6584448
Modeling and deformation of arms and legs based on ellipsoidal sweeping
We present a new approach to the modeling and deformation of a human or virtual character's arm and legs. Each limb is represented as a set of ellipsoids of varying size interpolated along a skeleton curve. A base surface is generated by approximating these ellipsoids with a swept ellipse, and the difference between that and the detailed shape of the arm or leg is represented as a displacement map. We demonstrate that the natural bending of arms and legs can be emulated using this approach, and show its effectiveness by articulating the limbs of a scanned human body and those of a virtual character.
7105370
Realistic human hand deformation
We present a new approach to realistic hand modeling and deformation with real-time performance. We model the underlying shape of a human hand by means of sweeps which follow a simplified skeleton. The resulting swept surfaces are blended, and an auxiliary surface is then bound to the swept representation in the palm region. In the areas of this palm-control surface where bulges occur in certain poses of a real hand, the vertices are given their own trajectories, so that the palm forms realistic shapes as the joints bend. Palm lines can also be modeled as valleys in the skin by sketching them on a displacement map on the palm-control surface, and activitating them when appropriate joint movements take place. Self-intersections and collisions are detected using geometric primitives that are automatically generated from, and deform with, the sweeps and palm surface. Our algorithm runs in real time, and the naturalism of its results are demonstrated by comparative images of modeled and real hands, including several challenging poses. Copyright © 2006 John Wiley & Sons, Ltd.
12220012
Sweep-based human deformation
We present a sweep-based approach to human body modeling and deformation. A rigid 3D human model, given as a polygonal mesh, is approximated with control sweep surfaces. The vertices on the mesh are bound to nearby sweep surfaces and then follow the deformation of the sweep surfaces as the model bends and twists its arms, legs, spine and neck. Anatomical features including bone-protrusion, muscle-bulge, and skin-folding are supported by a GPU-based collision detection procedure. The volumes of arms, legs, and torso are kept constant by a simple control using a volume integral formula for sweep surfaces. We demonstrate the effectiveness of this sweep-based human deformation in several test animation clips.
11748055
A New Search Algorithm using Autonomous and Cooperative Multiple Sensor Nodes
In this paper, we study search algorithms for using a set of autonomous and cooperative mobile sensor nodes (MSN) with limited sensing and communication ranges to search a large area. Our objectives include minimizing the total search time and the travel distance of MSNs while enabling fault tolerance to possible MSN failures. We propose a new rendezvous scheme, namely X synchronization (XS) to facilitate the exchange of both data and control signals among the MSNs during search. We also devise a way to calculate appropriate timeout periods used to detect an MSN failure at rendezvous points and describe how surviving MSNs subsequently carry out the search mission. Numerical analysis and simulations have been performed to evaluate the performance of XS. The results show that XS can outperform other rendezvous schemes in terms of the total search time and the average travel distance of MSNs.
6861234
Vector quantization with model selection
We propose an iterative algorithm that incorporates model selection into entropy-constrained vector quantization. Two model selection steps are added to the classic Lloyd algorithm as additional necessary conditions for optimality. Codewords are pruned by using a Lagrangian with entropy and codebook size constraints. Relevant features are found by using a partitioned vector quantization. Relevant and irrelevant features are modelled independently. Moreover, we model irrelevant features by a global probability density function to make them independent of partition cells. This enables us to avoid a problem in comparing the performances of vector quantizers in different dimensional spaces. As a Lagrangian decreases, we not only obtain a locally optimal codebook, but also reduce codebook size and identify relevant features.
6736183
Feature selection based on maximizing separability in Gauss mixture model and its application to image classification
We propose a feature selection algorithm suitable for classification problems. Our algorithm tries to find a subset of features, which maximizes separability between Gaussian clusters. To reduce the complexity of exhaustive searching the best feature set, we follow a backward elimination method. Our feature selection algorithm can be applied to a full search classifier to obtain a single global subspace. However, one global subspace may not alone capture local behavior well. We realize multiple subspace clustering by applying our dimension reduction algorithm to a tree structured classifier. Experimental results show that the resulting classifier not only removes irrelevant features but also improves classification performance.
3134846
Tiny-sync: Tight time synchronization for wireless sensor networks
Time synchronization is a fundamental middleware service for any distributed system. Wireless sensor networks make extensive use of synchronized time in many contexts (e.g., data fusion, TDMA schedules, synchronized sleep periods, etc.). We propose a time synchronization method relevant for wireless sensor networks. The solution features minimal complexity in network bandwidth, storage as well as processing, and can achieve good accuracy. Especially relevant for sensor networks, it also provides tight, deterministic bounds on offset and clock drift. A method for synchronizing the entire network is presented. The performance of the algorithm is analyzed theoretically and validated on a realistic testbed. The results show that the proposed algorithm outperforms existing algorithms in terms of precision and resource requirements.
8040923
Inductive Policy Selection for First-Order MDPs
We select policies for large Markov Decision Processes (MDPs) with compact first-order representations. We find policies that generalize well as the number of objects in the domain grows, potentially without bound. Existing dynamic-programming approaches based on flat, propositional, or first-order representations either are impractical here or do not naturally scale as the number of objects grows without bound. We implement and evaluate an alternative approach that induces first-order policies using training data constructed by solving small problem instances using PGraphplan (Blurn & Langford, 1999). Our policies are represented as ensembles of decision lists, using a taxonomic concept language. This approach extends the work of Martin and Geffner (2000) to stochastic domains, ensemble learning, and a wider variety of problems. Empirically, we find "good" policies for several stochastic first-order MDPs that are beyond the scope of previous approaches. We also discuss the application of this work to the relational reinforcement-learning problem.