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To improve the accuracy of color image completion with missing entries, we present a recovery method based on generalized higher-order scalars. We extend the traditional second-order matrix model to a more comprehensive higher-order matrix equivalent, called the "t-matrix" model, which incorporates a pixel neighborhood expansion strategy to characterize the local pixel constraints. This "t-matrix" model is then used to extend some commonly used matrix and tensor completion algorithms to their higher-order versions. We perform extensive experiments on various algorithms using simulated data and publicly available images. The results show that our generalized matrix completion model and the corresponding algorithm compare favorably with their lower-order tensor and conventional matrix counterparts. [ABSTRACT FROM AUTHOR]

We propose a novel expression transfer method based on an analysis of the frequency of multi- expression facial images. We locate the facial features automatically and describe the shape deformations between a neutral expression and non-neutral expressions. The subtle expression changes are important visual clues to distinguish different expressions. These changes are more salient in the frequency domain than in the image domain. We extract the subtle local expression deformations for the source subject, coded in the wavelet decomposition. This information about expressions is transferred to a target subject. The resulting synthesized image preserves both the facial appearance of the target subject and the expression details of the source subject. This method is extended to dynamic expression transfer to allow a more precise interpretation of facial expressions. Experiments on Japanese Female Facial Expression (JAFFE), the extended Cohn-Kanade (CK ) and PIE facial expression databases show the superiority of our method over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Highlights: [•] A feature extraction scheme using two-dimensional random projection, called 2DCS, is proposed. [•] A new classifier, called NCSC (including it fast version NCSC-II), is proposed. [•] Classifiers of NN, NFL and NS are the special cases of NCSC. [•] With tuned parameter, NCSC/NCSC-II outperforms NN, NFL, NS and the Orthonormal ℓ2-norm method. [Copyright &y& Elsevier]

Visible and infrared image registration is required for multi-sensor fusion and cooperative processing. However, traditional single-sensor image registration methods are generally not feasible as multi-sensor images are often loosely related and show different properties in imaging. This paper presents a coarse-to-fine procedure for registering visible and infrared images based on stable region features and edginess. Zernike moments are used to describe salient region features for a coarse registration, and an entropy optimal process based on edginess is used to refine the registration to achieve a more accurate result. Experiments show that the proposed method provides more robust and accurate registration than the existing methods. [ABSTRACT FROM AUTHOR]