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The most efficient signal edge-preserving smoothing filters, e.g., for denoising, are non-linear. Thus, their acceleration is challenging and is often done in practice by tuning filters parameters, such as increasing the width of the local smoothing neighborhood, resulting in more aggressive smoothing of a single sweep at the cost of increased edge blurring. We propose an alternative technology, accelerating the original filters without tuning, by running them through a conjugate gradient method, not affecting their quality.


Key-frame extraction has been a focused problem of human action recognition due to its effectiveness, efficiency and importance in action understanding. According to the characteristics of human motion perception, this paper proposes a new key-frame extraction framework based on motion change points. And in order to detect motion change points robustly, a hysteresis extrema seeking algorithm has been developed. Experimental results have demonstrated the good performance of the proposed methods.


We propose signal reconstruction algorithms which utilize a guiding subspace that represents desired properties of reconstructed signals. Optimal reconstructed signals are shown to belong to a convex bounded set, called the ``reconstruction'' set. Iterative reconstruction algorithms, based on conjugate gradient methods, are developed to approximate optimal reconstructions with low memory and computational costs. Effectiveness of the proposed method is demonstrated with an application to image magnification.