9323568
Robust relative pose estimation with integrated cheirality constraint
The cheirality constraint, which requires that reconstructed point correspondences lie in front of the cameras, has not typically been integrated into traditional RANSAC-based pose estimators. We have developed a new RANSAC-based relative pose estimator which incorporates the cheirality constraint not only to preempt invalid epipolar geometry hypotheses, but also as a criterion in identifying inliers for image feature correspondences. Because the application of the cheirality constraint is tightly related to the estimation of epipolar geometry, integrating them inside RANSAC can prevent inliers being falsely identified as cheirality outliers. The result is a more consistent and stable estimation which leaves denser feature correspondences for subsequent processing. Experimental comparison between the usual RANSAC-based approach and the proposed approach is performed.
7928906
Augmenting Exercise Systems with Virtual Exercise Environment
Adhering to an exercise program is a challenge for everybody who wants to build a healthier body and lifestyle through physical exercise. We have developed an Virtual Exercise Environment (VEE) that augments stationary exercise equipment with virtual reality techniques to make exercising more enjoyable. Our VEE system consists of a recording system to capture video, distance and incline data about real trails, and a playback system which "displays" both video and terrain data in the form of video speed and resistance. Trails are played back according to the speed the user generates on the stationary exercise equipment. The system uses commodity capture and display devices and supports standard interfaces for existing exercise equipment. User studies have shown that users enjoy the ability to guage their progress and performance via their progress through trail playback in the VEE.
1651955
Feature Matching under Region-Based Constraints for Robust Epipolar Geometry Estimation
Outlier-free inter-frame feature matches are important to accurate epipolar geometry estimation for many vision and robotics applications. We discover a set of high-level geometric and appearance constraints on low-level feature matches by exploiting reliable region matching results. A new outlier filtering scheme based on these constraints is proposed that can be combined with traditional robust statistical methods to identify outlier feature matches more reliably and efficiently. The proposed filtering scheme is tested in an real application of outdoor mobile robot navigation based on far-field scenes and of scenes that contain repeated structures.
644571
Parametric inference for progressive Type-I hybrid censored data on a simple step-stress accelerated life test model
This paper considers a simple step-stress accelerated life test model under progressive Type-I hybrid censoring scheme. The progressive Type-I hybrid censoring scheme and statistical method in synthetic accelerated stresses are provided so as to decrease the lifetime and reduce the test cost. An exponentially distributed life of test units and a cumulative exposure model are assumed. The maximum likelihood estimates of the model parameters are obtained using a pivotal quantity. Two useful lemmas and a theorem are given to construct the approximate confidence intervals for the model parameters. Finally, simulation results are provided to assess the method of inference developed in this article. The simulation results show that the method does improve for large sample size.
24579136
The Periodic Solution of a Class of Two Neurons Hopfield Network with Distributed Delay
This paper considers the two neurons Hopfield network with distributed delay, which can be transformed into the three-dimensional differential system. Then we use the result of competitive system to get the periodic solution of the system. We also show that existence and uniqueness of equilibrium point, and if the equilibrium is unstable, then an orbitally asymptotically stable periodic solution exists.
18672998
Latent Pose Estimator for Continuous Action Recognition
Recently, models based on conditional random fields (CRF) have produced promising results on labeling sequential data in several scientific fields. However, in the vision task of continuous action recognition, the observations of visual features have dimensions as high as hundreds or even thousands. This might pose severe difficulties on parameter estimation and even degrade the performance. To bridge the gap between the high dimensional observations and the random fields, we propose a novel model that replace the observation layer of a traditional random fields model with a latent pose estimator. In training stage, the human pose is not observed in the action data, and the latent pose estimator is learned under the supervision of the labeled action data, instead of image-to-pose data. The advantage of this model is twofold. First, it learns to convert the high dimensional observations into more compact and informative representations. Second, it enables transfer learning to fully utilize the existing knowledge and data on image-to-pose relationship. The parameters of the latent pose estimator and the random fields are jointly optimized through a gradient ascent algorithm. Our approach is tested on HumanEva [1] --- a publicly available dataset. The experiments show that our approach can improve recognition accuracy over standard CRF model and its variations. The performance can be further significantly improved by using additional image-to-pose data for training. Our experiments also show that the model trained on HumanEva can generalize to different environment and human subjects.
1496310
Discriminative learning of visual words for 3D human pose estimation
This paper addresses the problem of recovering 3D human pose from a single monocular image, using a discriminative bag-of-words approach. In previous work, the visual words are learned by unsupervised clustering algorithms. They capture the most common patterns and are good features for coarse-grain recognition tasks like object classification. But for those tasks which deal with subtle differences such as pose estimation, such representation may lack the needed discriminative power. In this paper, we propose to jointly learn the visual words and the pose regressors in a supervised manner. More specifically, we learn an individual distance metric for each visual word to optimize the pose estimation performance. The learned metrics rescale the visual words to suppress unimportant dimensions such as those corresponding to background. Another contribution is that we design an appearance and position context (APC) local descriptor that achieves both selectivity and invariance while requiring no background subtraction. We test our approach on both a quasi-synthetic dataset and a real dataset (HumanEva) to verify its effectiveness. Our approach also achieves fast computational speed thanks to the integral histograms used in APC descriptor extraction and fast inference of pose regressors.
2507502
An Analytical Performance Model of IP-SAN
In this paper we propose a component-based analytical performance model for IP-SAN. We model four key components within IP-SAN individually and they are IP network, cache, RAID and disks. We validate these component models and compose them to give an entire IP-SAN performance model. The entire IP-SAN model analyzes the performance of an IP-SAN configuration under multiple synchronous workloads by using closed queueing network model and under asynchronous workload by using open queueing network model. We also validate the entire IP-SAN performance model against a real IP-SAN system. The theoretical results are compared with the results from a real IP-SAN using a variety of synthetic workloads. These experiments prove that the component-based method is an appropriate way to build performance model for a complex storage system. To our knowledge, this is the first exploration into building analytical performance model for entire IP-SAN system and validating against a real system. We also identify areas for future research in the performance analysis of real IP-SAN.