6548026
Automated Mosaics via Topology Inference
The authors present a complete approach for the automated construction of mosaics from images and video using topology inference, local and global alignment, and compositing.
8291379
Video Flashlights: Real Time Rendering of Multiple Videosfor Immersive Model Visualization
Videos and 3D models have traditionally existed in separate worlds and as distinct representations. Although texture maps for 3D models have been traditionally derived from multiple still images, real-time mapping of live videos as textures on 3D models has not been attempted. This paper presents a system for rendering multiple live videos in real-time over a 3D model as a novel and demonstrative application of the power of commodity graphics hardware. The system, metaphorically called the Video Flashlight system, "illuminates" a static 3D model with live video textures from static and moving cameras in the same way as a flashlight (torch) illuminates an environment. The Video Flashlight system is also an augmented reality solution for security and monitoring systems that deploy numerous cameras to monitor a large scale campus or an urban site. Current video monitoring systems are highly limited in providing global awareness since they typically display numerous camera videos on a grid of 2D displays. In contrast, the Video Flashlight system exploits the real-time rendering capabilities of current graphics hardware and renders live videos from various parts of an environment co-registered with the model. The user gets a global view of the model and is also able to visualize the dynamic videos simultaneously in the context of the model. In particular, the location of pixels and objects seen in the videos are precisely overlaid on the model while the user navigates through the model. The paper presents an overview of the system, details of the real-time rendering and demonstrates the efficacy of the augmented reality application.
14585589
Real-time wide area multi-camera stereo tracking
We present a fully integrated real-time system to track humans with a network of stereo sensors over a wide area. The processing includes single camera tracking and multi-camera fusion. Each single camera detects and tracks humans in its own view and a multi-camera fusion module combines all the local tracks of the same human into a global track. We propose stereo segmentation and tracking techniques to handle multiple humans moving in groups in cluttered environments. We have developed a ground-based fusion method for camera handoff using space-time constraint. We show results and performance evaluation on very challenging data from a 12-camera system.
5884426
Validation of an optical flow method for tag displacement estimation
Presents a validation study of an optical-flow method for the rapid estimation of myocardial displacement in magnetic resonance tagged cardiac images. This registration and change visualization (RCV) software uses a hierarchical estimation technique to compute the flow field that describes the warping of an image of one cardiac phase into alignment with the next. This method overcomes the requirement of constant pixel intensity in standard optical-flow methods by preprocessing the input images to reduce any intensity bias which results from the reduction in stripe contrast throughout the cardiac cycle. To validate the method, SPAMM-tagged images were acquired of a silicon gel phantom with simulated rotational motion. The pixel displacement was estimated with the RCV method and the error in pixel tracking was <4% 1000 ms after application of the tags, and after 30/spl deg/ of rotation. An additional study was performed using a SPAMM-tagged multiphase slice of a canine left ventricle. The true displacement was determined using a previously validated active contour model (snakes). The error between methods was 6.7% at end systole. The RCV method has the advantage of tracking all pixels in the image in a substantially shorter period than the snakes method.
13502244
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
Decomposing video frames into coherent 2D motion layers is a powerful method for representing videos. Such a representation provides an intermediate description that enables applications such as object tracking, video summarization and visualization, video insertion, and sprite-based video compression. Previous work on motion layer analysis has largely concentrated on two-frame or multi-frame batch formulations. The temporal coherency of motion layers and the domain constraints on shapes have not been exploited. This paper introduces a complete dynamic motion layer representation in which spatial and temporal constraints on shape, motion and layer appearance are modeled and estimated in a maximum a-posteriori (MAP) framework using the generalized expectation-maximization (EM) algorithm. In order to limit the computational complexity of tracking arbitrarily shaped layer ownership, we propose a shape prior that parameterizes the representation of shape and prevents motion layers from evolving into arbitrary shapes. In this work, a Gaussian shape prior is chosen to specifically develop a near-real-time tracker for vehicle tracking in aerial videos. However, the general idea of using a parametric shape representation as part of the state of a tracker is a powerful one that can be extended to other domains as well. Based on the dynamic layer representation, an iterative algorithm is developed for continuous object tracking over time. The proposed method has been successfully applied in an airborne vehicle tracking system. Its performance is compared with that of a correlation-based tracker and a motion change-based tracker to demonstrate the advantages of the new method. Examples of tracking when the backgrounds are cluttered and the vehicles undergo various rigid motions and complex interactions such as passing, turning, and stop-and-go demonstrate the strength of the complete dynamic layer representation.
39247721
True Multi-Image Alignment and Its Application to Mosaicing and Lens Distortion Correction
Multiple images of a scene are related through 2D/3D view transformations and linear and nonlinear camera transformations. We present an algorithm for true multi-image alignment that does not rely on the measurements of a reference image being distortion free. The algorithm is developed to specifically align and mosaic images using parametric transformations in the presence of lens distortion. When lens distortion is present, none of the images can be assumed to be ideal. In our formulation, all the images are modeled as intensity measurements represented in their respective coordinate systems, each of which is related to an ideal coordinate system through an interior camera transformation and an exterior view transformation. The goal of the accompanying algorithm is to compute an image in the ideal coordinate system while solving for the transformations that relate the ideal system with each of the data images. Key advantages of the technique presented in this paper are: (i) no reliance on one distortion free image, (ii) ability to register images and compute coordinate transformations even when the multiple images are of an extended scene with no overlap between the first and last frame of the sequence, and (iii) ability to handle linear and nonlinear transformations within the same framework. Results of applying the algorithm are presented for the correction of lens distortion, and creation of video mosaics.
14392689
Composition trust bindings in pervasive computing service composition
In pervasive computing, devices or peers may implement or compose services using services from other devices or peers, and may use components from various sources. A composition trust binding is a prescriptive set of rules which defines the combination of allowable components for a particular service or application. Composition trust bindings can be used to protect both the service invocation path as well as the content handling path. The subsidiary relationships addressed by a composition trust binding are typically transparent today, but represent potential security exposure in pervasive computing systems because the subsidiary services or components may have security vulnerabilities. We define the composition trust binding and illustrate its use in the context of rights management and distributed search in personal content publishing. We compare this approach to existing authentication and authorization methods in service composition
6867265
Servo: a programming model for many-core computing
Conventional programming models were designed to be used by expert programmers for programming for large-scale multiprocessors, distributed computational clusters, or specialized parallel machines. These models, therefore, are deemed either too difficult for an "average" programmer (who will be expected to do parallel programming in the many-core world) or too inefficient to use for many-core architectures [3]. Similarly, conventional execution models were designed for performance, not scalability.
 To address the challenge of performance scalability for many-core architectures, we introduce Servo, a Service Oriented programming model that decomposes every program into a set of components (each with its own local memory, mutable state, and a program counter) that either request services or deliver services. Servo is characterized by the decoupling of logical communication mapping between program modules (or services) from physical communication mapping between modules. This allows the services to be migrated and replicated during execution. The proposed model also allows the granularity of data parallel operations to be changed dynamically. This allows runtime variation in the locking granularity which in turn enables higher write-parallelism. Finally, the models partition even data into services. This can significantly enhance locality and makes it possible to have superlinear speedups with increasing number of cores.
 Our preliminary investigations demonstrate significant performance scalability advantages for Servo.
18669485
A QoS routing protocol for delay-sensitive applications in mobile ad hoc networks
The Ad hoc On Demand Distance Vector (AODV) routing protocol is intended for use by mobile nodes in ad hoc networks. To provide quality of service, extensions can be added to the messages used during route discovery. These extensions specify the service requirements which must be met by nodes rebroadcasting a route request (RREQ) or returning a route reply (RREP) for a destination. In order to provide quality delivery to delay sensitive applications such as voice and video, it is extremely important that mobile ad hoc networks provide quality of service (QoS) support in terms of bandwidth and delay. Most existing routing protocols for mobile ad hoc networks are designed to search for the shortest path with minimum hop count. However, the shortest routes do not always provide the best performance, especially when there are congested nodes along these routes. In this paper we propose an on demand delay based quality of service (QoS) routing protocol (AODV-D) to ensure that delay does not exceed a maximum value for mobile ad hoc networks. This protocol will take into consideration MAC layer channel contention information and number of packets in the interface queue in addition to minimum hops. The protocol is implemented and simulated using GlomoSim simulator. Performance comparisons of the proposed AODV-D protocol against AODV, and QS-AODV is presented and shown that the proposed algorithm performs well.
16117863
A Comparative Study of Different Sensors for Smart Car Park Management
Wireless sensor networks offer a very attractive, low cost alternative for traffic monitoring and surveillance in car parking, freeways and intersection in different environmental conditions. In such systems, robust and reliable vehicle detection is a critical step, thus it is important to carefully evaluate the different modes of operations in order to address existing issues. This paper proposes a vision of improvements to wireless sensor car parking network system by presenting valuable comparison data for different sensors using different type of software functions and hardware components. The main goal of this work is to present a simple, automated analysis and comparison of power management, communication efficiency and threshold times using different sensors and a corresponding prediction of the above features. The results show a clear difference in various parameters by using different types of sensors for vehicle detection.
65135986
Model dependent inference of three-dimensional information from a sequence of two-dimensional images
In order to autonomously navigate through a complex environment, a mobile robot requires sensory feedback. This feedback will typically include the 3D motion and location of the robot and the 3D structure and motion of obstacles and other environmental features. The general problem considered in this thesis is how this 3D information may be obtained from a sequence of images generated by a camera mounted on a mobile robot. 
The first set of algorithms developed in this thesis are for robust determination of the 3D pose of the mobile robot from a matched set of model and image landmark features. Least-squares techniques for point and line tokens, which minimize both rotation and translation simultaneously are developed and shown to be far superior to the earlier techniques which solved for rotation first and then translation. However, least-squares techniques fail catastrophically when outliers (or gross errors) are present in the match data. Outliers arise frequently due to incorrect correspondences or gross errors in the 3D model. Robust techniques for pose determination are developed to handle data contaminated by fewer than 50.0% outliers. 
To make the model based approach widely applicable, it is necessary to be able to automatically build the landmark models. The approach adopted in this thesis is one of model extension and refinement. A partial model of the environment is assumed to exist and this model is extended over a sequence of frames. As will be shown in the experiments, the prior knowledge of the small partial model greatly enhances the robustness of the 3D structure computations. The initial 3D model may have errors and these too are refined over the sequence of frames. 
Finally, the sensitivity of pose determination and model extension to incorrect estimates of camera parameters is analyzed. It is shown that for small field of view systems, offsets in the image center do not significantly affect the location of the camera and the location of new 3D points in a world coordinate system. Errors in the focal length significantly affect only the component of translation along the optical axis in the pose computation.