Incremental Focus of Attention for Robust Vision-Based Tracking

We present the Incremental Focus of Attention (IFA) architecture for robust, adaptive, real-time motion tracking. IFA systems combine several visual search and vision-based tracking algorithms into a layered hierarchy. The architecture controls the transitions between layers and executes algorithms appropriate to the visual environment at hand: When conditions are good, tracking is accurate and precise; as conditions deteriorate, more robust, yet less accurate algorithms take over; when tracking is lost altogether, layers cooperate to perform a rapid search for the target and continue tracking.Implemented IFA systems are extremely robust to most common types of temporary visual disturbances. They resist minor visual perturbances and recover quickly after full occlusions, illumination changes, major distractions, and target disappearances. Analysis of the algorithm's recovery times are supported by simulation results and experiments on real data. In particular, examples show that recovery times after lost tracking depend primarily on the number of objects visually similar to the target in the field of view.     

Visual Focus of Attention in Non-calibrated Environments using Gaze Estimation

Estimating the focus of attention of a person highly depends on her/his gaze directionality. Here, we propose a new method for estimating visual focus of attention using head rotation, as well as fuzzy fusion of head rotation and eye gaze estimates, in a fully automatic manner, without the need for any special hardware or a priori knowledge regarding the user, the environment or the setup. Instead, we propose a system aimed at functioning under unpretending conditions, only with the usage of simple hardware, like a normal web-camera. Our system is aimed at functioning in a human-computer interaction environment, considering a person is facing a monitor with a camera adjusted on top. To this aim, we propose in this paper two novel techniques, based on local and appearance information, estimating head rotation, and we adaptively fuse them in a common framework. The system is able to recognize head rotational movement, under translational movements of the user towards any direction, without any knowledge or a-priori estimate of the user’s distance from the camera or camera intrinsic parameters.     

从"全国计算机信息高新技术考试试题汇编"CAD题解谈CAD矢量图绘制应注意的问题

本文针对"全国计算机信息高新技术考试试题汇编"CAD题解,谈了几点CAD矢量图绘制应注意的问题.     

Predicting Visual Focus of Attention From Intention in Remote Collaborative Tasks

While shared visual space plays a very important role in remote collaboration on physical tasks, it is challenging and expensive to track users' focus of attention (FOA) during these tasks. In this paper, we propose to identify a user's FOA from his/her intention based on task properties, people's actions in the workspace, and conversational content. We employ a conditional Markov model to characterize a subject's FOA. We demonstrate the feasibility of the proposed method using a collaborative laboratory task in which one partner (the helper) instructs another (the worker) on how to assemble online puzzles. We model a helper's FOA using task properties, workers' actions, and conversational content. The accuracy of the model ranged from 65.40% for puzzles with easy-to-name pieces to 74.25% for puzzles with more difficult-to-name pieces. The proposed model can be used to predicate a user's FOA in a remote collaborative task without tracking the user's eye gaze.      

Recognizing Visual Focus of Attention From Head Pose in Natural Meetings

We address the problem of recognizing the visual focus of attention (VFOA) of meeting participants based on their head pose. To this end, the head pose observations are modeled using a Gaussian mixture model (GMM) or a hidden Markov model (HMM) whose hidden states correspond to the VFOA. The novelties of this paper are threefold. First, contrary to previous studies on the topic, in our setup, the potential VFOA of a person is not restricted to other participants only. It includes environmental targets as well (a table and a projection screen), which increases the complexity of the task, with more VFOA targets spread in the pan as well as tilt gaze space. Second, we propose a geometric model to set the GMM or HMM parameters by exploiting results from cognitive science on saccadic eye motion, which allows the prediction of the head pose given a gaze target. Third, an unsupervised parameter adaptation step not using any labeled data is proposed, which accounts for the specific gazing behavior of each participant. Using a publicly available corpus of eight meetings featuring four persons, we analyze the above methods by evaluating, through objective performance measures, the recognition of the VFOA from head pose information obtained either using a magnetic sensor device or a vision-based tracking system. The results clearly show that in such complex but realistic situations, the VFOA recognition performance is highly dependent on how well the visual targets are separated for a given meeting participant. In addition, the results show that the use of a geometric model with unsupervised adaptation achieves better results than the use of training data to set the HMM parameters.     

The Enhanced Restricted Focus Viewer

The Enhanced Restricted Focus Viewer (ERFV) is a unique software tool for tracking the visual attention of users in hyperlinked environments, such as Web sites. The software collects data, such as mouse clicks along with the path of users' visual attention, as they browse a site. Unlike traditional eye-tracking procedures, the ERFV requires no hardware to operate other than a personal computer. In addition to time and cost savings, the ERFV allows the administration of usability testing to groups of participants simultaneously. A laboratory test comparing the ERFV to a hardware-based eye-tracking system showed that the two methods compared favorably in terms of how well they track a user's visual attention. The value of the ERFV as a usability testing tool was demonstrated through an experiment that evaluated two Web sites that were equivalent in content but that differed in terms of design. Although several open issues concerning the ERFV still remain, some of these issues are being addressed through ongoing research efforts.     

The Problem Statement is the Problem

A problem statement encouraging elegance is spare, unadorned, clean, and leaves the designer as much room as can be left. This is the hardest part of any design process. A good problem statement is a mentor and a supervisor. It asks the right question. A good problem statement extends its writer's skill and wisdom so that, if those are in short supply, the problem statement can make up for some of that itself.     

网站建设中应注意的安全问题

目前,全国各行各业建立的网站已经越来越多,网站建设安全问题引起了人们广泛关注.本文先对网站建设应注意的安全问题及原因进行了分析,继而对应采用的安全技术进行了探析,提出了自己的思考.要从硬件软件多方面协调配置,提高其抗恶意攻击和病毒传播的能力.要把网络安全作为网站发展的一个重要步骤.     

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TOP榜美国五大最危险计算机黑客出炉国外媒体近日评出了美国有史以来最危险的五大计算机黑客,他们年纪不大就成为罪犯并被捕。其中一些人从事黑客行为是为了赚钱,而另外一些则仅仅是出于兴趣。     

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