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We present a method for separating background and foreground optical flow fields induced by observer's egomotion and motion of objects, respectively. Optical flow is a vector field of instantaneous apparent motion computed from successive images. An optical flow field can be assumed as a linear combination with a few basis fields caused by translational and rotational egomotion and a spatially sparse...
Toward assistance of respiratory system diagnosis, sparse representation of auscultation signals is utilized to extract pulmonary sound components. This signal extraction is a challenging task because the pulmonary sounds such as vesicular sounds and crackles are overlapping each other in the time and frequency domains, and they are so faint that the quality of recorded signals is quite low in many...
We present a sparse representation-based method for detecting adventitious lung sounds in low-quality auscultation signals. Since the noise cannot be represented sparsely by any bases, we can extract clear breath sounds and adventitious sounds from noisy electronic auscultation signals via the sparse representation. Using these clear sound components, we measure the level of abnormality, and robustly...
We propose a randomized algorithm of spectral clustering and apply it to appearance-based image/video segmentation. Spectral clustering is a kernel-based method of grouping data on separate nonlinear manifolds. However, its high computational expensive restricts the applications. Our algorithm exploits random projection and subsampling techniques for reducing dimensionality and cardinality of data...
A robust classification method is developed on the basis of sparse subspace decomposition. This method tries to decompose a mixture of subspaces of unlabeled data (queries) into class subspaces as few as possible. Each query is classified into the class whose subspace significantly contributes to the decomposed subspace. Multiple queries from different classes can be simultaneously classified into...
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