2702895
Single-image reflectance estimation for relighting by iterative soft grouping
Reflectance values for image-based relighting are often estimated from grouped pixels with similar reflectance, but such groupings are difficult to compute with certainty for sparse image data. To address this problem, we propose an iterative method that aggregates BRDF data in a single image with known geometry and lighting by soft grouping, where pixels contribute to one another's estimate according to their degree of reflectance similarity. Estimation of specular reflectance is further improved by albedo-independent soft grouping of pixels based on shape continuity. With recovered reflectances, we demonstrate realistic relighting for synthetic and real scenes, including surfaces with spatially-varying reflectance.
14760859
Multiple-cue illumination estimation in textured scenes
In this paper, we present a method that integrates cues from shading, shadow and specular reflections for estimating directional illumination in a textured scene. Texture poses a problem for lighting estimation, since texture edges can be mistaken for changes in illumination condition, and unknown variations in albedo make reflectance model fitting impractical. Unlike previous works which all assume known or uniform reflectance, our method can deal with the effects of textures by capitalizing on physical consistencies that exist among the lighting cues. Since scene textures do not exhibit such coherence, we use this property to minimize the influence of texture on illumination direction estimation. For the recovered light source directions, a technique for estimating their intensities in the presence of texture is also proposed.
13422174
Motion Planning of Multiple Agents in Virtual Environments on Parallel Architectures
We proposed in a previous paper (2006) a hybrid two-layered approach for motion planning of multiple agents in static virtual environments, consisting of open spaces connected by multiple narrow passages. The discrete generalized Voronoi diagram (GVD) of the environment is used to identify narrow passages, and plan the global path of each agent independently of other agents' global paths. As each agent moves along its global path, the agent's path is locally modified using the hybrid technique of combining steering behaviors with Coordination Graphs (CG), where coordination graphs are used for deadlock avoidance in the narrow passages. The planner in the previous paper was single threaded, and it was able to plan the motions of 30 agents moving around in a simple virtual environment with 3 narrow passages. If more agents are moving in a more complex virtual environment (i.e., with more narrow passages), we may not be able to construct and process all the coordination graphs in real-time. In this paper, we parallelize the single threaded planner in a supervisor-worker paradigm with Unix processes who communicate with each other using System V interprocess communication (IPC) mechanism. We show that significant, scalable speedups are obtained by constructing and processing coordination graphs in parallel on a symmetric multiprocessing (SMP) system.
32291231
Face Recognition with Region Division and Spin Images
This paper explores how spin images can be constructed using shape-from-shading information and used for the purposes of face recognition. We commence by extracting needle-maps from gray-scale images of faces, using a mean needle-map to enforce the correct pattern of facial convexity and concavity. Spin images [4] are estimated from the needle maps using local spherical geometry to approximate the facial surface. Our representation is based on the spin image histograms for an arrangement of image patches. We demonstrate how this representation can be used to perform face recognition across different subjects and illumination conditions. Experiments show the method to be reliable and accurate, and the recognition precision reaches 98% on CMU PIE sub- database.
24872800
Face Recognition using Patch-based Spin Images
This paper explores how spin images can be constructed using shape-from-shading information and used for the purposes of face recognition. We commence by extracting needle-maps from gray-scale images of faces, using a mean needle-map to enforce the correct pattern official convexity and concavity. Spin images (Johnson and Hebert, 1999) are estimated from the needle maps using local spherical geometry to approximate the facial surface. Our representation is based on the spin image histograms for an arrangement of image patches. We demonstrate how this representation can be used to perform face recognition across different subjects and illumination conditions. Experiments show the method to be reliable and accurate
1095698
DaNaLIX: a domain-adaptive natural language interface for querying XML
We present DaNaLIX, a prototype domain-adaptive natural language interface for querying XML. Our system is an extension of NaLIX, a generic natural language interface for querying XML. While retaining the portability of a purely generic system like NaLIX, DaNaLIX can exploit domain knowledge, whenever available, to its advantage for query translation. More importantly, in DaNaLIX such domain knowledge does not have to be pre-defined; instead it can be automatically obtained from the interactions between a user and the system. In this demonstration, we describe the overall architecture of DaNaLIX. We also demonstrate how a generic system like DaNaLIX can take advantage of domain knowledge to improve its usability and query translation accuracy. In addition, we show DaNaLIX still possesses the portability of a generic system by using data collections from three different domains. Finally, we present how domain knowledge can be obtained through user interactions in an automatic fashion.
21282948
Information-Based Cooperation in Multiple Agent Systems
In this paper, we divide the cooperation in multiple agent systems into task-based cooperation and information-based cooperation. We describe information-based cooperation as information synthesizing and decision making. To implement information synthesizing, agents' beliefs are decomposed into two levels. The higher level represents the possible information, and the lower level subsequently estimates a number function for the belief by synthesizing the possible information.