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The practice of using divide and conquer techniques to solve complex, time-consuming problems has been in use for a very long time. Here we evaluate the performance of centroid-based clustering techniques, specifically k-means and its two approximation algorithms, the k-means++ and k-means|| (also known as Scalable k-means++), as divide and conquer paradigms applied for the creation of minimum spanning...
The method of laser beam angular divergence measurement with the help of Quadra copter is suggested. Assumption of Gaussian angular distribution of power density was used. Experimental dates are presented. Theoretical estimations of proposed methods are confirmed by experimental results.
This paper evaluates the performance of the routing protocols HWMP, Babel and B.A.T.M.A.N. advanced for disaster networks. The evaluation is performed using a virtual environment so that the obtained results are similar to the expectations of a real world testbed. According to the specific requirements in disaster situations, three different scenario categories are implemented. The focus of the scenarios...
Personal emergency response systems (PERS) such as Philips Lifeline help seniors maintain independence and age in place. PERS can use predictive analytics to help risk stratification and promote response-efficient emergency services. This paper presents a framework for estimating significant associations between Lifeline user characteristics and occurrence of emergency events. Predictive variables...
Video coding has become widespread through mobile devices. At the same time, the adopted resolutions have been enlarged, demanding more coding efficiency and motivating the development of the new state-of-the-art standard, High Efficiency Video Coding (HEVC). However, to achieve the required efficiency the new standard greatly increased the computational intensity. That, allied to real-time constraints...
Orthogonal frequency division multiple (OFDM) has been introduced into long term evolution (LTE) because of its high spectral efficiency and robust anti-multipath fading ability. However, a major drawback of OFDM signals is high fluctuations of signal envelope. Peak-to-average power ratio (PAPR) is a well-known measure for the envelope fluctuations. Recently, another metric named cubic metric (CM)...
The prevalence of bridging defects makes bridging fault models important to consider during fault simulation and test generation. The large number of bridging faults that can be defined for a circuit led to the development of procedures for selecting subsets of bridging faults that are likely to occur based on the circuit layout, and hard-to-detect bridging faults whose coverage provides a more effective...
In this paper, we present CORT, a factored concolic execution based methodology for high-level functional test generation. Our test generation effort is visualized as the systematic unraveling of the control-flow response of the design over multiple explorations. We begin by transforming the Register Transfer Level (RTL) source for the design into a high-performance C++ compiled functional simulator...
Understanding where people look in images is an important problem in computer vision. Despite significant research, it remains unclear to what extent human fixations can be predicted by low-level (contrast) compared to highlevel (presence of objects) image features. Here we address this problem by introducing two novel models that use different feature spaces but the same readout architecture. The...
Estimating a depth map from multiple views of a scene is a fundamental task in computer vision. As soon as more than two viewpoints are available, one faces the very basic question how to measure similarity across >2 image patches. Surprisingly, no direct solution exists, instead it is common to fall back to more or less robust averaging of two-view similarities. Encouraged by the success of machine...
We study the problem of single-image depth estimation for images in the wild. We collect human annotated surface normals and use them to help train a neural network that directly predicts pixel-wise depth. We propose two novel loss functions for training with surface normal annotations. Experiments on NYU Depth, KITTI, and our own dataset demonstrate that our approach can significantly improve the...
Person re-identification is an important task in video surveillance systems. It can be formally defined as establishing the correspondence between images of a person taken from different cameras at different times. In this paper, we present a two stream convolutional neural network where each stream is a Siamese network. This architecture can learn spatial and temporal information separately. We also...
The intensive annotation cost and the rich but unlabeled data contained in videos motivate us to propose an unsupervised video-based person re-identification (re-ID) method. We start from two assumptions: 1) different video tracklets typically contain different persons, given that the tracklets are taken at distinct places or with long intervals; 2) within each tracklet, the frames are mostly of the...
What is the right way to reason about human activities? What directions forward are most promising? In this work, we analyze the current state of human activity understanding in videos. The goal of this paper is to examine datasets, evaluation metrics, algorithms, and potential future directions. We look at the qualitative attributes that define activities such as pose variability, brevity, and density...
Automatic image aesthetics rating has received a growing interest with the recent breakthrough in deep learning. Although many studies exist for learning a generic or universal aesthetics model, investigation of aesthetics models incorporating individual user’s preference is quite limited. We address this personalized aesthetics problem by showing that individual’s aesthetic preferences exhibit strong...
Person re-identification is a challenge in video-based surveillance where the goal is to identify the same person in different camera views. In recent years, many algorithms have been proposed that approach this problem by designing suitable feature representations for images of persons or by training appropriate distance metrics that learn to distinguish between images of different persons. Aggregating...
While metric learning is important for Person reidentification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training. However, this limits their scalabilities to realistic applications, in which a large amount...
Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of curating diverse large-scale video datasets. This paper addresses both of those challenges, through an image to video feature-level domain adaptation...
Nowadays, life unfolds in a digitised world, in which, each person can have access to a huge amount of information through the use of Internet. In this situation, most of daily activities are being influenced by a new kind of society that allows ubiquitous and instantaneous interaction among its members. The creation of social platforms (SPs) has strengthened human relationships at such point that...
This paper proposes a new 3D video quality assessment based on 3D visual perception for texture and depth image for measuring the quality of stereoscopic 3D videos by detecting 3D distortions. The effectiveness of the proposed metrics is verified by conducting subjective evaluations on publicly available stereoscopic image databases. Experimental results show that significant consistency can be reached...
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