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The Mojette transform is a discrete, exact and redundant Radon transform. The application of the Mojette transform for lossless image compression is based on image projection similarity using different directions, with intra-projection coding, inter-projection coding and differential coding schemes being applied. For the latter case, we propose mean coding of projections to improve the Mojette transform...
Many statistical learning tasks deal with data which are presented in high-dimensional spaces, and the 'curse of dimensionality' phenomenon is often an obstacle to the use of many methods for solving these tasks. To avoid this phenomenon, various dimensionality reduction algorithms are used as the first key step in solving these tasks. The algorithms transform original high-dimensional data into lower...
In this work, we present extensions of the framework of sampling and reconstructing signals with a finite rate of innovation (FRI) to the graph domain, by tackling the problem of _R"-sparse graph signal reconstruction on perturbed circulant graphs, simulating network clusters within a large network. Given a dimensionality-reduced approximation of the GFT of the original graph signal, we develop...
Object tracking is an important task within the field of computer vision. Tracking accuracy depends mainly on finding good discriminative features to estimate the target location. In this paper, we introduce online feature learning in tracking and propose to learn good features to track generic objects using online convolutional neural networks (OCNN). OCNN has two feature mapping layers that are...
This paper deals with the recovery of corrupted depth maps in loss-prone networks. Different from color maps, depth maps are not directly used for display but served for view synthesis process. Therefore, the conventional concealment methods which focus on reducing the distortion of reconstructed color maps, are not suitable for the corrupted depth maps. In this paper, a novel mode selection method...
Resolution plays a crucial role for study of information in an image. Therefore to enhance the resolution of an image, there are so many techniques have been proposed with respect to the reference images. In this paper, we proposed a new scheme for single image super-resolution based on the neighbor embedding method. Many feature selection methods have been proposed for the learning based super-resolution...
The property of the system matrix of fan-beam computed tomography (CT) is investigated to achieve low radiation dose while reserve good reconstruction quality through compressed sensing (CS). To reduce the radiation dose, scanning data is under-sampled in both the view and bin direction. For limited-angle scanning, two sampling patterns are adopted: golden-angle and random-angle. For sparse bin setting,...
In this paper, we present a method for postprocessing ground penetrating radar (GPR) images that were reconstructed using the well known delay-and-sum (DAS) algorithm. The method generates improved GPR images from DAS images by minimizing a cost function where a DAS image is viewed as the data and regularization is achieved through an ℓ1 penalty function. We use the majorize-minimize principle to...
In this paper, we propose a two-step approach for the super-resolution reconstruction of video sequences based on the degraded model. Firstly we use the sparse principal component analysis and the linear minimum mean square-error estimation method to remove the noises from the degraded video sequences. Secondly we adopt the Newton-Thiele's vector valued rational interpolation which is one of the nonlinear...
Principal component analysis (PCA) is an effective statistical technique for face recognition because it can reduce the dimensions of a given unlabeled high-dimensional dataset while keeping its spatial characteristics as much as possible. However, since PCA only explains the covariance structure of all the data its most expressive components, it cannot represent the most important discriminant directions...
In this paper, we present a novel sparse imaging approach based on the multipole expansion of the electric field. On the example of the complex target imaging, we show that higher-order multipoles provide additional pieces of information, which help to resolve cases in which standard sparse imaging fails. Therefore, higher-order sparse processing may be a valuable tool in target classification and...
This paper proposes a method for reconstruction of simple objects by solving the inverse scattering problem using compressive sampling. One is a continuation of research previously developed by the authors for locating point targets. Unlike the latter, now we are dealing with more complex objects which can be seen as a white spot formed by various point target. A series of simple targets were studied...
In this communication we propose and discuss comparatively several techniques for ECG signal compression inspired from the fundamentals of compressed sensing (CS) theory, focusing on acquisition techniques, projection matrices and reconstruction dictionaries and on the effects of the preprocessing involved. Essentially, we investigate and discuss two approaches. The first approach for ECG signal compression...
Image secret sharing is an important research topic in the field of information security. Compared to a lot of digital information, images are favored in the network due to having a vivid, visual characteristics. However, most image secret sharing schemes without considering the characteristics of the image just regard the image as a series of general data and directly using the common secret sharing...
Image set based face recognition provides more opportunities compared to single mug-shot face recognition. However, modelling the variations in an image set is a challenging task. We propose a computationally efficient and accurate image set modelling technique. The idea is to reconstruct each image set sample with an unlabeled dictionary using the computationally efficient regularized least squares...
We propose a novel solution for reconstructing planar surface patches from omnidirectional camera images. The theoretical foundation relies on variational calculus, which yields a closed form solution for the normal vector a 3D planar surface patch, when a homography is known between the corresponding image region pairs. The method is quantitatively evaluated on a large set of synthetic data. Experimental...
Face recognition using eigenfaces is a popular technique based on principal component analysis (PCA). However, its performance suffers from the presence of outliers due to occlusions and noise often encountered in unconstrained settings. We address this problem by utilizing L1-eigenfaces for robust face recognition. We introduce an effective approach for L1-eigenfaces based on combining fast computation...
Three dimensional modeling of organs plays a crucial role in the treatment of cancer and radio vascular diseases. The purpose of this work is 3D modeling of breast vessels using only two uncalibrated two-dimensional mammography images in order to have the patient less exposed to X-ray radiation. In the proposed method, we first optimize the internal and external parameters using a nonlinear optimization...
Facial expression recognition has important practical applications. In this paper, we propose a method based on the combination of optical flow and a deep neural network—stacked sparse autoencoder (SAE). This method classifies facial expressions into six categories (i.e. happiness, sadness, anger, fear, disgust and surprise). In order to extract the representation of facial expressions, we choose...
The analysis of logo embedding based watermarking procedure in the presence of compressive sensing scenario is examined in this paper. The compressive sensed image is represented by a set of available coefficients, which are used for logo embedding using the image bit-planes modification. After the logo embedding and detection procedure are defined for the available CS measurements, the new type of...
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