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。     

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.     

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.     

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.     

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.     

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     

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     

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.     

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.     

Reasoning about shadows in a mobile robot environment

This paper describes a logic-based formalism for qualitative spatial reasoning with cast shadows (Perceptual Qualitative Relations on Shadows, or PQRS) and presents results of a mobile robot qualitative self-localisation experiment using this formalism. Shadow detection was accomplished by mapping the images from the robot’s monocular colour camera into a HSV colour space and then thresholding on the V dimension. We present results of self-localisation using two methods for obtaining the threshold automatically: in one method the images are segmented according to their grey-scale histograms, in the other, the threshold is set according to a prediction about the robot’s location, based upon a qualitative spatial reasoning theory about shadows. This theory-driven threshold search and the qualitative self-localisation procedure are the main contributions of the present research. To the best of our knowledge this is the first work that uses qualitative spatial representations both to perform robot self-localisation and to calibrate a robot’s interpretation of its perceptual input.     

Sharing a verifiable secret image using two shadows

In this paper, we propose a novel (2,2) verifiable secret sharing (VSS) scheme, which not only protects a secret image but also allows users to verify the restored secret image in the revealing and verifying phase, for all binary, grayscale and color images. Error diffusion and image clustering techniques are adopted to achieve our objective. Experimental results and discussions show that the proposed scheme, with its smaller shadow size and lower computational complexity, obviously outperforms previous VSS schemes designed either with or without the cheating prevention mechanism. Moreover, the use of a halftone logo gives an efficient solution to verifying whether the restored secret image is correct by using a halftone logo.     

Learning AAM fitting through simulation

The active appearance model (AAM) is a powerful method for modeling and segmenting deformable visual objects. The utility of the AAM stems from two fronts: its compact representation as a linear object class and its rapid fitting procedure, which utilizes fixed linear updates. Although the original fitting procedure works well for objects with restricted variability when initialization is close to the optimum, its efficacy deteriorates in more general settings, with regards to both accuracy and capture range. In this paper, we propose a novel fitting procedure where training is coupled with, and directly addresses, AAM fitting in its deployment. This is achieved by simulating the conditions of real fitting problems and learning the best set of fixed linear mappings, such that performance over these simulations is optimized. The power of the approach does not stem from an update model with larger capacity, but from addressing the whole fitting procedure simultaneously. To motivate the approach, it is compared with a number of existing AAM fitting procedures on two publicly available face databases. It is shown that this method exhibits convergence rates, capture range and convergence accuracy that are significantly better than other linear methods and comparable to a nonlinear method, whilst affording superior computational efficiency.     

Learning to play games using a PSO-based competitive learning approach

A new competitive approach is developed for learning agents to play two-agent games. This approach uses particle swarm optimizers (PSO) to train neural networks to predict the desirability of states in the leaf nodes of a game tree. The new approach is applied to the TicTacToe game, and compared with the performance of an evolutionary approach. A performance criterion is defined to quantify performance against that of players making random moves. The results show that the new PSO-based approach performs well as compared with the evolutionary approach.     

A piece-wise approach to removing the nonlinear and irregular stripes in MODIS data

As a result of imaging acquisition conditions, Moderate Resolution Imaging Spectroradiometer (MODIS) imagery suffers from nonlinear and irregular striping. The nonlinear stripes are those whose degradation parameters change with the ground objects, and the irregular stripes are those in which only some of the pixels are contaminated. These kinds of stripes result in great difficulties for conventional statistical destriping methods. To deal with these problems effectively, we propose a piece-wise destriping method. This approach divides the recognized defective rows into different portions by the local statistical and mean curve information. The destriping is then performed in each portion, based on the different correction coefficients, with a neighbouring normal row as a reference. Experimental results demonstrate that the proposed algorithm can effectively destripe MODIS data.