This paper proposes a hybrid model for deformable template which combines alignable and non-alignable sketches. These sketches are subject to slight or considerable translations in different images. For slight translations, Wu et al [13] proposed active basis model to capture them, where each sketch is allowed to shift in position and orientation. For larger translations of sketches, [13] assumed that they follow the same distribution as sketches of natural image ensembles, which need not be explicitly modeled. But in fact, for a specified object class, the unaligned sketches follow a totally different distribution from those of natural images. We summarize these sketches by their means in the foreground mask. We treat the mean value in each direction as independent features and fit their marginal distributions on object ensemble and natural image ensemble using Gaussian distribution. The marginal distributions are combined with Active Basis into a joint probability ratio to distinguish foreground object from natural background. Experiments are conducted on 14 object classes, most of which show considerable improvement in ROC.

从路口视频图像中实时地构建道路背景图像是基于背景减法的车流量检测的前提。由于路口车流信息复杂，图像受外界环境因素影响大，现有的背景估计算法都有不足之处。该文提出了一种改进的频度背景估计算法，通过离散样本图像，实时地构建背景。该方法不仅算法简单、实时性高，而且增强了在背景估计时对光线变化的适应性，提高了背景建模的准确度，可适用于基于视频的路口信号灯的智能控制系统。

Representing the appearances of surfaces illuminated from different directions has long been an active research topic. While many representation methods have been proposed, the relationships and conversion between different representations have been less well researched. These relationships are important, as they provide (a) an insight as to the different capabilities of the surface representations, and (b) a means by which they may be converted to common formats for computer graphic applications. In this paper, we introduce a single mathematical framework and use it to express three commonly used surface texture relighting representations: surface gradients (Gradient), Polynomial Texture Maps (PTM) and eigen base images (Eigen). The framework explicitly reveals the relations between the three methods, and from this we propose a set of conversion methods. We use 26 rough surface textures illuminated from 36 directions for our experiments and perform both quantitative and qualitative assessments to evaluate the conversion methods. The quantitative assessment uses a normalized root-mean-squared error as metric to compare the original images and those produced by proposed representation methods. The qualitative assessment is based on psychophysical experiments and non-parametric statistics. The results from the two assessments are consistent and show that the original Eigen representation produces the best performance. The second best performances are achieved by the original PTM representation and the conversion between Polynomial Texture Maps (PTM) and eigen base images (Eigen), while the performances of other representations are not significantly different.

This paper presents inexpensive methods for selfsimilarity based editing of real-world 3D surface textures. Unlike self-similarity based 2D texture editing approaches which only make changes to pixel color or intensity values, these techniques also allow surface geometry, reflectance and other representations of the captured 3D surface textures to be edited and relit using illumination directions that differ from those of the original. A single editing operation at a given location affects all similar areas and produces changes on all images of the sample rendered under different conditions. We perform painting, cloning and warping operations over two sets of 3D surface texture representations: (1) surface height and albedo maps, and (2) eigen base images. The surface height and albedo maps are commonly used for bump mapping, and eigen base images can be used for representing 3D surface textures with complex reflectance. The result representations can be efficiently used in modern computer graphics packages for real-time applications as rendering process only requires simple graphics calculations such as weighted sums of base images.

We introduce a simple texture synthesis method based on Support Vector Machines (SVM). Although SVM has been effectively used for various pattern recognition tasks, there is no report available on directly applying SVM for texture synthesis. The advantage of using SVM is that the sample can be simply modeled by a linear model and is not required during the synthesis stage. In addition, the method can be further extended to synthesize 3D surface texture or Bidirectional Texture Functions. Our experimental results show that the method can successfully model and synthesize semi or highly structured textures, which can be difficult subjects for previous texture synthesis methods based on parametric models.