Into the Blue: Better Caustics through Photon Relaxation

The photon mapping method is one of the most popular algorithms employed in computer graphics today. However, obtaining good results is dependent on several variables including kernel shape and bandwidth, as well as the properties of the initial photon distribution. While the photon density estimation problem has been the target of extensive research, most algorithms focus on new methods of optimising the kernel to minimise noise and bias. In this paper we break from convention and propose a new approach that directly redistributes the underlying photons. We show that by relaxing the initial distribution into one with a blue noise spectral signature we can dramatically reduce background noise, particularly in areas of uniform illumination. In addition, we propose an efficient heuristic to detect and preserve features and discontinuities. We then go on to demonstrate how reconfiguration also permits the use of very low bandwidth kernels, greatly improving render times whilst reducing bias.     

A solution to illumination direction estimation of a shaded image: Genetic algorithm

In oblique shape from shading (SfS), the illumination direction is essential for recovering the 3D surface of a shaded image. On the other hand, fast marching methods (FMM) are SfS algorithms that use the mechanism of wave propagation to reconstruct the surface. In this paper, the estimation of illumination direction is addressed and we model it as an optimization problem. The idea is to minimize the inconsistency of wave propagation of FMM during the reconstruction. As the consistency of wave propagation is a multi-modal function of illumination direction, genetic algorithm (GA) is utilized. The proposed algorithm is examined on four synthetic models and a real world object. The experimental results show that the proposed algorithm is superior to benchmark methods.     

基于空间边缘方向直方图的Mean Shift跟踪算法

传统的基于色彩直方图或空间色彩直方图的Mean Shift跟踪算法,在诸如跟踪目标出现尺度变化的复杂条件下,无法得到准确的跟踪结果。这是因为色彩直方图或空间色彩直方图无法显著区分颜色相近的目标和背景。鉴于此,提出了一种基于空间边缘方向直方图的Mean Shift跟踪算法,使用空间分布和纹理信息作为匹配信息。实验结果表明,该算法能够有效的处理遮挡、光照变化和尺度缩放等复杂情况,对目标进行准确有效的跟踪,改善了传统方法在尺度缩放等方面的局限性。     

基于空间梯度直方图的行人检测算法

为了快速准确地对行人进行检测,提出了一种采用空间梯度直方图作为特征的行人检测方法。该方法首先使用直方图相似性和Fisher准则来衡量所有定义特征的分辨能力,然后选择一些具有强分辨能力的特征来表征行人,使该方法获得了较高的检测率;最后用SVM分类器用来训练从目标和背景中获得的经过筛选的特征。实验结果表明,该方法对行人的检测是快速且有效的。     

Structure-based color learning on a mobile robot under changing illumination

A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. To operate in the real world, autonomous robots rely on sensory information. Despite the potential richness of visual information from on-board cameras, many mobile robots continue to rely on non-visual sensors such as tactile sensors, sonar, and laser. This preference for relatively low-fidelity sensors can be attributed to, among other things, the characteristic requirement of real-time operation under limited computational resources. Illumination changes pose another big challenge. For true extended autonomy, an agent must be able to recognize for itself when to abandon its current model in favor of learning a new one; and how to learn in its current situation. We describe a self-contained vision system that works on-board a vision-based autonomous robot under varying illumination conditions. First, we present a baseline system capable of color segmentation and object recognition within the computational and memory constraints of the robot. This relies on manually labeled data and operates under constant and reasonably uniform illumination conditions. We then relax these limitations by introducing algorithms for (i) Autonomous planned color learning, where the robot uses the knowledge of its environment (position, size and shape of objects) to automatically generate a suitable motion sequence and learn the desired colors, and (ii) Illumination change detection and adaptation, where the robot recognizes for itself when the illumination conditions have changed sufficiently to warrant revising its knowledge of colors. Our algorithms are fully implemented and tested on the Sony ERS-7 Aibo robots.

基于人工神经网络的彩色校正方法研究

中医舌象的物理特性主要反映在舌体的颜色上 ,而如何进行颜色的测量以及如何在不同设备间保持色彩的一致性和重复性 ,即彩色校正是研制中医舌象分析仪必须解决的难题 .该文简要介绍了中医舌象分析仪的系统结构 ,同时讨论了照明光源的选择和设计 ,最后提出了采用神经网络进行彩色校正的方法     

Visualization of eclipses and planetary conjunction events. The interplay between model coherence, scaling and animation

The problem of an instructive and realistic animation and visualization of the shadow- and color-conditions during conjunctions of actively and passively illuminated cosmic objects has found only particularly satisfying solutions so far. As an example we study a total solar eclipse. There are didactic shortcomings of specialized astronomical software, even though solutions have been given, which are very impressive for experts. Using the possibilities of commercial 3D-animation software we give an object-oriented partial solution. In order to get correct astronomical representations we model – for different tasks – the object space under cinematic aspects with parameters for spatial and temporal scaling, for illumination and coloring under couplings of varying strength. The adaptation of the parameters to optimal acceptance of the spectator must be done a posteriori.     

一种基于视觉的曲面光照模型参数确定方法

曲面光照模型参数与很多因素有关,很难精确确定,该文提出一种基于计算机视觉的模型参数确定方法。文章首先介绍了Phong光照模型,并以该模型为例,利用视觉信息,采用优化方法确定了光照模型中各参数。实例表明该方法具有有效性和实用性。     

一种改进的扫描线真实感图形显示算法

真实感图形显示是计算机图形学的一个重要组成部分。扫描线算法是一种常用的真实感图形显示算法。文章在扫描线直实感图形显示算法基础之上，对其作了一定的改进。对光照模型增加了衰减变化，并使扫描线算法也适用于曲面，提高了真实感的效果。     

Incremental Learning for Robust Visual Tracking

Visual tracking, in essence, deals with non-stationary image streams that change over time. While most existing algorithms are able to track objects well in controlled environments, they usually fail in the presence of significant variation of the object’s appearance or surrounding illumination. One reason for such failures is that many algorithms employ fixed appearance models of the target. Such models are trained using only appearance data available before tracking begins, which in practice limits the range of appearances that are modeled, and ignores the large volume of information (such as shape changes or specific lighting conditions) that becomes available during tracking. In this paper, we present a tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target. The model update, based on incremental algorithms for principal component analysis, includes two important features: a method for correctly updating the sample mean, and a forgetting factor to ensure less modeling power is expended fitting older observations. Both of these features contribute measurably to improving overall tracking performance. Numerous experiments demonstrate the effectiveness of the proposed tracking algorithm in indoor and outdoor environments where the target objects undergo large changes in pose, scale, and illumination.