Algorithms for antialiased cast shadows

The ability to render the shadows cast by objects in a scene is of prime importance in augmenting the realism of sequences produced by image synthesis algorithms. From a theoretical standpoint, the problem does not present a great number of difficulties: It requires an integration in the process, of the viewpoint from the light source and the utilization of this information when calculating the lighting of the pixels. The purpose of this study is not to present a radically new approach, but to explore in depth the most common problem present in these images, the aliasing, or more generally speaking, the sampling problem. This work has implied a specific approach in the hidden surface determination, the introduction of a variation of the already used A-buffers or Z-buffers, as well as an extensive study of the filtering problem.     

A fourier theory for cast shadows

Cast shadows can be significant in many computer vision applications, such as lighting-insensitive recognition and surface reconstruction. Nevertheless, most algorithms neglect them, primarily because they involve nonlocal interactions in nonconvex regions, making formal analysis difficult. However, many real instances map closely to canonical configurations like a wall, a V-groove type structure, or a pitted surface. In particular, we experiment with 3D textures like moss, gravel, and a kitchen sponge, whose surfaces include canonical configurations like V-grooves. This paper takes a first step toward a formal analysis of cast shadows, showing theoretically that many configurations can be mathematically analyzed using convolutions and Fourier basis functions. Our analysis exposes the mathematical convolution structure of cast shadows and shows strong connections to recent signal-processing frameworks for reflection and illumination.     

消除运动物体阴影的最大色度差分检测法

通过分析静止场景中的运动人体及其阴影与背景图像像素亮度、色度值之间的关系,提出了一种最大色度差分运动检测法,用前景图像与背景图像的最大色度差分代替传统的亮度差分,能够在尽量减少运动目标信息损失的条件下,有效地消除运动目标产生的阴影。     

Effect of shadows cast by vertical protrusions on AVHRR data

The effect of shadows cast by vertical protrusions on AVHRR data has been investigated. The AVHRR channel-1 data from the same ground area imaged from various viewing angles are examined. These data were obtained from nine orbits during eight successive days. The atmospheric effects were removed and an analysis of the resulting surface-leaving radiance shows that the effect of shadows is an important factor contributing to off-nadir viewing effect on the remotely sensed data.     

Learning robotic hand-eye coordination through a developmental constraint driven approach

The skill of robotic hand-eye coordination not only helps robots to deal with real time environment,but also afects the fundamental framework of robotic cognition.A number of approaches have been developed in the literature for construction of the robotic hand-eye coordination.However,several important features within infant developmental procedure have not been introduced into such approaches.This paper proposes a new method for robotic hand-eye coordination by imitating the developmental progress of human infants.The work employs a brain-like neural network system inspired by infant brain structure to learn hand-eye coordination,and adopts a developmental mechanism from psychology to drive the robot.The entire learning procedure is driven by developmental constraint: The robot starts to act under fully constrained conditions,when the robot learning system becomes stable,a new constraint is assigned to the robot.After that,the robot needs to act with this new condition again.When all the contained conditions have been overcome,the robot is able to obtain hand-eye coordination ability.The work is supported by experimental evaluation,which shows that the new approach is able to drive the robot to learn autonomously,and make the robot also exhibit developmental progress similar to human infants.     

The effects of cast shadows and stereopsis on performing computer-generated spatial tasks

More powerful computer hardware and software has enabled the development of numerous depth-cue techniques to depict the three-dimensional (3D) characteristics of computer-generated imagery. Particularly, the role of stereoscopic viewing in promoting the depth perception of objects in 3D space has been widely studied. However, there has been little study of the use of cast shadows in this context, and few investigations of how computer-generated depth cues are perceptually integrated. To investigate these issues, subjects: 1) position a sphere to complete a directional vector/vector segment extending in space and 2) resize a sphere to match the perceived size of a second sphere displaced in space. Task accuracies and response times are recorded under the following conditions: 1) stereoscopic, and monoscopic, scene viewing; 2) objects casting zero, or one shadow; 3) flat, and zig-zag, background surface shapes; and 4) solid and checkerboard background surface textures. The results suggest that, of the cues considered, stereo viewing has a consistently beneficial effect on positioning and resizing spheres in depth. However, objects casting shadows improve positioning accuracy to a level afforded by stereo viewing. Finally, more complex scene backgrounds impair positioning and resizing task performances. Implications for the design of effective 3D user interfaces are discussed.     

Detection and substitution of clouds/hazes and their cast shadows on IKONOS images

Frequently cloudy conditions in moist tropical regions often cause difficulty in acquiring good‐quality remote‐sensing images. This paper explores approaches to detect clouds/hazes and their cast shadows on IKONOS images. Two approaches based on maximum and minimum filters and the second‐moment texture measures were tested. The detected clouds/hazes and their cast shadows in one image were then replaced with the pixel values from the same location of another image after image normalization. This research indicates that both the approaches developed in this paper have successfully detected the clouds/hazes and their cast shadows on IKONOS images. In particular, the second‐moment texture approach is recommended.     

Learning to detect moving shadows in dynamic environments

We propose a novel adaptive technique for detecting moving shadows and distinguishing them from moving objects in video sequences. Most methods for detecting shadows work in a static setting with significant human input. To remove these limitations, we propose a more general semi-supervised learning technique to tackle the problem. First, we exploit characteristic differences in color and edges in the video frames to come up with a set of features useful for classification. Second, we use a learning technique that employs Support Vector Machines and the Co-training algorithm, that relies on a small set of human-labeled data. We observe a surprising phenomenon that Co-training can counter the effects of changing underlying probability distributions in the feature space. From the standpoint of detecting shadows, once deployed, the proposed method can dynamically adapt to varying conditions without any manual intervention, and performs better classification than previous methods on static and dynamic environments alike. The strengths of the proposed technique are the small quantity of human labeled data required, and the ability to adapt automatically to changing scene conditions.     

Rendering ground truth data sets to detect shadows cast by static objects in outdoors

In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically.     

A GIS-based method for modeling urban-climate parameters using automated recognition of shadows cast by buildings

Projections for the next thirty years predict a rapid growth in urban population, particularly in the less developed regions. Constructing and updating GIS databases of 3D urban form is essential for analyzing spatial phenomena related to urbanization. One of these phenomena, urban climate, is influenced by urban morphology. A prerequisite for representing urban morphology is 3D data including the height of urban features. In cases where no city plans or field survey data are available, a viable option is to use remotely-sensed data for recognition of urban features. Often, the output of automated object recognition consists of footprints of urban features. A known method for calculating a feature's height for reconstructing 3D urban morphology is to use the shadows cast by those features.A GIS-based method for constructing 3D geodatabases of urban morphology from cast shadows and analyzing geometrical parameters for urban-climate analysis was developed. The method focuses on the aspect-ratio of inner courtyards and its relation to solar access. A case-study is presented to demonstrate the feasibility of this method. Results demonstrate that the method offers a reliable and low-cost process for constructing 3D geodatabases that can be applied to the analysis of urban-climate parameters in less developed regions, where costly data and sophisticated processing practices are less accessible. This method can enhance the understanding of microclimatic conditions and facilitate climate conscious and sustainable future urban planning in sprawling urban centers.     

Towards a Model of Learning through Communication

Communication is an interactive, complex, structured process involving agents that are capable of drawing conclusions from the information they have available about some real-life situations. Such situations are generally characterized as being imperfect. In this paper, we aim to address learning from the perspective of the communication between agents. To learn a collection of propositions concerning some situation is to incorporate it within one's knowledge about that situation. That is, the key factor in this activity is for the goal agent, where agents may switch role if appropriate, to integrate the information offered with what it already knows. This may require a process of belief revision, which suggests that the process of incorporation of new information should be modeled nonmonotonically. We shall employ for reasoning a three-valued based nonmonotonic logic that formalizes some aspects of revisable reasoning and it is accessible to implementation. The logic is sound and complete. A theorem-prover of the logic has successfully been implemented. Received 3 August 1999 / Revised 17 April 2000 / Accepted 6 May 2000     

Argumentation through a distributed self-stabilizing approach

Argumentation is receiving an increasing attention as a technique for practical and uncertain reasoning underlying the realization of intelligent autonomous agents. Since a decentralized organization has been proposed by several authors as an appropriate paradigm for the design of agent architectures, we propose in this article, a distributed approach to argumentation, in which several independent asynchronous processes carry out argumentation activity, by exploiting local information only. The final result of this process is the computation of the defeat status of the arguments: we devise a general distributed algorithm, which does not rely on any specific notion of defeat between arguments. The issue of coordination has been explicitly tackled by ensuring the property of self-stabilization for the algorithm. A proof of its correctness, as well as an analysis of its complexity, is provided.     

基于支持向量机的椒盐噪声去除方法

针对自然图像中相邻像素的相关性及其椒盐噪声的特点,提出了一种基于支持向量机的椒盐噪声消除方法。该方法应用支持向量机的学习机制对图像灰度曲面进行最佳拟合,并从训练样本中提取支持向量与相应的决策函数,最后根据决策函数在拟合曲面上进行噪声像素点的灰度值预测,从而恢复噪声点的原始信号。通过与传统的中值滤波和均值滤波进行实验对比,提出的方法可有效地去除椒盐噪声,同时最大限度地保留图像的细节信息,尤其对高密度椒盐噪声图像的处理效果更为理想。     

基于RSC模型和噪声去除的半监督训练方法

“半监督学习”是利用已经标记好的训练样本和未标记的训练样本一起训练分类器.传统的半监督训练过程对噪声不作辨别,这种做法会因噪声的存在破坏分类器的训练过程,进而影响分类器的分类效果.针对该问题,提出了基于RSC模型和噪声去除的半监督训练方法,在样本训练过程中,使用RSC标签扩展的方法,并添加噪声去除环节.实验表明,该算法能有效降低半监督学习中噪声对分类器的影响,得到更加精确的分类边界,最终提高算法的性能和稳定性.     

Learning through multimedia

The creation of multimedia composition tools for young learners is addressed. The factors to consider in designing composing tools are discussed. Such a set of tools, called the Multimedia Works software environment, is described. It consists of three programs: Media Space, a multimedia database and research program; Multimedia Works Composer, a storyboarding and presentation program; and Video Light Table, a direct manipulation video-clip editor. The use of student participation in developing the tools is also described. Some problems that are being worked on are examined     

Smart wheelchair control through a deictic approach

In this paper, the implementation of assistance to the driving of a smart wheelchair through a deictic approach is described. Initially, a state of the art of mobility assistance, interfaces and types of commands for smart wheelchairs is presented. The deictic concept, and more particularly, the approach used for the design of our interface is examined. Then the two functionalities carried out to implement this type of interface, as well as methodology used to control our wheelchair are illustrated. Finally, the usability of this deictic approach for the assistance to the driving of a smart wheelchair is discussed.     

Signal differentiation through a Green''s function approach

We introduce a new approach for the design of differential operators, based on the Green's function solution to a signal matching equation. Its use is illustrated by the construction of step-edge enhancement filters whose performance figures are comparable, and even superior, to those reported in the literature.     

Reaping EDI benefits through a pro-active approach

EDI are emerging as a popular means for organizations to exchange trade documents that accomplish business transactions. This study examines the impact of Tradenet, a well-known system in Singapore, on the effectiveness and efficiency of its participants. Organizational effectiveness was measured in terms of international competitiveness, inventory control, and customer quantity. Organizational efficiency was gauged using preparation cost, exchange cost, and document flow. A survey was carried out on Tradenet participants. The results show that all, irrespective of organizational characteristics, attained enhanced organizational efficiency by participating. Furthermore, participants that planned ahead, participated in Tradenet early, or integrated Tradenet with their internal MIS, benefited more in terms of increased organizational effectiveness. The results suggest that organizations can reap greater benefits from their involvement in EDI by taking a proactive approach.