5669766
Automatic Estimation of 3D Transformations using Skeletons for Object Alignment
An algorithm for automatic estimation of 3D transformations between two objects is presented in this paper. Skeletons of the 3D objects are created using a fully parallel thinning technique, feature point pairs (land markers) are automatically extracted from skeletons, and a least squares method is applied to solve an over determined linear system to estimate the 3D transformation matrix. Experiments show that this method is quite accurate when the translations and rotation angles are small, even when there is some noise in the data. The estimation process requires about 2 seconds on an Intel Centrino Laptop with 512 MB memory, for a complex model with about 37,000 object points and 500 object points for its skeletons
15147164
Balanced incomplete designs for 3D perceptual quality estimation
Many factors, such as the number of vertices and the resolution of texture, can affect the display quality of 3D objects. When the resources of a graphics system are not sufficient to render the ideal image, degradation is inevitable. It is therefore important to study how individual factors affect the overall quality, and how the degradation can be controlled given limited resources. The essential factors determining the display quality are reviewed and a 3D perceptual estimation method is described. One of the major concerns in designing perceptual experiments is the large number of evaluations to be performed by judges, which results in fatigue and errors in judgement. To reduce judging fatigue and increase reliability of evaluations we propose using a statistical approach, following the design of experiments technique of balanced incomplete block design (BIBD). We develop models following BIBD for perceptual experiments and validate our model with experimental results. Even though the BIBD framework for perceptual evaluations is described in the context of 3D quality estimation, the approach can be used in other perceptual evaluation scenarios as well.
6293093
Iterative Estimation of 3D Transformations for Object Alignment
An Iterative Estimation Algorithm (IEA) of 3D transformations between two objects is presented in this paper. Skeletons of the 3D objects are extracted using a fully parallel thinning technique, feature point pairs (land markers) are extracted from skeletons automatically with a heuristic rule, and a least squares method and an iterative approach are applied to estimate the 3D transformation matrix. The algorithm has three advantages. First of all, no initial transformation matrix is needed. Secondly, user interaction is not required for identifying the land markers. Thirdly, the time complexity of this algorithm is polynomial. Experiments show that this method works quite well with high accuracy when the translations and rotation angles are small, even when noise exists in the data.
39372956
An Inequality on Connected Domination Parameters
42266951
TMO-structured cluster-based real-time management of location data on massive volume of moving items
A major challenge in the field of location based service (LBS) system engineering, is to establish a highly scalable system architecture which can be instantiated in moderate-size configurations handling thousands of moving items as well as in upper-end configurations handling millions of moving items. We are exploring an approach of using a cluster of database server nodes and effecting efficient distributed and parallel computing in both real-time update of location records of moving items and processing of location-related queries. The approach of developing efficient middleware which is layered on a cluster of nodes running commercial off-the-shelf database servers, has been adopted. The middleware design is substantially based on the TMO scheme for real-time distributed object programming and real-time distributed computing system design, which enables low-overhead coordination of distributed computations and highly abstract distributed programming styles.
206985371
Framework and network based multimedia object management environment
Because multimedia objects are becoming more prevalent, on ever increasing volume, inventing an efficient multimedia object management environment is a matter of increasing urgency. In recognition of this crisis, we developed the multimedia object management environment (MOME) that includes a suite of tools such as the Vortex framework, and network file indexer (NFI). MOME also includes a fully featured graphical user interface for maximum user control and flexibility. With meta data, it automatically generates indexes and paths for different types of multimedia objects and allows users to quickly find what they are looking for. In this paper, we address the background, architecture, and performance of MOME in detail.
16865574
On the superiority of DO-RE-ME/MPG-D over stuck-at-based defective part level prediction
Uses data collected from benchmark circuit simulations to examine the relationship between the tests which detect stuck-at faults and those which detect bridging surrogates. We show that the coefficient of correlation between these tests approaches zero as the stuck-at fault coverage approaches 100%. An enhanced version of the MPG-D model, which is based upon the number of detections of each site in a logic circuit, is shown to be superior to stuck-at fault coverage-based defective part level prediction. We then compare the accuracy of both predictors for an industrial circuit tested using two different test pattern sequences.
10441616
Automatic Gait Optimization with Gaussian Process Regression
Gait optimization is a basic yet challenging problem for both quadrupedal and bipedal robots. Although techniques for automating the process exist, most involve local function optimization procedures that suffer from three key drawbacks. Local optimization techniques are naturally plagued by local optima, make no use of the expensive gait evaluations once a local step is taken, and do not explicitly model noise in gait evaluation. These drawbacks increase the need for a large number of gait evaluations, making optimization slow, data inefficient, and manually intensive. We present a Bayesian approach based on Gaussian process regression that addresses all three drawbacks. It uses a global search strategy based on a posterior model inferred from all of the individual noisy evaluations. We demonstrate the technique on a quadruped robot, using it to optimize two different criteria: speed and smoothness. We show in both cases our technique requires dramatically fewer gait evaluations than state-of-the-art local gradient approaches.