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检索条件"机构=Computer Vision and Robotics Research Laboratory"

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ELsim: a timing simulator for digital CMOS integrated circuits

ELsim: a timing simulator for digital CMOS integrated circui...

引用

IEEE Pacific Rim Conference on Communications, computers and Signal Processing

作者： M.E. Malowany A.S. Malowany Computer Vision and Robotics Laboratory McGill Research Center for Intelligent Machines Montreal QUE Canada

The basic operating principles of ELsim, a timing simulator for digital CMOS circuits, are discussed. ELsim uses the electrical-logic, or ELogic, method where time is solved for rather than voltage and an event-driven, selective trace approach is used which exploits circuit latency to improve simulation speed. Voltage levels at the circuit nodes are descretized, and the size of the voltage steps can be increased to trade accuracy for simulation speed. Results of a comparison of the ELsim simulator performance to that of SPICE for sample CMOS circuits are featured. In general, the delay times and waveforms calculated with ELsim show satisfactorily agreement with those obtained using SPICE.< >

关键词： Timing Circuit simulation CMOS integrated circuits Computational modeling Voltage SPICE computer simulation CMOS digital integrated circuits Capacitors Circuit testing

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Head and gaze dynamics in visual attention and context learning

Head and gaze dynamics in visual attention and context learn...

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IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Anup Doshi Mohan M. Trivedi Laboratory for Intelligent and Safe Automobiles Computer Vision and Robotics Research Laboratory University of California San Diego USA

Future intelligent environments and systems may need to interact with humans while simultaneously analyzing events and critical situations. Assistive living, advanced driver assistance systems, and intelligent command-and-control centers are just a few of these cases where human interactions play a critical role in situation analysis. In particular, the behavior or body language of the human subject may be a strong indicator of the context of the situation. In this paper we demonstrate how the interaction of a human observer's head pose and eye gaze behaviors can provide significant insight into the context of the event. Such semantic data derived from human behaviors can be used to help interpret and recognize an ongoing event. We present examples from driving and intelligent meeting rooms to support these conclusions, and demonstrate how to use these techniques to improve contextual learning.

关键词： Humans Intelligent systems Intelligent robots Layout Monitoring Air traffic control Vehicle dynamics Cameras Laboratories Intelligent vehicles

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A Binary Tree for Probability Learning in Eye Detection

A Binary Tree for Probability Learning in Eye Detection

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IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Junwen Wu M.M. Trivedi Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego CA USA

In this paper we proposed to solve the eye detection and localization problem under a general statistical model based object detection framework. A binary tree representation is used to discover the objects’ underlying statistical structure. Tree structures enable us to describe the object local statistical structure in a coarse-to-fine fashion. Each subtree explains the statistics for certain local substructure. The tree is built in a top-down fashion. Subsets with negligible conditional independency are found by k-means clustering using mutual information. The conditionally independent features are separated into different subtrees, while more dependent features are tended to appear close in the tree. The distribution of the object can be learned accordingly. Gaussian mixture in the independent component analysis (ICA) subspace is used to model the distribution of each high dependent feature subset, where each independent component explains the local substructure. The use of tree structure enables us to learn the distribution recursively by applying Bayesian criterion. Substantial experiments were done to evaluate the performance over the eyes detection accuracy as well as the localization ability. Experimental results show a better detection accuracy than the Viisage system with a reasonable localization ability, which validate the algorithm.

关键词： Binary trees Object detection Bayesian methods computer vision Eyes Tree data structures Probability Statistical distributions Independent component analysis Face detection

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Articulated body posture estimation from multi-camera voxel data

Articulated body posture estimation from multi-camera voxel ...

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： I. Mikic M. Trivedi E. Hunter P. Cosman Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California La Jolla CA USA

ISBN: (纸本)0769512720

We present a framework for articulated body model acquisition and tracking from voxel data. A 3D voxel reconstruction of the person's body is computed from silhouettes extracted from four cameras. The model acquisition process is fully automated. In the first frame, body parts are located sequentially. The head is located first, since its shape and size are unique and stable. Other parts are found by sequential template growing and fitting. This initial estimate of body part locations, sizes and orientations is then used as a measurement for the extended Kalman filter which ensures a valid articulated body model. The same filter, with a slightly modified state and state transition matrix, is then used for tracking. The performance of the system has been evaluated on several video sequences with promising results.

关键词： Cameras Image reconstruction Biological system modeling Data mining computer vision Robot vision systems Head Shape Filters Image segmentation

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Distributed video networks for incident detection and management

Distributed video networks for incident detection and manage...

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International Conference on Intelligent Transportation

作者： M.M. Trivedi I. Mikic G. Kogut Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA USA

We describe a novel architecture for developing distributed video networks for incident detection and management. The networks utilize both rectilinear and omnidirectional cameras. It is recognized that robust and reliable segmentation of automobiles and shadows is critical in our application. We describe new segmentation procedure and present experimental results to support the basic feasibility and utility of the algorithms.

关键词： Delay estimation Layout Robustness Image segmentation Telecommunication traffic Sensor fusion computerized monitoring Remote monitoring Sensor arrays computer network management

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Visual Modules for Head Gesture Analysis in Intelligent Vehicle Systems

Visual Modules for Head Gesture Analysis in Intelligent Vehi...

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IEEE Symposium on Intelligent Vehicle

作者： Junwen Wu M.M. Trivedi Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego CA USA

In this paper a coarse-to-fine system framework for analyzing the head gesture is presented. We discuss several important modules from computer vision aspects, including the pose-invariant face detection, face tracking, pose determination and high-resolution image reconstruction for eye pupils detection. Visual cues using intensity images obtained from in-car cameras are explored. A pose-invariant face detection algorithm is used to get the initial face area; afterwards face tracking and validation step is proposed to segment the face region for pose determination. The algorithm is tested on the drivers images under natural driving conditions. Experimental results show that the algorithm is robust to the head pose changes as well as the illumination changes. In this system framework, we propose that when coarse analysis utilizing the head pose alone is not sufficient for driver's behavior analysis, a finer analysis based on the eye gaze tracking is used, which requires images with sufficient resolution. A novel super-resolution reconstruction algorithm is proposed to help reveal more facial details, so as to facilitate the pupil detection. Experiment on the synthesis data shows the effectiveness of the super-resolution reconstruction algorithm

关键词： Intelligent vehicles Face detection Head Image analysis Reconstruction algorithms computer vision Image reconstruction Cameras Image segmentation Testing

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A novel interactivity environment for integrated intelligent transportation and telematic systems

A novel interactivity environment for integrated intelligent...

引用

International Conference on Intelligent Transportation

作者： T.B. Hall M.A. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA USA

Advances in intelligent transportation are rapidly sensing and connecting both highways and vehicles. This recent availability of high-speed communication and large quantities of information are rapidly changing the way we perceive transportation. In order to develop technologies and applications that are practical in this intelligent environment, it is important to consider specific ways to best use this infrastructure effectively. This paper focuses on the development of and experiments with a visualization environment that presents an interactive, immersive, and context-preserving display of outdoor information. The developed visualization integrates a variety of types of information about a section of interstate such as aerial photographs, maps, and live outdoor videos into a single interactive environment. Additionally, a wireless mobile testbed is presented that allows the undertaking of novel interactivity experiments among mobile participants without requiring a fully instrumented vehicle. These systems allow effective experimentation with and integration between highway and vehicle based real-world intelligent transportation systems.

关键词： Intelligent transportation systems Telematics Intelligent vehicles Road transportation Road vehicles Visualization Joining processes Displays Videos Testing

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A Regression Model in TensorPCA Subspace for Face Image Super-resolution Reconstruction

A Regression Model in TensorPCA Subspace for Face Image Supe...

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International Conference on Pattern Recognition

作者： Junwen Wu M.M. Trivedi Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego CA USA

ISBN: (纸本)0769525210

A regression model in the tensorPCA subspace is proposed in this paper for face super-resolution reconstruction. An approximate conditional probability model is used for the tensor subspace coefficients and maximum-likelihood estimator gives a linear regression model. The approximation is corrected by adding non-linear component from a RBF-type regressor. Experiments on face images from FERET database validate the algorithm. Although each projection coefficient is estimated by a local estimator, tensorPCA subspace analysis is still a global descriptor, which makes the algorithm have certain ability to deal with partially occluded images

关键词： Image resolution Image reconstruction Tensile stress Spatial resolution Principal component analysis Face detection Image analysis computer vision Robot vision systems Laboratories

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Learning Residual Flow as Dynamic Motion from Stereo Videos

Learning Residual Flow as Dynamic Motion from Stereo Videos

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Seokju Lee Sunghoon Im Stephen Lin In So Kweon Robotics and Computer Vision Laboratory KAIST Daejeon Republic of Korea Microsoft Research Asia Beijing China

We present a method for decomposing the 3D scene flow observed from a moving stereo rig into stationary scene elements and dynamic object motion. Our unsupervised learning framework jointly reasons about the camera motion, optical flow, and 3D motion of moving objects. Three cooperating networks predict stereo matching, camera motion, and residual flow, which represents the flow component due to object motion and not from camera motion. Based on rigid projective geometry, the estimated stereo depth is used to guide the camera motion estimation, and the depth and camera motion are used to guide the residual flow estimation. We also explicitly estimate the 3D scene flow of dynamic objects based on the residual flow and scene depth. Experiments on the KITTI dataset demonstrate the effectiveness of our approach and show that our method outperforms other state-of-the-art algorithms on the optical flow and visual odometry tasks.

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Performance characterization for Gaussian mixture model based motion detection algorithms

Performance characterization for Gaussian mixture model base...

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IEEE International Conference on Image Processing

作者： Junwen Wu M. Trivedi Computer Vision and Robotics Research Laboratory ECE Department University of California San Diego CA USA

Pixelwise Gaussian mixture based background modeling algorithm proposed in S. Stauffer and W.E.L. Grimson (1999 and 2000) has been proved to be robust for many motion detection applications. However, the algorithm is not sensitive to fast motion. One possible solution is to introduce local correlations. Starting from this, this paper proposes to use the Gaussian mixture globally for modeling the distribution of the difference image between the new frame and the estimated background. Experimental evaluation validates the algorithm. Motivated by the demands of selecting the more appropriate algorithm for a specific application, qualitative and quantitative performance comparisons of these two approaches are presented. We proposes three metrics. One is to characterize the pixel level accuracy and the other two are to evaluate the errors in the object level. Pros and cons of both algorithms are summarized.

关键词： Motion detection computer vision Noise robustness Guidelines Statistical distributions Robot sensing systems Robot vision systems Laboratories Application software computer errors

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