基于轮廓线的非真实感绘制技术

从NPR技术的形成和特点出发，详细论述了NPR中轮廓线的检测和绘制技术等问题。轮廓线在NPR中占有重要地位，它的表现不仅关系到NPR最终效果的好坏，而且也是实时绘制中一个提高速度的关键。重点介绍了NPR中流行的和最新的轮廓线检测与绘制技术，并对这些算法和技术进行了分类与分析，总结了其优缺点和适用场景。     

非真实感绘制技术的研究

非真实感绘制是计算机图形学的一个分支,主要在于表现图形的艺术特质、模拟艺术作品或作为真实感图形的有效补充.本文从非真实感绘制技术的形成和发展出发,论述了非真实感绘制的特点、分类及应用.并介绍了非真实感的主要绘制技术,分析了每种绘制技术的具体算法.     

GPU加速的光滑轮廓线绘制

轮廓线的高效提取是非真实感绘制的一个关键问题。提出了一个完全利用GPU生成光滑轮廓线的高效算法。在几何处理阶段,先根据相邻三角形的法向量与视向量的关系检测出轮廓线,然后对轮廓线进行宽度扩充,同时对轮廓线顶点设置相应的渐变因子;在像素处理阶段把渐变因子转化为相应的alpha值,通过光照生成卡通渲染,最后通过alpha混合得到光滑轮廓线。算法完全在GPU里实现,能满足实时的绘制要求。     

基于NPR的3维模型线绘算法

为更好地进行3维模型的线绘,介绍了一个基于非真实感绘制技术的三角网格模型的线绘算法。该算法在预处理阶段利用离散化的曲率计算方法来估计模型顶点的平均曲率;在交互阶段,首先根据用户提供的平均曲率阈值检测模型的凹区域,同时通过设计启发式搜索算法来提取该区域适宜的线条,然后结合计算即可得到模型轮廓线,并在绘制时考虑虚拟光照效果,绘制结果表明,可以得到较满意的结果。     

非真实感铅笔画艺术效果研究

改进了非真实感铅笔画艺术效果的绘制算法,利用图像分割、线积分卷积技术确定线条纹理的方向,模拟得到局部区域铅笔画的笔划纹理。采用轮廓线增强、灰度色调控制最终铅笔画艺术效果的边缘信息和灰度色调。通过色彩传输技术得到具有彩色铅笔画的艺术效果。     

非真实感绘制技术研究进展

作为与真实感图形学相对应的图形学分支,非真实感绘制技术近年来受到人们的关注并已成为计算机图形学的研究热点之一,其研究成果已在计算机动画产业.计算机艺术和科学资料插图绘制等领域得到了广泛的应用.对非真实感绘制的相关技术进行了综述,首先介绍了非真实感绘制的发展过程,然后以不同艺术效果的模拟为线索对非真实感绘制技术进行了分类,并对各类技术的特点、发展状况和主要算法进行了分析和讨论,同时对非真实感绘制中所使用的特殊技术进行了分类讨论.最后对非真实感绘制技术的未来发展进行了展望.     

基于轮廓线的非真实感绘制技术

从NPR技术的形成和特点出发,详细论述了NPR中轮廓线的检测和绘制技术等问题。轮廓线在NPR中占有重要地位,它的表现不仅关系到NPR最终效果的好坏,而且也是实时绘制中一个提高速度的关键。重点介绍了NPR中流行的和最新的轮廓线检测与绘制技术,并对这些算法和技术进行了分类与分析,总结了其优缺点和适用场景。     

基于粒子和纹理绘制的火焰合成

现有的火焰合成算法多数都是基于粒子系统，其主要缺点是运算量大。本文先用少量粒子勾勒火焰的外轮廓线，再用纹理绘制的方法填充火焰纹理。这样既利用了粒子系统形成轮廓线的真实感，又避免大量的粒子状态运算，并能体现动态火焰纹理的一致性和连续性。     

森林场景的钢笔风格化实时绘制技术*

在大型复杂场景绘制关键技术研究项目中,通过把复杂场景绘制与调度技术,如Slicing、MMI、层次图像缓存和基本布告板等大场景实时加速绘制技术的多层次绘制方法,融合到非真实感绘制技术中,以提高系统面对复杂场景的实时绘制能力。最后实现了对森林等大型场景的钢笔画绘制,借助非真实感绘制技术中基于图像空间的生成方法,利用各种不同大小及形状的笔画实现了艺术化森林场景实时交互绘制。提出了植物多层次钢笔风格化渲染算法。     

点模型的快速高质量绘制

针对点模型的快速高质量绘制问题,提出一种单遍绘制算法.首先根据构造的移动最小二乘曲面计算采样点的表面几何属性,然后根据协方差分析确定点元在其切平面上的椭圆表示,最后采用椭圆加权平均滤波从远到近单遍绘制各点元.此外,根据表面几何属性确定点模型的轮廓线,实现了点模型的非真实感绘制.实验结果表明,该算法能够实现点模型的快速绘制且绘制效果令人满意.     

Shape based appearance model for kernel tracking

This paper investigates kernel based tracking using shape information. A kernel based tracker typically models an object with a primitive geometric shape, and then estimates the object state by fitting the kernel such that the appearance model is optimized. Most of the appearance models in kernel based tracking utilize the textural information within the kernel, although a few of them also make use of the gradient information along the kernel boundary. Interestingly, shape information of a general form has never been fully exploited in kernel tracking, despite the fact that shape has been widely used in silhouette tracking at the cost of intensive computation. In this paper, we propose an original way to incorporate shape knowledge into the appearance model of kernel based trackers while preserving their computational advantage versus silhouette based trackers. Experimental results demonstrate that kernel tracking is strongly improved by exploiting the proposed shape cue through comparisons to both kernel and silhouette trackers.     

Silhouette-based multi-sensor smoke detection

Fire is one of the leading hazards affecting everyday life around the world. The sooner the fire is detected, the better the chances are for survival. Today’s fire alarm systems, such as video-based smoke detectors, however, still pose many problems. In order to accomplish more accurate video-based smoke detection and to reduce false alarms, this paper proposes a multi-sensor smoke detector which takes advantage of the different kinds of information represented by visual and thermal imaging sensors. The detector analyzes the silhouette coverage of moving objects in visual and long-wave infrared registered (~aligned) images. The registration is performed using a contour mapping algorithm which detects the rotation, scale and translation between moving objects in the multi-spectral images. The geometric parameters found at this stage are then further used to coarsely map the silhouette images and coverage between them is calculated. Since smoke is invisible in long-wave infrared its silhouette will, contrarily to ordinary moving objects, only be detected in visual images. As such, the coverage of thermal and visual silhouettes will start to decrease in case of smoke. Due to the dynamic character of the smoke, the visual silhouette will also show a high degree of disorder. By focusing on both silhouette behaviors, the system is able to accurately detect the smoke. Experiments on smoke and non-smoke multi-sensor sequences indicate that the automated smoke detection algorithm is able to coarsely map the multi-sensor images. Furthermore, using the low-cost silhouette analysis, a fast warning, with a low number of false alarms, can be given.     

Expressive rendering: a review of nonphotorealistic techniques

The success of graphics systems that render photorealistic output has obscured parallel developments in nonphotorealistic rendering. A review of these “expressive” systems suggests a framework for further development. One of the authors, Simon Schofield, has developed a prototype system that gives users a wide variety of automatic rendering styles-from the almost photorealistic, through the styles resembling artists' sketches or paintings, to abstract renderings of entirely new forms. The significant problems associated with NPR are primarily aesthetic and only secondarily technical. The culturally oriented theoretical problems surrounding the field are many and, at their most difficult, broach central issues of perception and representation. We therefore begin by discussing the limitations of photorealism as a representational style. However, the technical problems of implementing an NPR system are not trivial. As far as we know, the literature defines no general framework for an NPR system, although it does include an array of more focused solutions. We survey more than 15 of these solutions. On the basis of this survey, we then define a framework for NPR systems that distinguishes them from systems focused on photorealism. We also identify the characteristics of NPR systems that go beyond the tendency to simulate traditional media, functioning instead as 3D renderers. In this context, we briefly describe Schofield's system, called Piranesi, which operates this way     

神经程序修复领域数据泄露问题的实证研究

修复软件缺陷是软件工程领域一个无法回避的重要问题,而程序自动修复技术则旨在自动、准确且高效地修复存在缺陷的程序,以缓解软件缺陷所带来的问题.近年来,随着深度学习的快速发展,程序自动修复领域兴起了一种使用深度神经网络去自动捕捉缺陷程序和其补丁之间关系的方法,被称为神经程序修复.从在基准测试上被正确修复的缺陷的数量上看,神经程序修复工具的修复性能已经显著超过了非学习的程序自动修复工具.然而,近期有研究发现神经程序修复系统性能的提升可能得益于测试数据在训练数据中存在,即数据泄露.受此启发,为进一步探究神经程序修复系统数据泄露的原因及影响,更公平地评估现有的系统,本文(1)对现有神经程序修复系统进行了系统的分类和总结,根据分类结果定义了神经程序修复系统的数据泄露,并为每个类别的系统设计了数据泄露的检测方法;(2)依照上一步骤中的数据泄露检测方法对现有模型展开了大规模检测,并探究了数据泄露对模型真实性能与评估性能间差异的影响以及对模型本身的影响;(3)分析现有神经程序修复系统数据集的收集和过滤策略,加以改进和补充,在现有流行的数据集上基于改进后的策略构建了一个纯净的大规模程序修复训练数据集,并验证了该数据集避免数据泄露的有效性.实验结果发现:本次调研的10个神经程序修复系统在基准测试集上均出现了数据泄露,其中神经程序修复系统RewardRepair的数据泄露问题较为严重,在基准测试集Defects4J(v1.2.0)上的数据泄露达24处,泄露比例高达53.33%.此外,数据泄露对神经程序修复系统的鲁棒性也造成了影响,调研的5个神经程序修复系统均因数据泄露产生了鲁棒性降低的问题.由此可见,数据泄露是一个十分常见的问题,且会使神经程序修复系统得到不公平的性能评估结果,以及影响系统本身的性质.研究人员在训练神经程序修复模型时,应尽可能避免出现数据泄露,且要考虑数据泄露问题对神经程序修复系统性能评估产生的影响,尽可能更公平地评估系统.     

Structure-preserving NPR framework for image abstraction and stylization

This work presents a structure-preserving non-photorealistic rendering (NPR) framework that can produce an effective structure-preserving abstracted and stylized output by manipulating visual features from 2D color image. The proposed framework distills the prominent structural features, dominant edges, medium-scale details, curved discontinued edges, silhouette, dendritic structures and curved boundaries and suppresses the superfluous details like noise, texture, irregular gradients, small-scale details and block artifacts. This framework effectively enhanced the significant image properties such as color, contrast, edge strength and sharpness at every stage based on the obtained statistical features availability information and the predefined conditions. This leads to enhancement of quality assessment features such as PSNR, SSIM and suppressing the image complexity and noise. It considers image and object space information to produce abstraction and stylization, thereby identifying emphasized elements of the structure using Harris feature detector algorithm. The proposed framework effectively preserves the structural features in the foreground of an image by comprehensively integrating the sequence of NPR image filters through rigorous experimental analysis simultaneously diminishing the background content of an image. Implementation of the proposed work is carried out in MATLAB 2018 with high-performance computer of 6.6 teraflop/s computing environment and Nvidia Tesla P100 GPU. The proposed framework evaluates every stage output with various subjective matters and quality assessment techniques with various statistical essences. By this manner, contextual features in an image have been identified and well preserved. Effectiveness of the proposed work has been validated by conducting the experiments taking David Mould dataset and Flickr images as references and comparing the obtained results with similar contemporary work cited in the literature. In addition, user’s visual feedback and the standard quality assessment techniques were also used to evaluate the work. Finally, this work lists out the structures preserving applications, constraints, framework implementation challenges and future work in the fields of image abstraction and stylization.

     

Shape representation and classification using the poisson equation

We present a novel approach that allows us to reliably compute many useful properties of a silhouette. Our approach assigns, for every internal point of the silhouette, a value reflecting the mean time required for a random walk beginning at the point to hit the boundaries. This function can be computed by solving Poisson's equation, with the silhouette contours providing boundary conditions. We show how this function can be used to reliably extract various shape properties including part structure and rough skeleton, local orientation and aspect ratio of different parts, and convex and concave sections of the boundaries. In addition to this, we discuss properties of the solution and show how to efficiently compute this solution using multigrid algorithms. We demonstrate the utility of the extracted properties by using them for shape classification and retrieval     

Fusion of color and infrared video for moving human detection

We approach the task of human silhouette extraction from color and thermal image sequences using automatic image registration. Image registration between color and thermal images is a challenging problem due to the difficulties associated with finding correspondence. However, the moving people in a static scene provide cues to address this problem. In this paper, we propose a hierarchical scheme to automatically find the correspondence between the preliminary human silhouettes extracted from synchronous color and thermal image sequences for image registration. Next, we discuss strategies for probabilistically combining cues from registered color and thermal images for improved human silhouette detection. It is shown that the proposed approach achieves good results for image registration and human silhouette extraction. Experimental results also show a comparison of various sensor fusion strategies and demonstrate the improvement in performance over non-fused cases for human silhouette extraction.     

A Dynamic Noise Primitive for Coherent Stylization

We present a new solution for temporal coherence in non‐photorealistic rendering (NPR) of animations. Given the conflicting goals of preserving the 2D aspect of the style and the 3D scene motion, any such solution is a tradeoff. We observe that primitive‐based methods in NPR can be seen as texture‐based methods when using large numbers of primitives, leading to our key insight, namely that this process is similar to sparse convolution noise in procedural texturing. Consequently, we present a new primitive for NPR based on Gabor noise, that preserves the 2D aspect of noise, conveys the 3D motion of the scene, and is temporally continuous. We can thus use standard techniques from procedural texturing to create various styles, which we show for interactive NPR applications. We also present a user study to evaluate this and existing solutions, and to provide more insight in the trade‐off implied by temporal coherence. The results of the study indicate that maintaining coherent motion is important, but also that our new solution provides a good compromise between the 2D aspect of the style and 3D motion.