two solvers,...
In this contribution, we implement a fully distributed diffusion field estimation algorithm based on the use of average consensus schemes. We show that the field reconstruction problem is equivalent to estimating the sources of the field, and then derive an exact inversion formula for jointly recovering these sources when they are localized and instantaneous. Next we adapt this formula to the sensor...
In the context of robust covariance matrix estimation, this work generalizes the shrinkage covariance matrix estimator introduced in [1, 2]. The shrinkage method is a way to improve and to regularize the Tyler's estimator [3, 4]. This paper proves that the shrinkage estimator does not require any trace constraint to be well-defined, as it has been previously developed in [1]. The existence and the...
We propose a minimax robust hypothesis testing scheme that involves a composite uncertainty class based on two different distances. The first distance models the misassumptions on the nominal distributions and the second distance models the outliers. We prove that the least favorable distributions, with a desired minimax property, exist for the composite uncertainty class. It is shown that such a...
In this work we consider the estimation of spatio-temporal covariance matrices in the low sample non-Gaussian regime. We impose covariance structure in the form of a sum of Kronecker products decomposition [1, 2] with diagonal correction [2], which we refer to as DC-KronPCA, in the estimation of multiframe covariance matrices. This paper extends the approaches of [1] in two directions. First, we modify...
When there are more than necessary distance measurements in localization by distance measurements with closed form estimators, forming smaller subgroups of measurements and averaging the location estimates obtained with these subgroups of measurements makes it possible to eliminate outlier measurements if they are present. In order to eliminate these outlier results, the nearest estimate to the geometric...
With advances in sensor technology, three dimensional (3D) face has become an emerging biometric modality, preferred especially in high security applications. However, dealing with facial occlusions is a great challenge, which should be handled to enable applicability to fully automatic security systems. In this paper, we propose a technique to deal with occlusions at the classification stage: A masking...
In this study, the effect of using regression on sentiment classification of Twitter data was analyzed. In other words, whether the strength of sentiment better discriminates the classes or not. Since our dataset includes class confidence scores rather than discrete class labels, regression analysis was employed on each class separately. Then, each tweet was assigned the class whose estimated confidence...
Wave propagation in high frequency (HF) band gives the opportunity of reliable and robust long distance communication. HF communication is provided as a result of refraction and reflection of sky wave in the ionosphere. The structure of the ionosphere is time dependent, inhomogeneous and anisotropic. Modeling of the ionosphere and wave propagation in the ionosphere properly is very important for HF...
Detection and analysis of tables on document images has been one of the most researched topics in document image processing. In this study, we define novel methods for the detection and analysis of tables from document images, and show their performance results on realistic table examples. The main method developed is projection-scale-space (PSS), where local and global constraints of the table in...
Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be reconstructed using less number of measurements. In many practical systems, the observation signal has a sparse representation in a continuous parameter space. This situation rises the possibility of use of the CS reconstruction techniques in the practical problems. In order to utilize CS techniques,...
Checking authentication of digital images have become a necessity due to ever increasing use in many fields. Passive methods, which make use of image statistics, have gained popularity among researchers lately. Block based authentication methods using feature vectors of subblocks in images can be classified as either spatial or frequency domain. A new hybrid authentication technique is proposed in...
Developing a watermarking method that is robust to cropping attack is a challenging task in image watermarking. The moment-based watermarking schemes show good robustness to common signal processing attacks and some geometric attacks but are sensitive to cropping attack. In this paper, we modify the moment-based approach to deal with cropping attack. Firstly, we find the probability density function...
New promising technique for information hiding in vocalized Arabic text is proposed in this paper. This technique makes use of diacritics (vowel signs), which are optional to put, by showing or omitting them to hide bits. The technique is found to be very useful due to the considerable provided capacity, and because every possible diacritic in the cover text is used to hide bits. In this method, even...
In this work, we propose a general principle for designing RSS-based algorithms so that they are robust to all-around signal strength attacks. Specifically, when the fingerprint for the mobile device changes from <; S<;sub>1<;/sub>, S<;sub>2<;/sub>,..., S<;sub>n<;/sub> > to <; S<;sub>1<;/sub> - S<;sub>t<;/sub>, S<;sub>2<;/sub>...
The proposed scheme of this paper is embedding watermark into intra frame (I-frame). The most challenge of watermark that embedded into intra frame is the selection of intra prediction might be changed. In the research, different AC coefficient gets different effect when the selection of intra prediction was changed in another compression course. Therefore, it's necessary that finding what AC coefficients...
We study the problem of distributed estimation, where a set of nodes are required to collectively estimate some parameter of interest from their measurements. Distributed implementations avoid the use of a fusion center and distribute the processing and communication across the entire network. Among distributed solutions, diffusion algorithms have been shown to achieve good performance, increased...
In this paper we present a novel method for digital audio steganography where encrypted covert data is embedded by adaptively modifying wavelet packet coefficients of host audio signal. The major contribution of the proposed scheme is the technique introduced for adaptively modifying the host audio to embed the covert data. The modification of host audio is done by imposing a constraint which forces...
This paper prepares a review of ICA based approaches that are used for separation of components in functional MRI sequences. In previous works, the FastICA and the Infomax algorithms are investigated in more details; therefore, in this paper we focus on methods such as "radical ICA", "SDD ICA", "Erica" and "Evd" for separation purposes. This comparative study...
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