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This paper introduces a novel class of transforms, called graph-based separable transforms (GBSTs), based on two line graphs with optimized weights. For the optimal GBST construction, we formulate a graph learning problem to design two separate line graphs using row-wise and column-wise residual block statistics, respectively. We also analyze the optimality of resulting separable transforms for both...
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...
We address the problem of how to design a more effective co-training scheme to tackle the multi-view spectral clustering. The conventional co-training procedure treats information from all views equally and often converges to a compromised consensus view that does not fully utilize the multiview information. We instead propose to learn an augmented view and construct its corresponding affinity matrix...
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...
Commonly, HoG/SVM classifier uses rectangular images for HoG feature descriptor extraction and training. This means significant additional work has to be done to process irrelevant pixels belonging to the background surrounding the object of interest. While some objects may indeed be square or rectangular, most of objects are not easily representable by simple geometric shapes. In Bitmap-HoG approach...
Image alignment and stitching continue to be the topics of great interest. Image mosaicking is a key application that involves both alignment and stitching of multiple images. Despite significant previous effort, existing methods have limited robustness in dealing with occlusions and local object motion in different captures. To address this issue, we investigate the potential of applying sparsity-based...
This paper presents an efficient constant-time bilateral filter where constant-time means that computational complexity is independent of filter window size. Many state-of-the-art constant-time methods approximate the original bilateral filter by an appropriate combination of a series of convolutions. It is important for this framework to optimize the performance tradeoff between approximate accuracy...
Hyperspectral unmixing is an important technique for identifying the constituent spectra and estimating their corresponding fractions in an image. Nonnegative Matrix Factorization (NMF) has recently been widely used for hyperspectral unmixing. However, due to the complex distribution of hyperspectral data, most existing NMF algorithms cannot adequately reflect the intrinsic relationship of the data...
In this paper, a robust moving camera calibration method is proposed in order to synthesize a free viewpoint soccer video with a high degree of accuracy. The main problem in video registration-based moving camera calibration is that the calibration accuracy is very low if the detected feature points are from moving objects. In order to solve this problem, the proposed method tracks the feature points...
Video analysis often begins with background subtraction, which consists of creation of a background model that allows distinguishing foreground pixels. Recent evaluation of background subtraction techniques demonstrated that there are still considerable challenges facing these methods. Processing per-pixel basis from the background is not only time-consuming but also can dramatically affect foreground...
Annihilating filer-based low rank Hankel matrix (ALOHA) approach was recently proposed as an intrinsic image model for image inpainting estimation. Based on the observation that smoothness or textures within an image patch are represented as sparse spectral components in the frequency domain, ALOHA exploits the existence of annihilating filters and the associated rank-deficient Hankel matrices in...
We propose a deblurring algorithm of point cloud attributes inspired by multi-Wiener SURE-LET deconvolution. The image reconstructed by the SURE-LET approach is expressed as a linear combination of multiple filtered images by the filters defined on the frequency domain. The coefficients of the linear combination are calculated so that the estimate of mean squared error between the original and restored...
We study the classification with respect to the class of curved Mahalanobis metrics that extend the celebrated flat Mahalanobis distances to constant curvature spaces. We prove that these curved Mahalanobis k-NN classifiers define piecewise linear decision boundaries, and report the performance of learning those metrics within the framework of the Large Margin Nearest Neighbor (LMNN). Finally, we...
This paper presents a factorization based active contour model for 2-phase texture segmentation. We utilize the local spectral histogram as the texture features, and then establish a novel energy function based on the theory of the matrix decomposition. Unlike the existing methods, we only choose the combination weights from object region and background region to handle the motion of curve. We compare...
Intrinsic image decomposition is an important topic in computer vision and computer graphics applications. However, this is a challenging problem by adopting the information of a single image. Therefore, additional priors or supplementary information such as multiply images or user interactions are necessary to address this problem. In this paper, we propose a novel scheme to use multiple images for...
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