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.
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.
7413311
A fast multigrid algorithm for mesh deformation
In this paper, we present a multigrid technique for efficiently deforming large surface and volume meshes. We show that a previous least-squares formulation for distortion minimization reduces to a Laplacian system on a general graph structure for which we derive an analytic expression. We then describe an efficient multigrid algorithm for solving the relevant equations. Here we develop novel prolongation and restriction operators used in the multigrid cycles. Combined with a simple but effective graph coarsening strategy, our algorithm can outperform other multigrid solvers and the factorization stage of direct solvers in both time and memory costs for large meshes. It is demonstrated that our solver can trade off accuracy for speed to achieve greater interactivity, which is attractive for manipulating large meshes. Our multigrid solver is particularly well suited for a mesh editing environment which does not permit extensive precomputation. Experimental evidence of these advantages is provided on a number of meshes with a wide range of size. With our mesh deformation solver, we also successfully demonstrate that visually appealing mesh animations can be generated from both motion capture data and a single base mesh even when they are inconsistent.
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.
207688593
Inverse global illumination: recovering reflectance models of real scenes from photographs
In this paper we present a method for recovering the reflectance properties of all surfaces in a real scene from a sparse set of photographs, taking into account both direct and indirect illumination. The result is a lighting-independent model of the scene’s geometry and reflectance properties, which can be rendered with arbitrary modifications to structure and lighting via traditional rendering methods. Our technique models reflectance with a lowparameter reflectance model, and allows diffuse albedo to vary arbitrarily over surfaces while assuming that non-diffuse characteristics remain constant across particular regions. The method’s input is a geometric model of the scene and a set of calibrated high dynamic range photographs taken with known direct illumination. The algorithm hierarchically partitions the scene into a polygonal mesh, and uses image-based rendering to construct estimates of both the radiance and irradiance of each patch from the photographic data. The algorithm computes the expected location of specular highlights, and then analyzes the highlight areas in the images by running a novel iterative optimization procedure to recover the diffuse and specular reflectance parameters for each region. Lastly, these parameters are used in constructing high-resolution diffuse albedo maps for each surface. The algorithm has been applied to both real and synthetic data, including a synthetic cubical room and a real meeting room. Rerenderings are produced using a global illumination system under both original and novel lighting, and with the addition of synthetic objects. Side-by-side comparisons show success at predicting the appearance of the scene under novel lighting conditions. CR Categories: I.2.10 [Artificial Intelligence]: Vision and Scene Understanding—modeling and recovery of physical attributes I.3.7 [Computer Graphics]: Three-dimensional Graphics and Realism—color, shading, shadowing, and texture I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism— Radiosity I.4.8 [Image Processing]: Scene Analysis—Color, photometry, shading
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.
1347392
Explaining Low-Level Brightness-Contrast Illusions Using Disinhibition
Conventional Difference of Gaussian (DOG) filter is usually used to model the early stage of visual processing. However, convolution operation used with DOG does not explicitly account for the effects of disinhibition. Because of this, complex brightness-contrast (B-C) illusions such as the White’s effect cannot be explained using DOG filters. We discovered that a model based on lateral disinhibition in biological retinas allows us to explain subtle B-C illusions. Further, we show that a feedforward filter can be derived to achieve this operation in a single pass. The results suggest that contextual effects can be processed through recurrent disinhibition. Such a context sensitive structure might be employed to improve network robustness of visual capturing or displaying systems. Another potential application of this algorithm could be automatic detection and correction of perceived incoherences where accurate perception of intensity level is critical.
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.
866390
Comparing Web Services with other Software Components
Software metrics are vital for the management of software development, especially when a new technology is being adopted and established practices have yet to emerge. As a kind of software components, Web service technology has flourished and attracted a flurry of research activities. Despite the vast amount of research on mechanisms of Web services, there have been little investigations of the overall nature of existing Web services from a software component point of view. This paper is the first attempt to compare Web services with other software components in terms of established metrics in software engineering, including object oriented metrics and interface metrics. In this study we conclude that there are statistical differences between the interface, variable name and other OO metrics when one compares a large sample of Web services with typical OO systems. The distributions obtained give insight into the typical characteristics of Web services and can be used to identify candidates for wrapping into Web services.
14679432
On Goal-based Variability Acquisition and Analysis
We introduce a variability-intensive approach to goal decomposition that is tailored to support requirements identification for highly customizable software. The approach is based on the semantic characterization of OR-decompositions of goals. We first show that each high-level goal can be associated with a set of concerns, in response to which, alternative refinements of the goal can be introduced. A text corpus relevant to the domain of discourse can be used to derive such variability concerns that are specific to the problem. In parallel, contextual facts that can vary while a goal is being fulfilled are modeled. Then, a high-variability goal model is constructed aiming at responding to the predefined variability concerns completely, while contextual factors are used to test whether it addresses all realistic background circumstances. We apply our approach in a study from the geriatric health care domain
10484545
Visualizing the Iteration Space in PEFPT
Sufficient and precise semantic information is essential to interactive parallel programming. In this paper, we present a feasible implementation of the iteration space dependence graph and discuss relevant technical problems. Moreover, we give a further prospect of interactive loop parallelization guided by the graph.