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In this manuscript, we propose an automatic sketch synthesis algorithm based on embedded hidden Markov model (E-HMM) and selective ensemble strategy. The E-HMM is used to model the nonlinear relationship between a photo-sketch pair firstly, and then a series of pseudo-sketches, which are generated based on several learned models for a given photo, are(More)
This paper aims to address the face recognition problem with a wide variety of views. We proposed a tensor subspace analysis and view manifold modeling based multi-view face recognition algorithm by improving the TensorFace based one. Tensor subspace analysis is applied to separate the identity and view information of multi-view face images. To model the(More)
This paper presents a new visual tracking framework based on an adaptive color attention tuned local sparse model. The histograms of sparse coefficients of all patches in an object are pooled together according to their spatial distribution. A particle filter methodology is used as the location model to predict candidates for object verification during(More)
Face images under uncontrolled environments suffer from the changes of multiple factors such as camera view, illumination, expression, etc. Tensor analysis provides a way of analyzing the influence of different factors on facial variation. However, the TensorFace model creates a difficulty in representing the nonlinearity of view subspace. In this paper, to(More)
We discuss a new multi-view face recognition method that extends a recently proposed nonlinear tensor decomposition technique. We use this technique to provide a generative face model that can deal with both the linearity and nonlinearity in multi-view face images. Particularly, we study the effectiveness of three kinds of view manifold for multi-view face(More)
Compressive sensing(CS) has been exploited for hype-spectral image(HSI) compression in recent years. Though it can greatly reduce the costs of computation and storage, the reconstruction of HSI from a few linear measurements is challenging. The underlying sparsity of HSI is crucial to improve the reconstruction accuracy. However, the sparsity of HSI is(More)
Hyperspectral compressive sensing (HCS) can greatly reduce the enormous cost of hyperspectral images (HSIs) on imaging, storage, and transmission by only collecting a few compressive measurements in the image acquisition. One of the most challenging problems for HCS is how to reconstruct the HSI accurately from such a few measurements. It has been proved(More)