Good Features to Correlate for Visual Tracking

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin

Does more intense competition lead to higher growth?

The relationship between the intensity of competition in an economy and its long-run growth is an open question in economics. Theoretically, there is no clear-cut answer. Empirical evidence exists, however, that in some sectors more competition leads to more innovation, and accelerates productivity growth. To complement those findings, and capture economy-wide effects, the authors conduct a cross-country study. They examine the impact on growth of various measures having to do with intensity of domestic competition - beyond the effects of trade liberalization. Their results indicate a strong correlation between long-run growth, and effective enforcement of antitrust, and competition policy.Environmental Economics&Policies,Economic Theory&Research,ICT Policy and Strategies,Labor Policies,Decentralization,Economic Theory&Research,Environmental Economics&Policies,ICT Policy and Strategies,Achieving Shared Growth,Governance Indicators

Iterative Time-Varying Filter Algorithm Based on Discrete Linear Chirp Transform

Denoising of broadband non--stationary signals is a challenging problem in communication systems. In this paper, we introduce a time-varying filter algorithm based on the discrete linear chirp transform (DLCT), which provides local signal decomposition in terms of linear chirps. The method relies on the ability of the DLCT for providing a sparse representation to a wide class of broadband signals. The performance of the proposed algorithm is compared with the discrete fractional Fourier transform (DFrFT) filtering algorithm. Simulation results show that the DLCT algorithm provides better performance than the DFrFT algorithm and consequently achieves high quality filtering.Comment: 6 pages, conference pape

Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters

Segmentation of an object from a video is a challenging task in multimedia applications. Depending on the application, automatic or interactive methods are desired; however, regardless of the application type, efficient computation of video object segmentation is crucial for time-critical applications; specifically, mobile and interactive applications require near real-time efficiencies. In this paper, we address the problem of video segmentation from the perspective of efficiency. We initially redefine the problem of video object segmentation as the propagation of MRF energies along the temporal domain. For this purpose, a novel and efficient method is proposed to propagate MRF energies throughout the frames via bilateral filters without using any global texture, color or shape model. Recently presented bi-exponential filter is utilized for efficiency, whereas a novel technique is also developed to dynamically solve graph-cuts for varying, non-lattice graphs in general linear filtering scenario. These improvements are experimented for both automatic and interactive video segmentation scenarios. Moreover, in addition to the efficiency, segmentation quality is also tested both quantitatively and qualitatively. Indeed, for some challenging examples, significant time efficiency is observed without loss of segmentation quality.Comment: Multimedia, IEEE Transactions on (Volume:16, Issue: 5, Aug. 2014

Analytical solutions of the Klein-Fock-Gordon equation with the Manning-Rosen potential plus a Ring-Shaped like potential

In this work, on the condition that scalar potential is equal to vector potential, the bound state solutions of the Klein-Fock-Gordon equation of the Manning-Rosen plus ring-shaped like potential are obtained by Nikiforov-Uvarov method. The energy levels are worked out and the corresponding normalized eigenfunctions are obtained in terms of orthogonal polynomials for arbitrary $l$ states. The conclusion also contain central Manning-Rosen, central and non-central Hulth\'en potential.Comment: 14 pages. arXiv admin note: substantial text overlap with arXiv:1210.537