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We present an approach for unsupervised object segmentation in unconstrained videos. Driven by the latest progress in this field, we argue that segmentation performance can be largely improved by aggregating the results generated by state-of-the-art algorithms. Initially, objects in individual frames are estimated through a per-frame aggregation procedure using majority voting. While this can predict...
Dense point trajectories estimation is a challenging yet important problem due to its potential of supporting other fields, such as motion estimation, action recognition, etc. In previous work, dense motion trackers always estimate trajectories based on consecutive frames and ignore scene context prior, thereby suffering from inaccurate estimation. In this paper, we present a novel dense point trajectories...
Video tracking of abrupt motion is a challenging task in computer vision, especially with abrupt scale change. To deal with the problem efficiently, we proposed a novel tracking algorithm based on Markov Chain Monte Carlo sampling method within Bayesian filtering framework. In our tacking scheme, samples were proposed efficiently using the hybrid model of density grid and distance of sub-regions to...
In this paper, an improved recursive least square (RLS) algorithm was proposed to estimate time-varying AR parameters in the presence of noise. Interharmonics signal can be modeled as a nonstationary auto-regressive (AR) model, the spectral estimation of interharmonics signal can be given by the estimated time-varying AR parameters. AR parametric spectral estimation methods have better frequency resolution...
Stochastic sampling based trackers have shown good performance for abrupt motion tracking so that they have gained popularity in recent years. However, the existing methods tend to explore the whole state space uniformly with an inefficiency preliminary sampling phase. In this paper, we propose a nearest neighbor field(NNF) driven stochastic sampling framework for abrupt motion tracking in which NNF...
This paper presents a robust and fully-automatic human motion tracking system without motion priors information using a camera in a fixed location. Bottom-up estimation approaches have recently been applied to such tasks with some success. However, the performance of these approaches is limited by the difficulty of building an effective appearance model. In particular, the appearance model must be...
The aim of the stereo matching is to get the accurate disparity. But for the slanted and low textured plane, the ambiguity is increased. To get the more accurate disparity, most constraints are applied. In this paper, the statistical probability method (SPT) is applied to inferring disparity. The reliable confidence (RC) based on the SPT measures how the pixel disparity is reliable. The unreliable...
Signal processing methods for speech enhancement are of vital interest for many equipment. In particular, beamforming algorithms, which perform spatial filtering to separate signals that have overlapping frequency content but are originated from different spatial locations, are important for a wide range of applications. In this paper, a particular beamforming algorithm based on the signal-to-noise...
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