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Most traditional video summarization methods are designed to generate effective summaries for single-view videos, and thus they cannot fully exploit the complicated intra- and inter-view correlations in summarizing multi-view videos. In this paper, we introduce a novel framework for summarizing multi-view videos in a way that takes into consideration both intra- and inter-view correlations in a joint...
Due to its importance, figure/ground segmentation in video has gained interest recently. The key factor of the segmentation is the construction of the spatio-temporal coherence. Previous works usually use the motion approximation as a measurement of the coherence, resulting in a low accuracy. In this paper, we present a novel method to measure the coherence, and an algorithm for target segmentation...
We derive theoretical guarantees for the exact recovery of piecewise constant two-dimensional images from a minimal number of non-uniform Fourier samples using a convex matrix completion algorithm. We assume the discontinuities of the image are localized to the zero level-set of a bandlimited function, which induces certain linear dependencies in Fourier domain, such that a multifold Toeplitz matrix...
A novel method, Stochastically Acquired Photoplethysmo-gram for Heart rate Inference in Realistic Environments (SAPPHIRE), is proposed for robust remote heart rate measurement through broadband video. A set of stochastically sampled points from the cheek region is tracked and used to construct corresponding time series observations via skin erythema transforms. From these observations, a photo-plethysmogram...
We develop an unsupervised graph clustering and image segmentation algorithm based on non-negative matrix factorization. We consider arbitrarily represented visual signals (in 2D or 3D) and use a graph embedding approach for image or point cloud segmentation. We extend a Projective Non-negative Matrix Factorization variant to include local spatial relationships over the image graph. By using properly...
This paper addresses the problem of designing a global tone mapping operator for rate-distortion optimized backward compatible compression of HDR images. We consider a two layer coding scheme in which a base SDR layer is coded with HEVC, inverse tone mapped and subtracted from the input HDR signal to yield the enhancement HDR layer. The tone mapping curve design is formulated as the minimization of...
Discrete tomography problems require the generation of an underlying attenuation field over a combinatorial set that renders the direct application of conventional tomographic reconstruction techniques obsolete. In this work, we propose a solution approach based on a new variable splitting coupled with the alternating direction method of multipliers (ADMM). The new approach creates subproblems that...
In this paper, we introduce a multi-dimensional approach to the problem of reconstruction of MR image sequences that are highly undersampled in k-space. By formulating the reconstruction as a high-order low-rank plus sparse tensor decomposition problem, we propose an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM) to solve the optimization. Through extensive...
Content-aware image retargeting adjusts images to arbitrary sizes and preserves visually salient content. Previous algorithms formulate the problem in terms of either pixel level or mesh level structures, deforming salient objects inconsistently. To improve retargeting quality and reduce complexity, we introduced a patch-wise method to generate sparse image grids based on visual saliency and gradient...
In volume seam carving, seam carving for three-dimensional (3D) cost volume, an optimal seam surface can be derived by graph cuts, resulting from sophisticated graph construction. However, the graph cuts algorithm is not suitable for practical use because it incurs a heavy computational load. We propose a multi-pass dynamic programming (DP) based approach for volume seam carving that reduces computation...
This paper presents a new tensor completion method named minimum volume constraint tensor completion. Unlike the nuclear norm penalization based methods, our method extends the conception of the matrix volume to the tensor volume, and uses the volume measurement as the penalization to address the tensor completion problem. The alternating direction method of multipliers (ADMM) algorithm is then employed...
The robust principal component analysis (PCA) method has shown very promising results in seismic ambient noise attenuation when dealing with outliers in the data. However, the model assumes a general Gaussian distribution plus sparse outliers for the noise. In seismic data however, the noise standard variation could vary from one place to another leading to a more heavy-tailed noise distribution....
Machine learning is a very promising way of solving some image processing tasks. However, existing approaches fails at integrating feature selection within the learning task. This paper introduces a new two stage learning algorithm called near infinitely linear combination (NILC) that performs at the same time variable selection and error minimization. Empirical evidence reported on different document...
In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated on the observation that unlabeled data is cheap and can be used to improve the accuracy of classifiers. In this paper we propose an unsupervised regularization term that explicitly forces the classifier's prediction for multiple classes to be mutually-exclusive...
A new image specularity removal method is presented in this paper. This method is based on the polarization imaging through global energy minimization. Traditional color-based methods generate severe color distortions, and local-patch-based algorithms produce limited results without integrating the long range information. To handle these limitations, the proposed method uses polarization images to...
We address the problem of optimizing block-coded motion parameters for use inside typical motion-compensating video encoders. We cast the given discrete problem as a nonsmooth nonconvex optimization problem which is defined over some graph, and solve it using the split primal-dual hybrid gradient algorithm. Although computational efficiency is not the main focus of this paper, an efficient, parallelized...
In this paper, we address the problem of the statistical multiplexing of video streams. Dynamic bitrate allocation is used to improve the overall video quality of a pool of channels. The balance is obtained by providing more bits to complex channels, while deprivations are applied to non-complex ones. In this study, the error minimization optimization of several compressed video is considered along...
Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Various greedy recovery algorithms have been proposed to achieve a lower computational complexity compared to the optimal ℓ1 minimization, while maintaining a good reconstruction accuracy. We propose a new greedy recovery algorithm for compressed sensing, called the Adaptive Reduced-set...
This work considers reconstructing a target signal in a context of distributed sparse sources. We propose an efficient reconstruction algorithm with the aid of other given sources as multiple side information (SI). The proposed algorithm takes advantage of compressive sensing (CS) with SI and adaptive weights by solving a proposed weighted 𝓃-ℓ1 minimization. The proposed algorithm computes the adaptive...
We consider the total variation (TV) minimization problem used for compressive sensing and solve it using the generalized alternating projection (GAP) algorithm. Extensive results demonstrate the high performance of proposed algorithm on compressive sensing, including two dimensional images, hyperspectral images and videos. We further derive the Alternating Direction Method of Multipliers (ADMM) framework...
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