Textures on Rank‐1 Lattices

Storing textures on orthogonal tensor product lattices is predominant in computer graphics, although it is known that their sampling efficiency is not optimal. In two dimensions, the hexagonal lattice provides the maximum sampling efficiency. However, handling these lattices is difficult, because they are not able to tile an arbitrary rectangular region and have an irrational basis. By storing textures on rank‐1 lattices, we resolve both problems: Rank‐1 lattices can closely approximate hexagonal lattices, while all coordinates of the lattice points remain integer. At identical memory footprint texture quality is improved as compared to traditional orthogonal tensor product lattices due to the higher sampling efficiency. We introduce the basic theory of rank‐1 lattice textures and present an algorithmic framework which easily can be integrated into existing off‐line and real‐time rendering systems.     

仿射重投影混合虚实注册方法

针对特征图形结合自然纹理特征的虚实注册误差较大、运行速度慢的问题,提出仿射重投影混合虚实注册方法.通过建立摄像机、真实场景以及特征坐标系之间对应关系的仿射坐标系,且采用ORB算法进行特征检测及后续特征模板的构造,并利用仿射重投影矩阵来恢复特征模板在当前图像上的投影位置信息以计算虚实注册矩阵.采用OpenGL生成的立方体模型与真实场景之间进行虚实注册仿真实验.仿真结果表明:在特征图形被遮挡或不可见时,本文方法在精度、健壮性以及速率方面表现优异,提高了增强现实(AR)系统的实时性和鲁棒性.     

Compressed Multiresolution Hierarchies for High‐Quality Precomputed Shadows

The quality of shadow mapping is traditionally limited by texture resolution. We present a novel lossless compression scheme for high‐resolution shadow maps based on precomputed multiresolution hierarchies. Traditional multiresolution trees can compactly represent homogeneous regions of shadow maps at coarser levels, but require many nodes for fine details. By conservatively adapting the depth map, we can significantly reduce the tree complexity. Our proposed method offers high compression rates, avoids quantization errors, exploits coherency along all data dimensions, and is well‐suited for GPU architectures. Our approach can be applied for coherent shadow maps as well, enabling several applications, including high‐quality soft shadows and dynamic lights moving on fixed‐trajectories.     

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

In this paper a new feature extraction method called Multi-scale Sobel Angles Local Binary Pattern (MSALBP) is proposed for application in personal verification using biometric Finger Texture (FT) patterns. This method combines Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). The resulting characteristics are formed into non-overlapping blocks and statistical calculations are implemented to form a texture vector as an input to an Artificial Neural Network (ANN). A Probabilistic Neural Network (PNN) is applied as a multi-classifier to perform the verification. In addition, an innovative method for FT fusion based on individual finger contributions is suggested. This method is considered as a multi-object verification, where a finger fusion method named the Finger Contribution Fusion Neural Network (FCFNN) is employed for the five fingers. Two databases have been employed in this paper: PolyU3D2D and Spectral 460 nm (S460) from CASIA Multi-Spectral (CASIA-MS) images. The MSALBP feature extraction method has been examined and compared with different Local Binary Pattern (LBP) types; in classification it yields the lowest Equal Error Rate (EER) of 0.68% and 2% for PolyU3D2D and CASIA-MS (S460) databases, respectively. Moreover, the experimental results revealed that our proposed finger fusion method achieved superior performance for the PolyU3D2D database with an EER of 0.23% and consistent performance for the CASIA-MS (S460) database with an EER of 2%.     

基于词包和特征融合的目标识别算法

针对现有词包模型对目标识别性能的不足,对特征提取、图像表示等方面进行改进以提高目标识别的准确率。首先,以密集提取关键点的方式取代SIFT关键点提取,减少了计算时间并最大程度地描述了图像底层信息。然后采用尺度不变特征变换(Scale-invariant feature transform, SIFT)描述符和统一模式的局部二值模式(Local binary pattern,LBP)描述符描述关键点周围的形状特征和纹理特征,引入K-Means聚类算法分别生成视觉词典,然后将局部描述符进行近似局部约束线性编码,并进行最大值特征汇聚。分别采用空间金字塔匹配生成具有空间信息的直方图,最后将金字塔直方图相串联,形成特征的图像级融合,并送入SVM进行分类识别。在公共数据库中进行实验,实验结果表明,本文所提方法能取得较高的目标识别准确率。     

一种基于鲁棒局部纹理特征的背景差分方法

针对复杂场景下运动目标的精确检测这一问题,提出一种对噪声鲁棒并具备灰度尺度不变性的局部纹理特征描述子LBP_Center,将其与像素的颜色信息结合应用于背景建模中,采用随机抽样的机制更新模型,同时引入背景复杂度以去除多模态动态背景产生的噪点。在标准测试数据集上的实验结果表明,该算法对柔性阴影及光照缓慢变化具备良好的鲁棒性,综合性能更优。     

小波在基于CT图像的肝脏疾病良恶性分类中的研究

肝癌是一种常见的恶性肿瘤,近年来发病率呈缓慢上升的趋势,病死率也随之上升。文章利用小波在特征提取和模式识别方面的独特优势,提取了基于小波和灰度共生矩阵的纹理特征,结合遗传算法进行特征选择和优化,用KNN分类器设计出高精确度的肝脏疾病良恶性分类器。采用肝脏CT平扫图像,将肝癌与其他的良性病变进行分类,探讨了小波的不同性质及特征提取方式对分类结果的影响,对小波在肝脏CT图像良恶性分类中的研究有指导意义。     

引入边缘信息的纹理传输改进算法

本文针对Efros等人提出的块缝合纹理合成与传输算法,在搜索匹配块的过程中未考虑目标图块边缘信息这一不足之处,提出了一种改进算法．新算法在原有的搜索误差匹配公式中,通过计算梯度的方式增加了边缘信息处理项,同时整个传输过程还引入了亮度重映射的匹配预处理策略．实验结果表明,改进后算法的传递效果优于传统算法,尤其是目标图的边缘轮廓部分传递效果有比较明显的改善．     

Predicted Virtual Soft Shadow Maps with High Quality Filtering

In this paper we present a novel image based algorithm to render visually plausible anti‐aliased soft shadows in a robust and efficient manner. To achieve both high visual quality and high performance, it employs an accurate shadow map filtering method which guarantees smooth penumbrae and high quality anisotropic anti‐aliasing of the sharp transitions. Unlike approaches based on pre‐filtering approximations, our approach does not suffer from light bleeding or losing contact shadows. Discretization artefacts are avoided by creating virtual shadow maps on the fly according to a novel shadow map resolution prediction model. This model takes into account the screen space frequency of the penumbrae via a perceptual metric which has been directly established from an appropriate user study. Consequently, our algorithm always generates shadow maps with minimal resolutions enabling high performance while guarantying high quality. Thanks to this perceptual model, our algorithm can sometimes be faster at rendering soft shadows than hard shadows. It can render game‐like scenes at very high frame rates, and extremely large and complex scenes such as CAD models at interactive rates. In addition, our algorithm is highly scalable, and the quality versus performance trade‐off can be easily tweaked.     

General Spectral Camera Lens Simulation

We present a camera lens simulation model capable of producing advanced photographic phenomena in a general spectral Monte Carlo image rendering system. Our approach incorporates insights from geometrical diffraction theory, from optical engineering and from glass science. We show how to efficiently simulate all five monochromatic aberrations, spherical and coma aberration, astigmatism, field curvature and distortion. We also consider chromatic aberration, lateral colour and aperture diffraction. The inclusion of Fresnel reflection generates correct lens flares and we present an optimized sampling method for path generation.