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Abstract: In this paper, we present a kernel-based eigentransformation framework to hallucinate the high-resolution (HR) facial image of a low-resolution (LR) input. The eigentransformation method is a linear subspace approach, which represents an image as a linear combination of training samples. Consequently, those novel facial appearances not included in the training samples cannot be super-resolved properly. To solve this problem, we devise a kernel-based extension of the eigentransformation method, which takes higher-order statistics of the image data into account. To generate HR face images with higher fidelity, the HR face image reconstructed using this kernel-based eigentransformation method is treated as an initial estimation of the target HR face. The corresponding high-frequency components of this estimation are extracted to form a prior in the maximum a posteriori (MAP) formulation of the SR problem so as to derive the final reconstruction result. We have evaluated our proposed method using different kernels and configurations, and have compared these performances with some current SR algorithms. Experimental results show that our kernel-based framework, along with a proper kernel, can produce good HR facial images in terms of both visual quality and reconstruction errors. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we describe a video-object segmentation and 3D-trajectory estimation method for the analysis of dynamic scenes from a monocular uncalibrated view. Based on the color and motion information among video frames, our proposed method segments the scene, calibrates the camera, and calculates the 3D trajectories of moving objects. It can be employed for video-object segmentation, 2D-to-3D video conversion, video-object retrieval, etc. In our method, reliable 2D feature motions are established by comparing SIFT descriptors among successive frames, and image over-segmentation is achieved using a graph-based method. Then, the 2D motions and the segmentation result iteratively refine each other in a hierarchically structured framework to achieve video-object segmentation. Finally, the 3D trajectories of the segmented moving objects are estimated based on a local constant-velocity constraint, and are refined by a Hidden Markov Model (HMM)-based algorithm. Experiments show that the proposed framework can achieve a good performance in terms of both object segmentation and 3D-trajectory estimation. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an effective image magnification algorithm based on an adaptive Markov random field (MRF) model with a Bayesian framework is proposed. A low-resolution (LR) image is first magnified to form a high-resolution (HR) image using a fractal-based method, namely the multiple partitioned iterated function system (MPIFS). The quality of this magnified HR image is then improved by means of a blockwise adaptive MRF model using the Bayesian ‘maximum a posteriori’ (MAP) approach. We propose an efficient parameter estimation method for the MRF model such that the staircase artifact will be reduced in the HR image. Experimental results show that, when compared to the conventional MRF model, which uses a fixed set of parameters for a whole image, our algorithm can provide a magnified image with the well-preserved edges and texture, and can achieve a better PSNR and visual quality. [Copyright &y& Elsevier]

This paper addresses the problem of estimating high-resolution (HR) facial images from a single low-resolution (LR) input. We assume that the input LR and estimated HR images are under the same view-point and illumination condition, i.e. the setting of image super-resolution. At the core of our techniques is that the facial images can be decomposed as a texture vector, characterized in terms of the appearance, and a shape vector, characterized in terms of the geometry variations. This enables a two-stage successive estimation framework that is geometry aware and obviates the needs in sophisticated optimizations. In particular, the proposed technique first solves for appearance of the HR faces form the correspondence derived between an interpolated LR face and its corresponding HR face. Given the texture of the HR faces, we incorporate optical flow to solve the local structure at sub-pixel level for the HR faces; here, we use additional geometry inspired priors to further regularize the solution. Experimental results show that our method outperforms other state-of-the-art methods in terms of retaining the facial-feature shape and the estimation of novel features. [ABSTRACT FROM AUTHOR]