Sharif Digital Repository

In this paper, a semi-supervised graph-based method for estimating 3D body pose from a sequence of silhouettes, is presented. The performance of graph-based methods is highly dependent on the quality of the constructed graph. In the case of the human pose estimation problem, the missing depth information from silhouettes intensifies the occurrence of shortcut edges within the graph. To identify and remove these shortcut edges, we measure the similarity of each pair of connected vertices through the use of sliding temporal windows. Furthermore, by exploiting the relationships between labeled and unlabeled data, the proposed method can estimate the 3D body poses, with a small set of labeled... 

Give a triangulation of a set of points on the plane, dilation of any two points is defined as the ratio between the length of the shortest path of these points and their Euclidean distance. Minimum dilation triangulation is a triangulation in which the maximum dilation between any pair of the points is minimized. We give upper bounds on the dilation of the minimum dilation triangulation for two kinds of point sets: An upper bound of nsin⁡(π/n)/2 for a centrally symmetric convex point set containing n points, and an upper bound of 1.19098 for a set of points on the boundary of a semicircle. © 2018 Elsevier B.V  

A geometric spanner on a point set is a sparse graph that approximates the Euclidean distances between all pairs of points in the point set. Here, we intend to construct a geometric spanner for a massive point set, using a distributed algorithm on parallel machines. In particular, we use the MapReduce model of computation to construct spanners in several rounds with inter-communications in between. An algorithm in this model is called efficient if it uses a sublinear number of machines and runs in a polylogarithmic number of rounds. In this paper, we propose an efficient MapReduce algorithm for constructing a geometric spanner in a constant number of rounds, using linear amount of... 

Consider a scenario in which several agents are located in the Euclidean space, and the agents want to create a network in which everyone has fast access to all or some other agents. Geometric t-spanners are examples of such a network providing fast connections between the nodes of the network for some fixed value t, i.e. the length of the shortest path between any two nodes in the network is at most t times their Euclidean distance. Geometric t-spanners have been extensively studied in the area of computational geometry where they are created by a central authority. In this paper, we investigate a situation in which selfish agents want to create such a network in the absence of a central... 

In this paper, a multiple metric learning scheme for human pose estimation from a single image is proposed. Here, we focused on a big challenge of this problem which is; different 3D poses might correspond to similar inputs. To address this ambiguity, some Euclidean distance based approaches use prior knowledge or pose model that can work properly, provided that the model parameters are being estimated accurately. In the proposed method, the manifold of data is divided into several clusters and then, we learn a new metric for each partition by utilizing not only input features, but also their corresponding poses. The manifold clustering allows the decomposition of multiple manifolds into a... 

In this paper, we suggest a general model for the fixed-valued impulse noise and propose a two-stage method for high density noise suppression while preserving the image details. In the first stage, we apply an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employ an Adaptive Iterative Mean filter to restore them. The filter is adaptive in terms of the number of iterations, which is different for each noisy pixel, according to the Euclidean distance from the nearest uncorrupted pixel. Experimental results show that the proposed filter is fast and outperforms the best existing techniques in both objective and subjective performance measures... 

Discriminative approaches to human pose estimation have became popular in recent years. These approaches face a big challenge: Similar inputs might correspond to very dissimilar poses. This property misleads the mapping functions which rely on the Euclidean distances in the input space. In this paper, we use the distances between the labels of the training data to learn a metric and map the input data to a space where this problem is minimized. Our mapping is linear and hence preserves the manifold structure of the input data. We benefit from the unlabeled data to estimate this manifold in the new space as a nearest neighbor graph. We finally utilize Tikhonov regularization to find a smooth... 

Semi-supervised learning based on manifolds has been the focus of extensive research in recent years. Convenient neighbourhood graph construction is a key component of a successful semi-supervised classification method. Previous graph construction methods fail when there are pairs of data points that have small Euclidean distance, but are far apart over the manifold. To overcome this problem, we start with an arbitrary neighbourhood graph and iteratively update the edge weights by using the estimates of the geodesic distances between points. Moreover, we provide theoretical bounds on the values of estimated geodesic distances. Experimental results on real-world data show significant... 

Given a triangulation of a point set on the plane, dilation of any pair of the points is the ratio of their shortest path length to their Euclidean distance. The maximum dilation over all pairs of points is called the dilation of this triangulation. Minimum dilation triangulation problem seeks a triangulation with the least possible dilation of an input point set. For this problem no polynomial time algorithm is known. We present an exact algorithm based on a branch and bound method for finding minimum dilation triangulations. This deterministic algorithm after generating an initial solution, iteratively computes a lower bound for the answer and then applies a branch and bound method to find... 

This paper proposes distributed optimal attitude consensus control for single-integrator multi rigid bodies with undirected network evolving on Special Orthogonal Group SO(3) while simultaneously guarantees the connectivity preservation property for agents using descent gradient algorithm. Since by Use of the Euclidean distance on Lie group as a measure of the energy of the state does not define and preserve the topology of SO(3); besides, solving the Hamilton–Jacobi–Bellman equation in optimal control problems shows difficulty implementing Euclidean distances and limits the results for SO(3) configuration state spaces. As a result, in this paper, the generic distance on SO(3) associated to... 

A novel low-complexity detection scheme is proposed for the multiple-input multiple-output (MIMO) single-carrier frequency division-multiple access (SC-FDMA) systems, which is suitable for ASIC implementations. The proposed detection scheme makes an initial estimate of the transmitted signal based on a minimum mean square error (MMSE) frequency domain equalizer (FDE) detector and finds symbols with higher error probability among them and browse more candidates for them in the constellation to improve their initial estimate. Based on this approach, architecture is introduced that achieves superior bit error rate (BER) performance compared to the conventional MMSE FDE. The performance of the...