17166398
Image classification using GMM with context information and with a solution of singular covariance problem
Summary form only given. Taking the average of feature vectors from the center and neighboring blocks to a block being coded is proposed as a method of considering context information in block classification. The algorithm has the advantage of low complexity. Gauss mixture models (GMM) are adopted to extract features from image blocks, including an algorithm to handle singular covariance matrices. Two different distortion measures are used; namely log-likelihood quadratic discrimination analysis (QDA) and a dimension-compensated distortion measure defined by dividing the QDA distortion by the corresponding cell's dimension. Aerial images were used to train and test. Experimental results show that the proposed algorithm not only improves the classification performance, but also provides a solution to the singular covariance problem.
14200816
Context-dependent tree-structured image classification using the QDA distortion measure and the hidden Markov model
Vector quantization based on the Gauss mixture model (GMM) and the quadratic discriminant analysis (QDA) distortion measure has been shown to perform well in statistical image classification problems. Previous work in this area has concentrated on designing a separate GMM-based vector quantizer using the QDA distortion measure for each class using full search. We design a single vector quantizer for all classes using a tree-structured algorithm based on the (generalized) BFOS algorithm. This reduces the search complexity, while it increases the correct classification rate. Further, the pruning stage of our algorithm takes into account the dependencies between the image blocks assuming a hidden Markov model (HMM). During the test stage, our algorithm aims to iteratively maximize the joint probability of occurrence of all image blocks based on the HMM. Our simulation results indicate that our algorithm performs better (both in terms of computational complexity and classification rate) when compared to the previously published algorithms based on the GMM.
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.
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.
477360
Approximate Policy Iteration with a Policy Language Bias
We explore approximate policy iteration, replacing the usual cost-function learning step with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve. In particular, we induce high-quality domain-specific planners for classical planning domains (both deterministic and stochastic variants) by solving such domains as extremely large MDPs.
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.