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We present an approach for detecting moving objects from a dynamic video sequence, using a stereo camera system. The detection of moving objects is a challenging problem, especially when backgrounds are also time-varying due to the concurrent changes of moving objects and backgrounds. Most of the previous approaches have been limited to the use of appearance information such as colors and 2D motions...
This paper proposes a new cost construction method with multiscale Weber (MSW) descriptor and weighted linear regression for robust stereo matching in a two-layer hierarchical structure. Firstly, the MSW descriptors extracted from stereo pairs are utilized to combined raw matching costs to reduce the disparity search range. Secondly, the indispensable matching costs on the subsets of disparity candidates...
Local Binary Descriptors (LBDs) are good at matching image parts, but how much information is actually carried? Surprisingly, this question is usually ignored and replaced by a comparison of matching performances. In this paper, we directly address it by trying to reconstruct plausible images from different LBDs such as BRIEF [4] and FREAK [1]. Using an inverse problem framework, we show that this...
This paper presents a method for feature-based 3D object recognition in cluttered scenes. It deals with the problem of non-uniform sampling density which is inherent in typical range sensing methods. We suggest a method operating on polygonal meshes which overcomes the problem by exploiting surface area in both establishing local frames and creating feature descriptors. The method is able to recognize...
Recently a collaborative representation (CR) based classification with regularized least squares (CRC-RLS) has been proposed for the classification of faces. CRC-RLS is a simple yet fast alternative to sparse representation (SR) based classification (SRC). While SR is the solution to an l1-regularized least square decomposition, CR starts from an l2-regularized least square formulation. In this paper...
We address the limitation of sparse representation based classification with group information for multi-pose face recognition. First, we observe that the key issue of such classification problem lies in the choice of the metric norm of the residual vectors, which represent the fitness of each class. Then we point out that limitation of the current sparse representation classification algorithms is...
This paper presents a spectral correspondence method for fingerprint matching. Minutia matching is formulated as recovering the dense sub-block in the corresponding matrix. And then the spectral correspondence method is used for searching the dense sub-block. First, we propose the pairwise adjacency matrix (PAM), whose diagonal elements represent similarities of minutia structures and other elements...
We present a sparse representation-based method for detecting adventitious lung sounds in low-quality auscultation signals. Since the noise cannot be represented sparsely by any bases, we can extract clear breath sounds and adventitious sounds from noisy electronic auscultation signals via the sparse representation. Using these clear sound components, we measure the level of abnormality, and robustly...
In order to estimate multiple structures without prior knowledge of the noise scale, this paper utilizes Jensen-Shannon Divergence (JSD), which is a similarity measurement method, to represent the relations between pairwise data conceptually. This conceptual representation encompasses the geometrical relations between pairwise data as well as the information about whether pairwise data coexist in...
In this paper, we discuss how the sensors available in modern smartphones can improve 6-degree-of-freedom (6DOF) localization in wide-area environments. In our research, we focus on phones as a platform for large-scale Augmented Reality (AR) applications. Thus, our aim is to estimate the position and orientation of the device accurately and fast — it is unrealistic to assume that users are willing...
Finding correspondences between two 3D shapes is common both in computer vision and computer graphics. In this paper, we propose a general framework that shows how to build correspondences by utilizing the isometric property. We show that the problem of finding such correspondences can be reduced to the problem of spectral assignment, which can be solved by finding the principal eigenvector of the...
Accurate segmentation provides a useful contour constraint to alleviate drifting during online learning for tracking. Towards this end, we present a closed-loop method for object tracking that links Hough forests and alpha matting via an effective back-projection scheme for patches. A novel hybrid-Hough-forests-based method first estimates object location. Given the object location, the trimap of...
Calibrating hand-eye geometry is often based on explicit feature correspondences. This article presents an alternative method that uses the apparent flow induced by the motion of the camera to achieve self-calibration. To make the method more robust against noise, the strategy is to use the orientation of the normal flow field which is more noise-immune, to recover first the direction component of...
The main issue in current algorithms for the detection of single-frame defects like dust, dirt and blotches in archived film is the significant number of false alarms due to motion compensation errors and film grain. This typically leads to disturbing artifacts occurring in the subsequent defect removal process. We propose a novel algorithm for the detection of single-frame defects which addresses...
Local Binary Patterns (LBPs) and Covariance Matrices (CovMs) are two popular kinds of texture descriptors. However, local correlation brought by LBPs and global correlation brought by CovMs could not be directly combined to achieve enhanced discriminative power. This paper develops a powerful descriptor, named COV-LBP. Firstly, we propose a variant of LBPs on Euclidean space, named the LBP Difference...
This work introduces a new representation for Motion Capture data (MoCap) that is invariant under rigid transformation and robust for classification and annotation of MoCap data. This representation relies on distance matrices that fully characterize the class of identical postures up to the body position or orientation. This high dimensional feature descriptor is tailored using PCA and incorporated...
We present a new split-type algorithm for the minimization of a p-harmonic energy with added data fidelity term. The half-quadratic splitting reduces the original problem to two straightforward problems, that can be minimized efficiently. The minimizers to the two sub-problems can typically be computed pointwise and are easily implemented on massively parallel processors. Furthermore the splitting...
We present an efficient algorithm that computes the relative pose between two calibrated views given that the rotation is around a single axis. The algorithm is suited for indoor and urban environments that have an abundance of orthogonal lines. We also present a framework in which this algorithm is used within a hypothesize-and-test framework to simultaneously detect orthogonal lines and compute...
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