Spectral clustering has been used in computer vision successfully in recent years, which refers to the algorithm that the global-optima is found in the relaxed continuous domain obtained by eigendecomposition, and the...
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This paper proposes a new approach to beat detection and prediction in music. Recurrent timing networks are used to detect and predict periodicities in an onset stream and are contained within nodes that compete for s...
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This paper proposes a new approach to beat detection and prediction in music. Recurrent timing networks are used to detect and predict periodicities in an onset stream and are contained within nodes that compete for selection as the best beat hypothesis. Beat prediction nodes perform period self-adjustment to better represent the detected music beat period. The system is tested using a variety of music from different genres and shows promise, in many cases with high correct beat detection percentages.
The probability density function (PDF) optimized quantization has been shown to be more efficient than the conventional quantization methods. In practical application, the data with bounded support can be modelled bet...
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This paper deals about embedding capacity computation for reversible watermarking schemes. The paper proposes a unique way of computing embedding capacity directly from the data set without actually embedding the wate...
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The accurate time measurement is very important of science research and engineering technologies. This paper introduced a new design method which implements the digital TDC (time-to-digital convert) based on FPGA. Thi...
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This paper proposes a new method for building detection and reconstruction from satellite images. In our approach, we propose to use divergence-based centroid neural network to carry out the grouping of 3D line segmen...
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Robust foreground detection is a fundamental precursor of many video processing applications. Although various approaches were advanced, there still exist many factors making detection very challenging: 1) Dynamic bac...
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ISBN:
(纸本)9781424444625
Robust foreground detection is a fundamental precursor of many video processing applications. Although various approaches were advanced, there still exist many factors making detection very challenging: 1) Dynamic background with gradual brightness changes, camera movement and large amount of noises. 2) Sharp illumination changes caused by shadows, light on-off, and so on. 3) Real-time requirement for practical systems. To overcome these problems, a new approach is proposed in this paper. It is based on the background of conventional Gaussian Mixed Model, incorporating tempo-spatial consistency validation to search genuine foreground seeds, so that foreground segments can be reliably acquired using region growth method. Experiments demonstrate that our approach achieves better performance than conventional GMM approach in detection accuracy, adaptability to sudden illumination changes and computation time.
Data and information, these two terms may look quite similar and also sometimes used with the same meaning. But in-depth, these terms are completely different from each other. This paper mainly focuses on this differe...
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