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We propose optimal rate-allocation, using viewer attention information among viewpoints, for depth map cameras within a free-viewpoint television broadcast system. An attention-weighted rate-allocation framework enables bit-rate, or quality, to be distributed across the multiple cameras in accordance with viewer interest, minimizing total observed distortions perceived among all viewers. Prior work...
This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover, lidar and image sensor positions generally differ during acquisition,...
In this work, a novel and fast algorithm for real-time 3D body reconstruction from stereo sequences is proposed. The main contributions of this work consist of a novel approach for a statistically guided stereo processing and a data parallel iteration scheme for 3D estimation that includes temporal predecessors from a local spatial neighborhood. A purely GPU based implementation is provided that exhibits...
In this paper, we propose an image prior based on morphological gradients for image recovery. The morphological gradient is defined as the difference between dilation and erosion of an image and approximates the image gradient. This prior provides regularization with an 𝐿1-𝐿∞ norm. The regularization problem with the proposed prior is reduced to a constrained minimization problem and is solved by...
Professional TV studio footage often poses specific challenges to camera calibration due to lack of features and complex camera operation. As available algorithms often fail, we propose a novel approach based on robust tracking of ellipse and line features of a predefined logo. We further devise a predictive and iterative estimation algorithm, which incorporates confidence measures and filtering....
Compressed sensing is a powerful approach to reconstruct high-quality images using a small number of samples. This paper presents a novel compressed sensing method that uses a probabilistic atlas to impose spatial constraints on the reconstruction of brain magnetic resonance imaging (MRI) data. A weighted total variation (TV) model is proposed to characterize the spatial distribution of gradients...
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...
Video databases, used for benchmarking and evaluating the performance of new video technologies, should represent the full breadth of consumer video content. The parameterisation of video databases using low-level features has proven to be an effective way of quantifying the diversity within a database. However, without a comprehensive understanding of the importance and relative frequency and of...
Video quality assessment can be performed by comparing distorted video with the undistorted version by taking human vision system (HVS) into account. A perceptual vision sensitivity model is developed in this paper by systematically integrating visual attention and foveation mechanism into contrast sensitivity function (CSF). The model can accurately estimate a critical frequency beyond which the...
A compressive video microscope based on structured illumination is built. The source-side illumination coding scheme allows the emission photons being collected by the full aperture of the microscope objective, and thus is suitable for the fluorescence readout mode. A block-wise total variation algorithm has been proposed to address the mismatch between the illumination pattern size and the detector...
This paper proposes a novel linear hyperspectral unmixing method based on 𝑙1−𝑙2 sparsity and total variation (TV) regularization. First, the enhanced sparsity based on 𝑙1−𝑙2 norm is explored to depict the intrinsic sparse characteristic of the fractional abundances in sparse regression unmixing model. By taking the correlation between hyperspectral pixels into account, total variation is minimized...
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...
Tubular structure segmentation is an important task, with many applications in medical image analysis such as vessel segmentation both in 2D and 3D. However, this task is challenging due to the spatial sparsity of these objects, implying a high sensitivity to noise. An important cue in this context is the local orientation of the tubular structures. Using this information, it is possible to regularize...
This paper describes an atmospheric lidar photon-limited imaging problem in which observations are contaminated with Poisson noise. The observations are a nonlinear function of two spatially varying physical parameters. The first parameter, called the transmittance, is known to be a bounded monotonic non-increasing function. The second parameter, called the backscatter cross-section, is non-negative...
We propose to incorporate a weighted difference of anisotropic and isotropic total variation (TV) norms into a relaxed formulation of the two phase Mumford-Shah (MS) model for image segmentation. We show results exceeding those obtained by the MS model when using the standard TV norm to regularize partition boundaries. In particular, examples illustrating the qualitative differences between the proposed...
Thanks to the fast development of sensors, it is now possible to acquire sequences of hyperspectral images. Those hyperspectral video sequences (HVS) are particularly suited for the detection and tracking of chemical gas plumes. In this paper, we present a novel gas plume detection method. It is based on the decomposition of the sequence into a low-rank and a sparse term, corresponding to the background...
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