Activity recognition from videos with parallel hypergraph matching on GPUs

In this paper, we propose a method for activity recognition from videos based on sparse local features and hypergraph matching. We benefit from special properties of the temporal domain in the data to derive a sequential and fast graph matching algorithm for GPUs. Traditionally, graphs and hypergraphs are frequently used to recognize complex and often non-rigid patterns in computer vision, either through graph matching or point-set matching with graphs. Most formulations resort to the minimization of a difficult discrete energy function mixing geometric or structural terms with data attached terms involving appearance features. Traditional methods solve this minimization problem approximately, for instance with spectral techniques. In this work, instead of solving the problem approximatively, the exact solution for the optimal assignment is calculated in parallel on GPUs. The graphical structure is simplified and regularized, which allows to derive an efficient recursive minimization algorithm. The algorithm distributes subproblems over the calculation units of a GPU, which solves them in parallel, allowing the system to run faster than real-time on medium-end GPUs

Removal of the phase noise in the autocorrelation estimates with data windowing

13th European Signal Processing Conference, EUSIPCO 2005; Antalya; Turkey; 4 September 2005 through 8 September 2005The sinusoidal frequency estimation from short data records based on Toeplitz autocorrelation (AC) matrix estimates suffer from phase noise. This effect becomes prominent especially when additive noise vanishes becoming a nuisance, that is at high signal-to-noise ratios (SNR). Based on both analytic derivation of the AC lag terms and simulation experiments, we show that data windowing can mitigate the limitations caused by the phase noise. Thus with proper windowing, the variance of the frequency estimate is no more limited by phase noise, but it continues to decrease linearly with the SNR. The cases of the Pisarenko frequency estimator and of MUSIC, both for the single sinusoid case, are analyzed in detail

Phase noise mitigation in the autocorrelation estimates with data windowing: The case of two close sinusoids

14th European Signal Processing Conference, EUSIPCO 2006; Florence; Italy; 4 September 2006 through 8 September 2006We address the phase noise and the superresolution problem in Toeplitz matrix-based spectral estimates. The Toeplitz autocorrelation (AC) matrix approach in spectral estimation brings in an order of magnitude computational advantage while the price paid is the phase noise that becomes effective at high signal-to-noise ratios (SNR). This noise can be mitigated with windowing the data though some concomitant loss in resolution occurs. The trade-offs between additive noise SNR, resolvability of sinusoids closer than the resolution limit, and behavior of the estimated AC lags and tone frequencies are investigated

A Novel System for Content-Based Retrieval of Single and Multi-Label High-Dimensional Remote Sensing Images

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a novel content-based remote sensing (RS) image retrieval system that consists of the following. First, an image description method that characterizes both spatial and spectral information content of RS images. Second, a supervised retrieval method that efficiently models and exploits the sparsity of RS image descriptors. The proposed image description method characterizes the spectral content by three different novel spectral descriptors that are: raw pixel values, simple bag of spectral values and the extended bag of spectral values descriptors. To model the spatial content of RS images, we consider the well-known scale invariant feature transform-based bag of visual words approach. With the conjunction of the spatial and the spectral descriptors, RS image retrieval is achieved by a novel sparse reconstruction-based RS image retrieval method. The proposed method considers a novel measure of label likelihood in the framework of sparse reconstruction-based classifiers and generalizes the original sparse classifier to the case both single-label and multi-label RS image retrieval problems. Finally, to enhance retrieval performance, we introduce a strategy to exploit the sensitivity of the sparse reconstruction-based method to different dictionary words. Experimental results obtained on two benchmark archives show the effectiveness of the proposed system.EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEart

Bosphorus database for 3d face analysis

Abstract. A new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions is presented in this paper. This database is unique from three aspects: i) the facial expressions are composed of judiciously selected subset of Action Units as well as the six basic emotions, and many actors/actresses are incorporated to obtain more realistic expression data; ii) a rich set of head pose variations are available; and iii) different types of face occlusions are included. Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis. 1

Adaptive Langevin Sampler for Separation of t-Distribution Modelled Astrophysical Maps

We propose to model the image differentials of astrophysical source maps by Student's t-distribution and to use them in the Bayesian source separation method as priors. We introduce an efficient Markov Chain Monte Carlo (MCMC) sampling scheme to unmix the astrophysical sources and describe the derivation details. In this scheme, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and reduces the computation time significantly (by two orders of magnitude). In addition, Student's t-distribution parameters are updated throughout the iterations. The results on astrophysical source separation are assessed with two performance criteria defined in the pixel and the frequency domains.Comment: 12 pages, 6 figure