With the advance of pedagogical materials from printed textbooks to e-textbooks, the methods of note-taking should also be improved. For e-Learning with 3D interactive curricular contents, an ideal note-taking approach should be intuitive and tightly coupled with the curricular contents. Particularly, augmented reality (AR) technology is capable of displaying virtual contents in real-life images. Combining head-mounted displays with cameras and wearable computers, AR provides chances and challenges to improve note-taking for situated learning in contextual surroundings. We propose an AR-based note-taking system tailored for 3D curricular contents. A learner can take notes on a physical tabletop by finger writing, manipulate curricular contents using hand gestures and embed the complete notes in the corresponding contents in a 3D space. An analytic hierarchy process demonstrates the strengths and weaknesses of the proposed 3D note-taking system. Especially, note-taking using finger writing and hand gestures with 3D maneuver is better than other alternatives in terms of relevance, usefulness, intuition and novelty.

Augmented Reality (AR) is a technology that merges virtual objects with real-world images seamlessly. The power of real-time interaction and complete immersion makes AR ideal for language learning in that the exposure and motivation are two important factors. Therefore, we propose a Virtual English Classroom empowered with marker less AR technologies and 3D gesture interactions, called VECAR. The proposed vision-based interface is intuitive and convenient for users to play around with free hands, without the need of keyboard and mouse. The objective of VECAR is to make English teaching more interesting to increase learners' motivations.

Vision‐based traffic surveillance plays an important role in traffic management. However, outdoor illuminations, the cast shadows and vehicle variations often create problems for video analysis and processing. Thus, the authors propose a real‐time cost‐effective traffic monitoring system that can reliably perform traffic flow estimation and vehicle classification at the same time. First, the foreground is extracted using a pixel‐wise weighting list that models the dynamic background. Shadows are discriminated utilising colour and edge invariants. Second, the foreground on a specified check‐line is then collected over time to form a spatial–temporal profile image. Third, the traffic flow is estimated by counting the number of connected components in the profile image. Finally, the vehicle type is classified according to the size of the foreground mask region. In addition, several traffic measures, including traffic velocity, flow, occupancy and density, are estimated based on the analysis of the segmentation. The availability and reliability of these traffic measures provides critical information for public transportation monitoring and intelligent traffic control. Since the proposed method only process a small area close to the check‐line to collect the spatial–temporal profile for analysis, the complete system is much more efficient than existing visual traffic flow estimation methods.

Abstract Computer graphics animation often lacks interaction between rigid object and granular material. In this paper, we propose a method for the deformation of the ground surface that consists of granular material when it is penetrated by a rigid body object in motion. Meanwhile, the motion of the rigid object is also affected due to the collision with the ground surface. Our simulation model concerns: updating the motion of object, the collision detection between the rigid object and the ground surface, the distribution of the ground granular material and the deformation of the ground surface. Our contribution is that we present a method to simulate the interaction between the ground granular material and the rigid body object in motion. Moreover, a render to texture method is presented to accelerate the ray casting collision detection between the ground surface and the object. And, our implementation for the method can be simulated at interactive frame rates. Copyright © 2007 John Wiley & Sons, Ltd.

A computer vision-based system using images from an airborne aircraft can increase flight safety by aiding the pilot to detect obstacles in the flight path so as to avoid mid-air collisions. Such a system fits naturally with the development of an external vision system proposed by NASA for use in high-speed civil transport aircraft with limited cockpit visibility. The detection techniques should provide high detection probability for obstacles that can vary from subpixels to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter. Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. For this purpose, we have implemented a number of algorithms to detect airborne obstacles using image sequences obtained from a camera mounted on an aircraft. This paper describes the methodology used for characterizing the performance of the dynamic programming obstacle detection algorithm and its special cases. The experimental results were obtained using several types of image sequences, with simulated and real backgrounds. The approximate performance of the algorithm is also theoretically derived using principles of statistical analysis in terms of the signal-to-noise ration (SNR) required for the probabilities of false alarms and misdetections to be lower than prespecified values. The theoretical and experimental performance are compared in terms of the required SNR.

The traditional e-learning systems are generally less attractive to students due to their lack of 3D immersion and real voice interaction. The technology of virtual reality can be exploited to compensate these weaknesses. We propose a realistic and interactive virtual English classroom entitled VEC3D by integrating vivid 3D graphics and real-time voice communication. The goal of VEC3D aims to help undergraduate students develop the overall English communicative competence in listening, speaking, reading and writing.

Network Of Workstations (NOW) platforms put together with off-the-shelf workstations and networking hardware have become a cost effective, scalable, and flexible platform for video processing applications. Still, one has to manually schedule an algorithm to the available processors of the NOW to make efficient use of the resources. However, this approach is time-consuming and impractical for a video processing system that must perform a variety of different algorithms, with new algorithms being constantly developed. Improved support for program development is absolutely necessary before the full benefits of parallel architectures can be realized for video processing applications. Toward this goal, an automatic compile-time scheduler has been developed to schedule input tasks of video processing applications with precedence constraints onto available processors. The scheduler exploits both spatial (parallelism) and temporal (pipelining) concurrency to make the best use of machine resources. Two important scheduling problems are addressed. First, given a task graph and a desired throughput, a schedule is constructed to achieve the desired throughput with the minimum number of processors. Second, given a task graph and a finite set of available resources, a schedule is constructed such that the throughput is maximized while meeting the resource constraints. Results from simulations show that the scheduler and proposed optimization techniques effectively tackle these problems by maximizing processor utilization. A code generator has been developed to generate parallel programs automatically. The tools developed in this paper make it much easier for a programmer to develop video processing applications on these parallel architectures.

Many computes vision applications are computationally challenging especially when they need to meet real-time constraints. A major problem with special purpose systems is that they require the developers of image-processing applications to be aware of the low-level hardware design, making the task cumbersome. To avoid inflexible and expensive hardware designs, another possible alternative is a network of workstations (NOW) platform put together with off-the-shelf workstations and networking hardware. Still, one had to manually schedule an algorithm to the available processors of the NOW to make efficient use of the resources. However, this approach is time consuming and impractical for a vision system that must perform a variety of different algorithms, with new algorithms being constantly developed. Improved support for program development is absolutely necessary before the full benefits of parallel architectures can be realized for vision applications. Towards this goal, an automatic compile-time scheduler has been developed to schedule input tasks of vision applications with precedence constraints onto available processors. The scheduler exploits both spatial (parallelism) and temporal (pipelining) concurrency to make the best use of machine resources. Two important scheduling problems are addressed. First, given a task graph and a desired throughput, a schedule is constructed to achieve the desired throughput with the minimum number of processors. Second, given a task graph and a finite set of available resources, a schedule is constructed such that the throughput is maximized while meeting the resource constraints. Results from simulations show that the scheduler and proposed optimization techniques effectively tackle these problems by maximizing the processor utilization. A code generator has been developed to generate parallel programs automatically. The execution profiles of the resulting parallel programs demonstrate the feasibility of the scheduler. The tools developed in this paper make it much easier for a programmer to develop vision applications on these high-performance platforms.

The National Aeronautics and Space Administration (NASA), along with members of the aircraft industry, recently developed technologies for a new supersonic aircraft. One of the technological areas considered for this aircraft is the use of video cameras and image processing equipment to aid the pilot in detecting other aircraft in the sky. The detection techniques should provide high detection probability for obstacles that can vary from sub-pixel to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. This paper describes approaches to detect airborne obstacles on collision course and crossing trajectories in video images captured from an airborne aircraft. In both cases the approaches consist of an image processing stage to identify possible obstacles followed by a tracking stage to distinguish between true obstacles and image clutter, based on their behavior. The crossing target detection algorithm was also implemented on a pipelined architecture from DataCube and runs in real time. Both algorithms have been successfully tested on flight tests conducted by NASA.