46180
Efficient visibility processing for projective texture mapping
Abstract Projective texture mapping is a powerful tool for image-based rendering. However, visibility information must be obtained in order to project correct textures onto the geometric sturcture. This paper presents a novel visibility algorithm for determining which polygons are visible from each camera position. It has object-space precision, but operates in both image-space and object-space to get better performance. It generates much less polygons than traditional object-space algorithms to help accelerate the speed of texture mapping. The techniques used to achieve these are conservative image-space occlusion testing and object-space shallow clipping . This algorithm has been successfully used to generate large-scale image-based rendering of a whole university campus, and image-based rendering of architectures under dynamic lighting.
11797790
Two-Level Image Segmentation Based on Region and Edge Integration
This paper introduces a two-level approach for image seg- mentation based on region and edge integration. Edges are first detected in the original image using a combination of operators for intensity gra- dient and texture discontinuities. To preserve the spatial coherence of the edges and their surrounding image regions, the detected edges are vectorized into connected line segments which serve as the basis for a constrained Delaunay triangulation. Segmentation is first performed on the triangulation using graph cuts. Our method favors segmentations that pass through more vectorized line segments. Finally, the obtained segmentation on the triangulation is projected onto the original image and region boundaries are refined to achieve pixel accuracy. Experimen- tal results show that the two-level approach can achieve accurate edge localization, better spatial coherence and improved efficiency.
123116880
Shadow Graphs and 3D Texture Reconstruction
We present methods for recovering surface height fields such as geometric details of 3D textures by incorporating shadow constraints. We introduce shadow graphs which give a new graph-based representation for shadow constraints. It can be shown that the shadow graph alone is sufficient to solve the shape-from-shadow problem from a dense set of images. Shadow graphs provide a simpler and more systematic approach to represent and integrate shadow constraints from multiple images. To recover height fields from a sparse set of images, we propose a method for integrated shadow and shading constraints. Previous shape-from-shadow algorithms do not consider shading constraints while shape-from-shading usually assumes there is no shadow. Our method is based on collecting a set of images from a fixed viewpoint as a known light source changes its position. It first builds a shadow graph from shadow constraints from which an upper bound for each pixel can be derived if the height values of a small number of pixels are initialized correctly. Finally, a constrained optimization procedure is designed to make the results from shape-from-shading consistent with the height bounds derived from the shadow constraints. Our technique is demonstrated on both synthetic and real imagery.
3019671
Extracting Objects from Range and Radiance Images
In this paper, we present a pipeline and several key techniques necessary for editing a real scene captured with both cameras and laser range scanners. We develop automatic algorithms to segment the geometry from range images into distinct surfaces, register texture from radiance images with the geometry, and synthesize compact high-quality texture maps. The result is an object-level representation of the scene which can be rendered with modifications to structure via traditional rendering methods. The segmentation algorithm for geometry operates directly on the point cloud from multiple registered 3D range images instead of a reconstructed mesh. It is a top-down algorithm which recursively partitions a point set into two subsets using a pairwise similarity measure. The result is a binary tree with individual surfaces as leaves. Our image registration technique performs a very efficient search to automatically find the camera poses for arbitrary position and orientation relative to the geometry. Thus, we can take photographs from any location without precalibration between the scanner and the camera. The algorithms have been applied to large-scale real data. We demonstrate our ability to edit a captured scene by moving, inserting, and deleting objects.
1372639
Motion Field Estimation for Temporal Textures
This paper presents a novel approach for estimating the flow fields of dynamic temporal textures whose motion differs radically from that of rigid bod- ies. Our approach is based on a local flow probability distribution function at each pixel using the STAR model and the data from a spatio-temporal neighborhood. The peak of this density function can be regarded as the estimated local flow vec- tor. Our complete algorithm exploits a two-stage process. The first stage of the algorithm applies a simple tensor method to estimate the direction of the optical flow at each pixel in the texture. In the second stage, the flow probability function is used to perform a one-dimensional search along the flow direction to obtain the magnitude. Performance analysis and experiments with real video sequences show that our methods can successfully estimate flow fields.
1542177
Out-of-core tensor approximation of multi-dimensional matrices of visual data
Tensor approximation is necessary to obtain compact multilinear models for multi-dimensional visual datasets. Traditionally, each multi-dimensional data item is represented as a vector. Such a scheme flattens the data and partially destroys the internal structures established throughout the multiple dimensions. In this paper, we retain the original dimensionality of the data items to more effectively exploit existing spatial redundancy and allow more efficient computation. Since the size of visual datasets can easily exceed the memory capacity of a single machine, we also present an out-of-core algorithm for higher-order tensor approximation. The basic idea is to partition a tensor into smaller blocks and perform tensor-related operations blockwise. We have successfully applied our techniques to three graphics-related data-driven models, including 6D bidirectional texture functions, 7D dynamic BTFs and 4D volume simulation sequences. Experimental results indicate that our techniques can not only process out-of-core data, but also achieve higher compression ratios and quality than previous methods.
495865
Achieving Wireline Random Access Throughput in Wireless Networking Via User Cooperation
Well appreciated at the physical layer, user cooperation is introduced here as a diversity enabler for wireless random access (RA) at the medium access control sublayer. This is accomplished through a two-phase protocol in which active users start with a low power transmission attempting to reach nearby users and follow up with a high power transmission in cooperation with the users recruited in the first phase. We show that such a cooperative protocol yields a significant increase in throughput. Specifically, we prove that for networks with a large number of users, the throughput of a cooperative wireless RA network operating over Rayleigh-fading links approaches the throughput of an RA network operating over additive white Gaussian noise links-thus justifying the title of the paper. The message borne out of this result is that user cooperation offers a viable choice for migrating diversity benefits to the wireless RA regime, thus bridging the gap to wireline RA networks, without incurring a bandwidth or energy penalty
8525620
Failure insensitive routing for ensuring service availability
Intra-domain routing protocols employed in the Internet route around failed links by having routers detect adjacent link failures, exchange link state changes, and recompute their routing tables. Due to several delays in detection, propagation and recomputation, it may take tens of seconds to minutes after a link failure to resume forwarding of packets to the affected destinations. This discontinuity in destination reachability adversely affects the quality of continuous media applications such as Voice over IP. Moreover, the resulting service unavailability for even a short duration could be catastrophic in the world of e-commerce. Though careful tuning of the various parameters of the routing protocols can accelerate convergence, it may cause instability when the majority of the failures are transient. To improve the failure resiliency without jeopardizing the routing stability, we propose a local rerouting based approach called failure insensitive routing. Under this approach, upon a link failure, adjacent router suppresses global updating and instead initiates local rerouting. All other routers infer potential link failures from the packet's incoming interface, precompute interface specific forwarding We demonstrate that the proposed approach provides higher service availability than the existing routing schemes.
5687277
A self-adaptive scheduling algorithm of on-demand broadcasts
In mobile wireless systems data on air can be accessed by a large number of mobile users. Many of these applications such as wireless internets and traffic information systems are pull-based, that is, they respond to on-demand user requests. In this paper, we study the scheduling problems of on-demand broadcast environments. Traditionally, the response time of the requests has been used as a performance measure. In this paper we consider the performance as the average cost of request composed of three kinds of costs—access time cost, tuning time cost, and cost of handling failure request. Our main contribution is a self-adaptive scheduling algorithm named LDFC, which computes the delay cost of data item as the priority for broadcast. It performs well compared with some previous algorithms in this context.
13911639
A Cost-Efficient Scheduling Algorithm of On-Demand Broadcasts
In mobile wireless systems data on air can be accessed by a large number of mobile users. Many of these applications including wireless internets and traffic information systems are pull-based, that is, they respond to on-demand user requests. In this paper, we study the scheduling problems of on-demand broadcast environments. Traditionally, the response time of the requests has been used as a performance measure. In this paper we consider the performance as the average cost of request composed of three kinds of costs – access time cost, tuning time cost, and cost of handling failure request. Our main contribution is a self-adaptive scheduling algorithm named LDFC, which computes the delay cost of data item as the priority of broadcast. It costs less compared with some previous algorithms in this context, and shows good adaptability as well even in pure push-based broadcasts.
8259100
Adapt Bagging to Nearest Neighbor Classifiers
It is well-known that in order to build a strong ensemble, the component learners should be with high diversity as well as high accuracy. If perturbing the training set can cause significant changes in the component learners constructed, then Bagging can effectively improve accuracy. However, for stable learners such as nearest neighbor classifiers, perturbing the training set can hardly produce diverse component learners, therefore Bagging does not work well. This paper adapts Bagging to nearest neighbor classifiers through injecting randomness to distance metrics. In constructing the component learners, both the training set and the distance metric employed for identifying the neighbors are perturbed. A large scale empirical study reported in this paper shows that the proposed BagInRand algorithm can effectively improve the accuracy of nearest neighbor classifiers.