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We address and compare two new frameworks for neural network (NN) computing-based feature enhanced (FE) fusion of remote sensing (RS) imagery acquired with different coherent radar sensing modalities. Both approaches exploit aggregation of the descriptive experiment design regularization (DEDR) based and the theoretical informatics inspired maximum entropy (ME) regularization paradigms for iterative...
Image annotation is a hard multi-label learning problem which aims at automatically tagging each input image with relevant keywords reflecting its semantic concepts. Recently, several late fusion methods were proposed to improve the accuracy of image annotation. But these late fusion methods need normalization of confidence score vectors of independent models corresponding to distinct representations...
Semi-supervised classification methods try to improve a supervised learned classifier with the help of unlabeled data. In many cases one assumes a certain structure on the data, as for example the manifold assumption, the smoothness assumption or the cluster assumption. Self-training is a method that does not need any assumptions on the data itself. The idea is to use the supervised trained classifier...
The distance set is known to be a versatile local descriptor of shape. As this is simply a set of ordinary distances between sample points on a shape, it is easy to construct and use. More importantly, it remains invariant under many settings and deformations, unlike other typical descriptors. However, in shape matching with distance sets, there is a tradeoff between performance and computational...
Recently sparse representation has been applied to visual tracking by modeling the target appearance using a sparse approximation over the template set. However, this approach is limited by the high computational cost of the ℓ1-norm minimization involved, which also impacts on the amount of particle samples that we can have. This paper introduces a basic constraint on the self-representation of the...
An intuitive approach is proposed for outlier recognition among 2D point correspondences. The main novelty of the proposed method is the exploitation of feature point topology provided by Delaunay triangulation. The solution obtained by minimizing an energy originated from neighboring correspondences in order to remove incorrectly paired points. Assuming local, approximately rigid structures, it is...
Considering the graph of a feature function as an embedded surface in three dimensions is a standard device in computer vision. When multiple feature functions (eg. multiple images) are available, the natural extension of the above concept is to a higher-dimensional embedded surface. This has received surprisingly little attention. In this paper, we advocate for this view by showing the utility of...
Fluctuations in signed distance measurement often reduce the numerical precision of level set methods (LSMs) in image segmentation. Inspired by the split Bregman method for L1-regularization problems, this paper proposes an efficient energy-based level set framework with Bregman divergence reaction to achieve stable and accurate numerical solutions. In this proposed algorithm, the level set and its...
In this paper, we propose a new approach for dense disparity estimation in a global energy minimization framework. We combine the feature matching cost defined using the learned hierarchical features of given left and right stereo images, with the pixel-based intensity matching cost to form the data term. The features are learned in an unsupervised way using the deep deconvolutional network. Our regularization...
We propose a method for understanding a room from a single spherical image, i.e., reconstructing and identifying structural planes forming the ceiling, the floor, and the walls in a room. A spherical image records the light that falls onto a single viewpoint from all directions and does not require correlating geometrical information from multiple images, which facilitates robust and precise reconstruction...
In this paper, we introduce a nonlinear dimensionality reduction (NLDR) technique that can construct a low-dimensional embedding efficiently and accurately with low embedding distortions. The key idea is to divide NLDR into nonlinearity reduction and linear dimensionality reduction, which simplifies the overall NLDR process. Nonlinearity reduction is based on the elastic shell model that measures...
A novel sparse coding framework with unity range codes and the possibility to produce a discriminative dictionary is presented. The framework is, in contrast to many other works, able to handle unsupervised, supervised and semi-supervised settings. Furthermore, codes are constrained to be in unity range, which is beneficial in many scenarios. The paper presents the framework and solvers used to produce...
How to implement an effective factorization for nonrigid structure from motion(NRSFM) has attracted much attention in recent years. Addressing this problem, we propose a novel sequential factorization method without extra priors other than the basis low-rank prior, consisting of a motion estimation module and a 3D shape recovery module. In the motion estimation module, for improving the estimation...
Essential image processing and analysis tasks, such as image segmentation, simplification and denoising, can be conducted in a unified way by minimizing the Mumford-Shah (MS) functional. Although seductive, this minimization is in practice difficult because it requires to jointly define a sharp set of contours and a smooth version of the initial image. For this reason, various relaxations of the original...
In this paper, we propose an algorithm for missing value recovery of visual data such as image or video. These missing values may result from the corruption in acquisition process, or user-specified unexpected outliers. This problem exists in wide range of applications. We use the nuclear norm (NN) regularization to enforce the global consistency of the image, while the total variation (TV) regularization...
Minimization of discrete energy functions considering higher-order potentials is a challenging yet an important problem. In this work, a three-step procedure will be presented and exemplified on a general problem related to the dense depth map computation from multi-view configurations: Achieving a joint reconstruction of structure and semantics with piecewise planarity constraints. The three steps...
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