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In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR-based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction...
Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multi-label. The proposed approach is characterized by simultaneously...
In real world, an image is usually associated with multiple labels which are characterized by different regions in the image. Thus image classification is naturally posed as both a multi-label learning and multi-instance learning problem. Different from existing research which has considered these two problems separately, we propose an integrated multi- label multi-instance learning (MLMIL) approach...
This paper introduces a bilinear model to analyze and transfer expression or identity of 3D faces, and its applications in 3D and 2D areas. Our aim is to separate identity and expression factors of face data into two independent linear subspaces. First all the data are proceeded to have vertex-to-vertex correspondences. We build a morphable face model to transform these dense-vertex data into a low-dimension...
This paper proposes a method for acquiring face depth information directly from near infrared (NIR) images, using statistical learning. To perform such learning, ground truth NIR images and range data are captured. A method of alignment between the two image modalities is proposed. By constructing the low dimensional face subspaces of NIR images and depth maps, the raw data are projected into respective...
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