盒式录像机中的画中画系统

本文提出了一种使用双端存储器、可同时显示磁带和电视广播图象的新的画中画系统.     

Cutting and Pasting Irregularly Shaped Patches for Texture Synthesis

This paper proposes a patch‐based texture synthesis approach that cuts and stitches irregularly shaped texture patches to generate new texture images with minimized visual discontinuity. It works well on a wide range of textures. A semiautomatic algorithm is developed to obtain the irregularly shaped patches. To synthesize strictly structured textures, a regular pasting method is proposed to identify the texture structures and subsequently position the irregularly shaped patches according to the identified structures. The results and comparisons with related work are given.     

Video Panorama for 2D to 3D Conversion

Accurate depth estimation is a challenging, yet essential step in the conversion of a 2D image sequence to a 3D stereo sequence. We present a novel approach to construct a temporally coherent depth map for each image in a sequence. The quality of the estimated depth is high enough for the purpose of2D to 3D stereo conversion. Our approach first combines the video sequence into a panoramic image. A user can scribble on this single panoramic image to specify depth information. The depth is then propagated to the remainder of the panoramic image. This depth map is then remapped to the original sequence and used as the initial guess for each individual depth map in the sequence. Our approach greatly simplifies the required user interaction during the assignment of the depth and allows for relatively free camera movement during the generation of a panoramic image. We demonstrate the effectiveness of our method by showing stereo converted sequences with various camera motions.     

Time‐Series Plots Integrated in Parallel‐Coordinates Displays

We present a natural extension of two‐dimensional parallel‐coordinates plots for revealing relationships in time‐dependent multi‐attribute data by building on the idea that time can be considered as the third dimension. A time slice through the visualization represents a certain point in time and can be viewed as a regular parallel‐coordinates display. A vertical slice through one of the axes of the parallel‐coordinates display would show a time‐series plot. For a focus‐and‐context Integration of both views, we embed time‐series plots between two adjacent axes of the parallel‐coordinates plot. Both time‐series plots are drawn using a pseudo three‐dimensional perspective with a single vanishing point. An independent parallel‐coordinates panel that connects the two perspectively displayed time‐series plots can move forward and backward in time to reveal changes in the relationship between the time‐dependent attributes. The visualization of time‐series plots in the context of the parallel‐coordinates plot facilitates the exploration of time‐related aspects of the data without the need to switch to a separate display. We provide a consistent set of tools for selecting and contrasting subsets of the data, which are important for various application domains.     

Retailoring Box Splines to Lattices for Highly Isotropic Volume Representations

3D box splines are defined by convolving a 1D box function with itself along different directions. In volume visualization, box splines are mainly used as reconstruction kernels that are easy to adapt to various sampling lattices, such as the Cartesian Cubic (CC), Body‐Centered Cubic (BCC), and Face‐Centered Cubic (FCC) lattices. The usual way of tailoring a box spline to a specific lattice is to span the box spline by exactly those principal directions that span the lattice itself. However, in this case, the preferred directions of the box spline and the lattice are the same, amplifying the anisotropic effects of each other. This leads to an anisotropic volume representation with strongly preferred directions. Therefore, in this paper, we retailor box splines to lattices such that the sets of vectors that span the box spline and the lattice are disjoint sets. As the preferred directions of the box spline and the lattice compensate each other, a more isotropic volume representation can be achieved. We demonstrate this by comparing different combinations of box splines and lattices concerning their anisotropic behavior in tomographic reconstruction and volume visualization.     

Glare Generation Based on Wave Optics

This paper proposes a novel and general method of glare generation based on wave optics. A glare image is regarded as a result of Fraunhofer diffraction, which is equivalent to a 2D Fourier transform of the image of given apertures or obstacles. In conventional methods, the shapes of glare images are categorized according to their source apertures, such as pupils and eyelashes and their basic shapes (e.g. halos, coronas, or radial streaks) are manually generated as templates, mainly based on statistical observation. Realistic variations of these basic shapes often depend on the use of random numbers. Our proposed method computes glare images fully automatically from aperture images and can be applied universally to all kinds of apertures, including camera diaphragms. It can handle dynamic changes in the position of the aperture relative to the light source, which enables subtle movement or rotation of glare streaks. Spectra can also be simulated in the glare, since the intensity of diffraction depends on the wavelength of light. The resulting glare image is superimposed onto a given computer‐generated image containing high‐intensity light sources or reflections, aligning the center of the glare image to the high‐intensity areas. Our method is implemented as a multipass rendering software. By precomputing the dynamic glare image set and putting it into texture memory, the software runs at an interactive rate.     

Facial Feature Exaggeration According to Social Psychology of Face Perception

We propose a personality trait exaggeration system emphasizing the impression of human face in images, based on multi‐level features learning and exaggeration. These features are called Personality Trait Model (PTM). Abstract level of PTM is social psychology trait of face perception such as amiable, mean, cute and so on. Concrete level of PTM is shape feature and texture feature. A training phase is presented to learn multi‐level features of faces from different images. Statistical survey is taken to label sample images with people's first impressions. From images with the same labels, we capture not only shape features but also texture features to enhance exaggeration effect. Texture feature is expressed by matrix to reflect depth of facial organs, wrinkles and so on. In application phase, original images will be exaggerated using PTM iteratively. And exaggeration rate for each iteration is constrained to keep likeness with the original face. Experimental results demonstrate that our system can emphasize chosen social psychology traits effectively.     

Efficient Packing of Arbitrary Shaped Charts for Automatic Texture Atlas Generation

Texture atlases are commonly used as representations for mesh parameterizations in numerous applications including texture and normal mapping. Therefore, packing is an important post‐processing step that tries to place and orient the single parameterizations in a way that the available space is used as efficiently as possible. However, since packing is NP hard, only heuristics can be used in practice to find near‐optimal solutions. In this publication we introduce the new search space of modulo valid packings. The key idea thereby is to allow the texture charts to wrap around in the atlas. By utilizing this search space we propose a new algorithm that can be used in order to automatically pack texture atlases. In the evaluation section we show that our algorithm achieves solutions with a significantly higher packing efficiency when compared to the state of the art, especially for complex packing problems.     

Linear Discriminative Star Coordinates for Exploring Class and Cluster Separation of High Dimensional Data

One main task for domain experts in analysing their nD data is to detect and interpret class/cluster separations and outliers. In fact, an important question is, which features/dimensions separate classes best or allow a cluster‐based data classification. Common approaches rely on projections from nD to 2D, which comes with some challenges, such as: The space of projection contains an infinite number of items. How to find the right one? The projection approaches suffers from distortions and misleading effects. How to rely to the projected class/cluster separation? The projections involve the complete set of dimensions/features. How to identify irrelevant dimensions? Thus, to address these challenges, we introduce a visual analytics concept for the feature selection based on linear discriminative star coordinates (DSC), which generate optimal cluster separating views in a linear sense for both labeled and unlabeled data. This way the user is able to explore how each dimension contributes to clustering. To support to explore relations between clusters and data dimensions, we provide a set of cluster‐aware interactions allowing to smartly iterate through subspaces of both records and features in a guided manner. We demonstrate our features selection approach for optimal cluster/class separation analysis with a couple of experiments on real‐life benchmark high‐dimensional data sets.