检索结果-内蒙古大学图书馆

A Novel LOS Rate Estimation Method Based on Images for Strap-down Inertial Guidance 3

学校读者我要写书评

暂无评论

A Novel LOS Rate Estimation Method Based on Images for Strap...

2020 3rd International Conference on Advanced Algorithms and Control Engineering, ICAACE 2020

作者： Xu, Zheng Luo, Haibo Hui, Bin Chang, Zheng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Science Shenyang110016 China Key Lab of Image Understanding and Computer Vision Shenyang110016 China

With the development of technology, precision guided weapon is becoming more and more important in modern war. In order to launch our recent guidance system on medium and small guided weapons, we propose a method to obtain the LOS rate by combining information from both camera and gyroscope. To be specific, we firstly calculate the body LOS angle through transforming the image pixel coordinate system into the image physical coordinate system according to camera internal parameters;then subtract the missile motion information contained in the measurement signal of the seeker and finally the LOS rate is deduced. Comparing with traditional gimballed seekers, our strap-down seekers with camera have significantly reduced costs and influences caused by external environments on platform. © Published under licence by IOP Publishing Ltd.

关键词： Cameras

A learning-based method for online adjustment of C-arm cone-beam CT source trajectories for artifact avoidance

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Thies, Mareike Zäch, Jan-Nico Gao, Cong Taylor, Russell Navab, Nassir Maier, Andreas Unberath, Mathias Laboratory for Computational Sensing + Robotics Johns Hopkins University BaltimoreMD United States Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Computer Vision Laboratory Eidgenössische Technische Hochschule Zürich

Purpose: During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm Cone-beam CT (CBCT) provides intraoperative 3D tomographic imaging which would allow for immediate verification and, if needed, revision. However, the reconstruction quality attainable with commercial CBCT devices is insufficient, predominantly due to severe metal artifacts in the presence of pedicle screws. These artifacts arise from a mismatch between the true physics of image formation and an idealized model thereof assumed during reconstruction. Prospectively acquiring views onto anatomy that are least affected by this mismatch can, therefore, improve reconstruction quality. Methods: We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task, i.e. verification of screw placement. Adjustments are performed on-the-fly using a convolutional neural network that regresses a quality index over all possible next views given the current x-ray image. Adjusting the CBCT trajectory to acquire the recommended views results in non-circular source orbits that avoid poor images, and thus, data inconsistencies. Results: We demonstrate that convolutional neural networks trained on realistically simulated data are capable of predicting quality metrics that enable scene-specific adjustments of the CBCT source trajectory. Using both realistically simulated data as well as real CBCT acquisitions of a semianthropomorphic phantom, we show that tomographic reconstructions of the resulting scene-specific CBCT acquisitions exhibit improved image quality particularly in terms of metal artifacts. Conclusion: The proposed method is a step towards online patient-specific C-arm CBCT source trajectories that enable high-quality tomographic imaging in the operating room. Since the optimization objectiv

关键词： Convolution

Camera Exposure Control for Robust Robot vision with Noise-Aware Image Quality Assessment

学校读者我要写书评

暂无评论

Camera Exposure Control for Robust Robot Vision with Noise-A...

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

作者： Ukcheol Shin Jinsun Park Gyumin Shim Francois Rameau In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea

In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture best-exposed images, which can boost the performance of various computer vision and robotics tasks. For this purpose, we carefully design an image quality metric that captures complementary quality attributes and ensures light-weight computation. Specifically, our metric consists of a combination of image gradient, entropy, and noise metrics. The synergy of these measures allows the preservation of sharp edges and rich texture in the image while maintaining a low noise level. Using this novel metric, we propose a real-time and fully automatic exposure and gain control technique based on the Nelder-Mead method. To illustrate the effectiveness of our technique, a large set of experimental results demonstrates the higher qualitative and quantitative performance compared with conventional approaches.

关键词：

Are You With Me? Determining the Association of Individuals and the Collective Social Space

学校读者我要写书评

暂无评论

Are You With Me? Determining the Association of Individuals ...

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

作者： Alan D. G. Silva Douglas G. Macharet Computer Vision and Robotics Laboratory (VeRLab) Universidade Federal de Minas Gerais MG Brazil

The increasing use of autonomous mobile robots in different parts of society, and not restricted only to industrial environments, makes it important to propose techniques that will allow them to behave in the most socially acceptable way as possible. In most real-world scenarios, individuals in the environment are interacting with each other and are arranged into groups. Therefore, it is paramount the proposition of techniques to efficiently and correctly identify and represent such groups. This information can be useful in different tasks such as approaching and initiating an interaction, escorting, and the navigation itself. In this work, we propose a novel graph-based approach to evaluate the possible association of individuals in the environment based on their position and body orientation. Next, based on this association, we propose a representation of the combined social space of individuals in the same group. The methodology was evaluated using synthetic and real-world datasets, showing that it achieves results comparable to or better than the state-of-the-art.

关键词：

Camera exposure control for robust robot vision with noise-aware image quality assessment

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Shin, Ukcheol Park, Jinsun Shim, Gyumin Rameau, Francois Kweon, In So Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon34141 Korea Republic of

In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture the best-exposed image which can boost the performance of various computer vision and robotics tasks. For this purpose, we carefully design an image quality metric which captures complementary quality attributes and ensures light-weight computation. Specifically, our metric consists of a combination of image gradient, entropy, and noise metrics. The synergy of these measures allows preserving sharp edge and rich texture in the image while maintaining a low noise level. Using this novel metric, we propose a real-time and fully automatic exposure and gain control technique based on the Nelder-Mead method. To illustrate the effectiveness of our technique, a large set of experimental results demonstrates higher qualitative and quantitative performances when compared with conventional approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： computer vision

Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

学校读者我要写书评

暂无评论

SSRN

SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

Risk-averse MPC via visual-inertial input and recurrent networks for online collision avoidance

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Schperberg, Alexander Chen, Kenny Tsuei, Stephanie Jewett, Michael Hooks, Joshua Soatto, Stefano Mehta, Ankur Hong, Dennis The Robotics and Mechanisms Laboratory Department of Mechanical and Aerospace Engineering United States The Laboratory for Embedded Machines and Ubiquitous Robots Department of Electrical and Computer Engineering United States The UCLA Vision Lab Department of Computer Science United States The University of California Los Angeles Los AngelesCA90095 United States

In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates through each step of our MPC's finite time horizon. The RNN model is trained on a dataset that comprises of robot and landmark poses generated from camera images and inertial measurement unit (IMU) readings via a state-of-the-art visual-inertial odometry framework. To detect and extract object locations for avoidance, we use a custom-trained convolutional neural network model in conjunction with a feature extractor to retrieve 3D centroid and radii boundaries of nearby obstacles. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms, demonstrating autonomous behaviors that can plan fast and collision-free paths towards a goal point. Copyright © 2020, The Authors. All rights reserved.

关键词： Model predictive control

Underwater image super-resolution using deep residual multipliers

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Islam, Md Jahidul Enan, Sadman Sakib Luo, Peigen Sattar, Junaed Interactive Robotics and Vision Laboratory Department of Computer Science and Engineering Minnesota Robotics Institute University of Minnesota Twin Cities United States

We present a deep residual network-based generative model for single image super-resolution (SISR) of underwater imagery for use by autonomous underwater robots. We also provide an adversarial training pipeline for learning SISR from paired data. In order to supervise the training, we formulate an objective function that evaluates the perceptual quality of an image based on its global content, color, and local style information. Additionally, we present USR-248, a large-scale dataset of three sets of underwater images of ‘high’ (640 × 480) and ‘low’ (80 × 60, 160 × 120, and 320 × 240) resolution. USR-248 contains over 7K paired instances in each set of data for supervised training of 2×, 4×, or 8× SISR models. Furthermore, we validate the effectiveness of our proposed model through qualitative and quantitative experiments and compare the results with several state-of-the-art models’ performances. We also analyze its practical feasibility for applications such as scene understanding and attention modeling in noisy visual conditions. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

Learning Residual Flow as Dynamic Motion from Stereo Videos

学校读者我要写书评

暂无评论

Learning Residual Flow as Dynamic Motion from Stereo Videos

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.

关键词：