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Shock filters and related tools, like coherence-enhancing filters, are popular methods for denoising and creating artistic effects. They iteratively apply morphological operators with a constant structuring element. We propose in this article to improve the original shock filtering scheme using smoothed local histograms. Our method exhibits better performance and control of the erosion and dilation...
In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. We first present the definition of extended ideal kernel for both labeled and unlabeled data of multiple classes. Based on this extended ideal kernel, we propose an ideal regularization which is a linear function of the kernel matrix to be learned. The ideal regularization...
In this paper, we propose a 3D model retrieval system using sketch queries. The 3D model is described by a single characteristic view with the biggest exposure opportunity. The sketch query and the characteristic view are divided into several parts. Each part is quantized into a Fourier descriptor, and the spatial arrangements of these parts are measured by the graph spectra. Our contributions are...
In this work, we introduce an alternative measurement to evaluate quality of flow field. We then present an alternative filter called OA-filter (Occlusion-Aware filter) which uses this measurement. It is integrated inside the solver to push the performance of the model in terms of accuracy and sharpness. The experiments show that the estimation result has been significantly improved. The method yields...
Recently, l1 graph based analysis using sparse representation has received much attention in pattern recognition and related communities. In this paper, motivated by the success of l1 graph in dimensionality reduction, we extend it to feature selection and propose a novel filter-type method called Sparsity Score (SS) which ranks features according to their respective sparsity preserving capability...
In many real-world applications, different mis-classification errors will cause different costs. However, cost-sensitive learning only applied in classification phase and not in the feature selection phase to address this problem. In this paper, we study cost-sensitive feature selection and propose a framework which incorporates a cost matrix into traditional feature selection methods. And we developed...
Semi-supervised learning is important when labeled data are scarce. In this paper, we develop a novel semi-supervised spectral feature selection technique using label regression and by using l\-norm regularized models for subset selection. Specifically, we propose a new two-step spectral regression technique for semi-supervised feature selection. In the first step, we use label propagation and label...
This paper presents a novel analysis and application of the eigensystem of the edge-based Laplacian of a graph. The advantage of using the edge-based Lapla-cian over its vertex-based counterpart is that it significantly expands the set of differential operators that can be implemented in the graph domain. We use the analysis to develop a novel method for defining poseinvariant signatures for non-rigid...
Statistical mixtures such as Rayleigh, Wishart or Gaussian mixture models are commonly used in pattern recognition and signal processing tasks. Since the Kullback-Leibler divergence between any two such mixture models does not admit an analytical expression, the relative entropy can only be approximated numerically using time-consuming Monte-Carlo stochastic sampling. This drawback has motivated the...
Most existing feature selection methods focus on ranking individual features based on a utility criterion, which neglecting the correlations among features. To overcome this problem, we develop a novel feature selection technique using the spectral data transformation and by using l1-norm regularized models for subset selection. Specifically, we propose a new two-step spectral regression technique...
The aim of this paper is to introduce a classical method of pattern recognition as the solution for the medical imaging, and to provide a new angle of using the pattern recognition theory for MEG source reconstruction. We explore a new method of MEG source spatio-temporal reconstruction based on modeling the neural source with extended basis functions. Inspired by the graph theory that Laplacian eigenvectors...
This paper proposes a corner and skeleton based method for arbitrarily oriented text detection. By calculating the minimum moment of inertia of each candidate text region, we firstly obtain the orientation and minimum bounding box of each connected component. Secondly, based on the fact that corners are frequent and essential patterns in text regions, we propose a geodesic distance between corner...
This paper addresses the problem of 3D tracking deformable surfaces undergoing non-rigid motion from multi-view video sequences. We propose a method that starts from a set of feature points which have been matched across views and time. A data-driven motion propagation technique makes the motion dense enough to give initial guess to the parameter estimation of the vertices of the 3D model. Finally...
During the image placement onto the compositing surface (mosaic), stitching algorithms try to minimize visual inconsistencies (texture discontinuities), seam induced color gradients, and blurry image regions. These problems are classically processed separately. In this contribution, we describe a two step graph-cut algorithm that combines these issues. In the first step, optimal seam locations are...
This paper presents a new approach to image-thresholding-based segmentation. It considerably improves existing methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions. The proposed approach seamlessly: 1) extends the Otsu's method to arbitrary numbers of thresholds and 2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal...
This paper provides a generic framework of component analysis (CA) methods introducing a new expression for scatter matrices and Gram matrices, called Generalized Pairwise Expression (GPE). This expression is quite compact but highly powerful: The framework includes not only (1) the standard CA methods but also (2) several regularization techniques, (3) weighted extensions, (4) some clustering methods,...
In this paper, we study and evaluate the application to image segmentation of a p-Laplacian based relaxation of the Cheeger Cut problem. Based on a l1 relaxation of the initial clustering problem, we show that these methods can outperform usual well-known graph based approaches, e.g., min-cut/max-flow algorithm or l2 spectral clustering, for unsupervised and very weakly supervised image segmentation...
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