5664063
Parallel Progressive Radiosity with Adaptive Meshing
Radiosity plays an important role in computer graphics. This paper presents an efficient parallel algorithm for progressive radiosity with adaptive meshing, which adopts a novel static processor assignment strategy and takes advantages of hierarchical computation structure in this problem to minimize communication and balance dynamic load. Our experiments on a Meiko CS-2 distributed memory machine show that this algorithm has achieved good performance for the tested cases.
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.
13369249
Reconstruction of 3-D Symmetric Curves from Perspective Images without Discrete Features
The shapes of many natural and man-made objects have curved contours. The images of such contours usually do not have sufficient distinctive features to apply conventional feature-based reconstruction algorithms. This paper shows that both the shape of curves in 3-D space and the camera poses can be accurately reconstructed from their perspective images with unknown point correspondences given that the curves have certain invariant properties such as symmetry. We show that in such cases the minimum number of views needed for a solution is remarkably small: one for planar curves and two for nonplanar curves (of arbitrary shapes), which is significantly less than what is required by most existing algorithms for general curves. Our solutions rely on minimizing the L 2-distance between the shapes of the curves reconstructed via the “epipolar geometry” of symmetric curves. Both simulations and experiments on real images are presented to demonstrate the effectiveness of our approach.
13948350
Feature matching and deformation for texture synthesis
One significant problem in patch-based texture synthesis is the presence of broken features at the boundary of adjacent patches. The reason is that optimization schemes for patch merging may fail when neighborhood search cannot find satisfactory candidates in the sample texture because of an inaccurate similarity measure. In this paper, we consider both curvilinear features and their deformation. We develop a novel algorithm to perform feature matching and alignment by measuring structural similarity. Our technique extracts a feature map from the sample texture, and produces both a new feature map and texture map. Texture synthesis guided by feature maps can significantly reduce the number of feature discontinuities and related artifacts, and gives rise to satisfactory results.
10850316
Modeling realistic virtual hairstyles
The author presents an effective method for modeling realistic curly hairstyles, taking into account both artificial hairstyling processes and natural curliness. The result is a detailed geometric model of hairs that can be rendered and animated via existing methods. Our technique exploits the analogy between hairs and a vector field; it interactively and efficiently models global and local hair flows by superimposing procedurally defined vector field primitives that have local influence. Usually only a very small number of vector field primitives are needed to model a complicated hairstyle. An initial model of hair strands is extracted from the superimposed vector fields by tracing their field lines. Random natural or artificial curliness can be added to the initial model through a parametric hair offset function with a randomized distribution of parameters over the scalp. Techniques for shearing and clustering are also designed to improve the overall appearance of the hair model. Our technique has been successfully applied to generate a variety of realistic hairstyles with different curliness and length distributions.
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.
723568
A Bayesian Network Approach to Ontology Mapping
This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs. Probabilities needed for constructing conditional probability tables (CPT) during translation and for measuring semantic similarity during mapping are learned using text classification techniques where each concept in an ontology is associated with a set of semantically relevant text documents, which are obtained by ontology guided web mining. The basic ideas of this approach are validated by positive results from computer experiments on two small real-world ontologies.
40955801
Polarimetric SAR image segmentation using texture partitioning and statistical analysis
This paper presents a new technique for partitioning polarimetric synthetic aperture radar (POL-SAR) imagery into regions of homogeneous polarimetric backscattering properties. The method consists of two cascaded steps: an initial texture segmentation of one image derived from the POL-SAR data, and a polarimetric statistical region merging process that refines and modifies the initial segmentation. In the first stage, a morphological region-based image partitioning technique, the watershed algorithm, plays the key role. While in the second stage, the region adjacency graph and the segment dissimilarity measures derived from the Wishart model and the K distribution are applied. The overall segmentation algorithm has been tested using real 4-look POL-SAR imagery and sample results are provided in the paper. The main innovation of this work is the utilization of the K distribution in the segmentation process.
15986119
Designing activities for learning software quality practices
This paper presents our design of the learning activities in a course on software quality practices. We describe how we strive to provide opportunities for students to gain hands-on experience on exemplar software quality practices in spite of various constraints.
45848865
Optimal improvement of the lower bound performance of partition testing strategies
Although partition testing strategies are intuitively more appealing than random testing, previous empirical and analytical studies show that under unfavourable circumstances partition testing can be very ineffective. The problem of maximally improving the lower bound performance of partition testing by the choice of appropriate test distributions is investigated. An algorithm that generates optimal test distributions for this purpose is proposed and analysed. Moreover, the algorithm can also serve to systematically approximate the proportional sampling strategy, which has previously been proved to be at least as good as random testing.
45980845
Optimal adaptive bandwidth monitoring for QoS based retrieval
Network aware multimedia delivery applications guarantee QoS parameters like media object transmission time limit by actively monitoring the available bandwidth of the network and adapting the object to a target size that can be transmitted within a given time limit. A critical problem is how to obtain an accurate enough estimation of available bandwidth while not wasting too much time in bandwidth testing. We present an algorithm to determine the optimal amount of bandwidth testing, given a probabilistic confidence level for network-aware multimedia retrieval. Our implementation and experiments show that the algorithm finds out the optimal amount of bandwidth testing effectively with minimum computation overhead.