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We propose a novel depth maps refinement algorithm and generate multi-view video sequences from two-view video sequences for modern autostereoscopic display. In order to generate realistic contents for virtual views, high-quality depth maps are very critical to the view synthesis results. We propose an iterative depth refinement approach of a joint error detection and correction algorithm to refine...
In this paper, we propose a robust algorithm for 3D object pose estimation from a single 2D image. The proposed pose estimation algorithm is based on modifying the traditional image projection error function to a sum of squared image projection errors weighted by their associated distances. By using an Euler angle representation, we formulate the energy minimization for the pose estimation problem...
In this paper, we propose a novel algorithm to generate multiple virtual views from a video-plus-depth sequence for modern autostereoscopic displays. To synthesize realistic content in the disocclusion regions at the virtual views is the main challenging problem for this task. Spatial coherence and temporal consistency are the two key factors to produce perceptually satisfactory virtual images. The...
The paper proposes a robust image alignment framework based on Fourier transform of a gradient complex image. The proposed Fourier-based algorithm can handle translation, rotation, and scaling, and it is robust against noise and non-uniform illumination. The proposed alignment algorithm is further extended to work under occlusion by partitioning the template and performing the Fourier-based alignment...
The video-plus-depth format has been widely used for representing the 3D scene due to its main advantage of compatibility to image format. In practice, the depth inconsistency may lead to unsatisfactory view synthesis results. In this paper, we propose a new structure-from-motion (SfM) technique, called locally temporal bundle adjustment (LTBA), to handle the dynamic scenes as well as the static camera...
Robustness is an essential issue to computer vision and pattern recognition in developing multimedia applications. In this work, we present a robust kernel approach that is highly robust against random noises and intra-class deformations. By incorporating the robust error function used in robust statistics together with a deformation-invariant distance measure, the derived robust kernel is shown to...
Robustness is one of the most critical issues in the appearance-based learning strategies. In this work, we propose a novel kernel that is robust against data corruption for various visual learning problems. By incorporating a robust rho-function to relieve the influence of outliers, the proposed kernel is shown to be robust against various types of outliers. By incorporating the proposed kernel into...
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