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arXiv 2022年

作者： Luo, Cheng Song, Siyang Xie, Weicheng Shen, Linlin Gunes, Hatice Computer Vision Institute Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing China Department of Computer Science and Technology University of Cambridge United Kingdom

The activations of Facial Action Units (AUs) mutually influence one another. While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display. This paper proposes an AU relationship modelling approach that deep learns a unique graph to explicitly describe the relationship between each pair of AUs of the target facial display. Our approach first encodes each AU’s activation status and its association with other AUs into a node feature. Then, it learns a pair of multi-dimensional edge features to describe multiple task-specific relationship cues between each pair of AUs. During both node and edge feature learning, our approach also considers the influence of the unique facial display on AUs’ relationship by taking the full face representation as an input. Experimental results on BP4D and DISFA datasets show that both node and edge feature learning modules provide large performance improvements for CNN and transformer-based backbones, with our best systems achieving the state-of-the-art AU recognition results. Our approach not only has a strong capability in modelling relationship cues for AU recognition but also can be easily incorporated into various backbones. Our PyTorch code is made available at https://***/CVI-SZU/ME-GraphAU. Copyright © 2022, The Authors. All rights reserved.

关键词： Chemical activation

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Adaptive real-time video-tracking for arbitrary objects

Adaptive real-time video-tracking for arbitrary objects

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2010 IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Dominik A. Klein Dirk Schulz Simone Frintrop Armin B. Cremers Intelligent Vision Systems Group Department of Computer Science III Rheinische Friedrich-Wilhelms-Universität Bonn Germany US Armed Systems Group Fraunhofer FKIE Wachtberg Germany

In this paper, we present a visual object tracker for mobile systems that is able to specialize to individual objects during tracking. The core of our method is a novel observation model and the way it is automatically adapted to a changing object and background appearance over time. The model is integrated into the well known Condensation algorithm (SIR filter) for statistical inference, and it consists of a boosted ensemble of simple threshold classifiers built upon center-surround Haar-like features, which the filter continuously updates based on the images perceived. We present optimizations and reasonable approximations to limit the computational costs. Thus, the final algorithms are capable of processing video input at real-time. To experimentally investigate the gain of adapting the observation model we compare two different approaches with a non-adapting version of our observation model: maintaining a single observation model for all particles, and maintaining individual observation models for each particle. In addition, experiments were conducted to compare system performances between the proposed algorithms and two other state of the art Condensation based tracking approaches.

关键词： Adaptation model Computational modeling Histograms Training Target tracking Real time systems Image color analysis

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Latent Space Policy Search for robotics

Latent Space Policy Search for Robotics

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IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Kevin Sebastian Luck Gerhard Neumann Erik Berger Jan Peters Heni Ben Amor Department of Computer Science Technische Universitat Darmstadt 64289 Darmstadt Germany Department of Mathematics and Computer Science Technische Universitat Bergakademie Freiberg 09599 Freiberg Germany Institute for Robotics and Intelligent Machines Georgia Institute of Technology GA 30332 USA

ISBN: (纸本)9781479969357

Learning motor skills for robots is a hard task. In particular, a high number of degrees-of-freedom in the robot can pose serious challenges to existing reinforcement learning methods, since it leads to a high-dimensional search space. However, complex robots are often intrinsically redundant systems and, therefore, can be controlled using a latent manifold of much smaller dimensionality. In this paper, we present a novel policy search method that performs efficient reinforcement learning by uncovering the low-dimensional latent space of actuator redundancies. In contrast to previous attempts at combining reinforcement learning and dimensionality reduction, our approach does not perform dimensionality reduction as a preprocessing step but naturally combines it with policy search. Our evaluations show that the new approach outperforms existing algorithms for learning motor skills with high-dimensional robots.

关键词： Robots Aerospace electronics Mathematical model Learning (artificial intelligence) Vectors Equations Joints

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Social robotics 1

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丛书名： Lecture Notes in computer science

1000年

作者： Bilge Mutlu Christoph Bartneck Jaap Ham Vanessa Evers Takayuki Kanda

ISBN: (数字)9783642255045

ISBN: (纸本)9783642255038

This book constitutes the refereed proceedings of the Third International Conference on Social robotics, ICSR 2011, held in Amsterdam, The Netherlands, in November 2011. The 23 revised full papers were carefully selected during two rounds of reviewing and improvement from 51 submissions. The papers are organized in topical sections on social interaction with robots; nonverbal interaction with social robots; robots in society; social robots in education; affective interaction with social robots; robots in the home.

关键词： Artificial Intelligence User Interfaces and Human computer Interaction computers and Society computer Appl. in Social and Behavioral sciences computer Imaging, vision, Pattern Recognition and Graphics Special Purpose and Application-Based Systems

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Mining discriminative adjectives and prepositions for natural scene recognition

Mining discriminative adjectives and prepositions for natura...

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

作者： Bangpeng Yao Juan Carlos Niebles Li Fei-Fei Department of Computer Science Princeton University NJ USA Robotics and Intelligent Systems Group Universidad del Norte Colombia Department of Electrical Engineering Princeton University NJ USA

This paper presents a method that considers not only patch appearances, but also patch relationships in the form of adjectives and prepositions for natural scene recognition. Most of the existing scene categorization approaches only use patch appearances or co-occurrence of patch appearances to determine the scene categories, but the relationships among patches remain ignored. Those relationships are, however, critical for recognition and understanding. For example, a `beach' scene can be characterized by a `sky' region above `sand', and a `water' region between `sky' and `sand'. We believe that exploiting such relations between image regions can improve scene recognition. In our approach, each image is represented as a spatial pyramid, from which we obtain a collection of patch appearances with spatial layout information. We apply a feature mining approach to get discriminative patch combinations. The mined patch combinations can be interpreted as adjectives or prepositions, which are used for scene understanding and recognition. Experimental results on a fifteen class scene dataset show that our approach achieves competitive state-of-the-art recognition accuracy, while providing a rich description of the scene classes in terms of the mined adjectives and prepositions.

关键词： Layout Road transportation Image representation intelligent robots Image recognition Data mining computer science intelligent systems Histograms

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Machining feature-based comparisons of mechanical parts

Machining feature-based comparisons of mechanical parts

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International Conference on Shape Modeling and Applications

作者： V. Cicirello W.C. Regli The Robotics Institute Carnegie Mellon University Pittsburgh PA USA Geometric and Intelligent Computing Laboratory Department of Mathematics and Computer Science Drexel University Philadelphia PA USA

ISBN: (纸本)0769508537

Solid models are the critical data elements in modern computer-aided design (CAD) environments, describing the shape and form of manufactured artifacts. Their growing ubiquity has created new problems in how to effectively manage the many models that are now stored in the digital libraries of large design and manufacturing enterprises. Existing techniques from the engineering literature and from industrial practice, such as group technology, rely on human-supervised encoding and classification, while techniques from the multimedia database and computer graphics/vision communities often ignore the manufacturing attributes most significant in the classification of models. This paper presents our approach to the manufacturing similarly assessment of solid models of mechanical parts based on machining features. Our technical approach is three-fold: (1) perform machining feature extraction to map the solid model to a set of STEP AP 224 machining features; (2) construct a model dependency graph from the set of machining features; and (3) find the nearest neighbors to the query graph using an iterative improvement search across a database of other models. We also present empirical experiments to validate our approach using our testbed, the National Design Repository ( ). The contribution of this research is the first fully automated technique for machining feature-based comparisons of mechanical artifacts. We believe that this work can lead to radical changes in the way in which design data is managed in modern engineering enterprises.

关键词： Machining Solid modeling Virtual manufacturing Design automation computer aided manufacturing Data engineering Shape Software libraries computer industry Manufacturing industries

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Calibrated and partially calibrated semi-generalized homographies

arXiv

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arXiv 2021年

作者： Bhayani, Snehal Sattler, Torsten Barath, Daniel Beliansky, Patrik Heikkilä, Janne Kukelova, Zuzana Center for Machine Vision and Signal Analysis University of Oulu Finland Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Computer Vision and Geometry Group Department of Computer Science ETH Zürich Faculty of Mathematics and Physics Charles University Prague Czech Republic Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague

In this paper, we propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera. The proposed solvers use five 2D-2D image point correspondences induced by a scene plane. One group of solvers assumes the perspective camera to be fully calibrated, while the other estimates the unknown focal length together with the absolute pose parameters. This setup is particularly important in structurefrom-motion and visual localization pipelines, where a new camera is localized in each step with respect to a set of known cameras and 2D-3D correspondences might not be available. Thanks to a clever parametrization and the elimination ideal method, our solvers only need to solve a univariate polynomial of degree five or three, respectively a system of polynomial equations in two variables. All proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Cameras

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A visual servoing approach for tracking features in urban areas using an autonomous helicopter

A visual servoing approach for tracking features in urban ar...

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IEEE International Conference on robotics and Automation (ICRA)

作者： L. Mejias P. Campoy S. Saripalli G.S. Sukhatme Computer Vision Group Universidad Politécnica de Madrid Madrid Spain Robotic Embedded Systems Laboratory Center for Robotics and Embedded Systems Department of Computer Science University of Southern California Los Angeles CA USA

The use of unmanned aerial vehicles (UAVs) in civilian and domestic applications is highly demanding, requiring a high-level of capability from the vehicles. This work addresses the design and implementation of a vision-based feature tracker for an autonomous helicopter. Using vision in the control loop allows estimating the position and velocity of a set of features with respect to the helicopter. The helicopter is then autonomously guided to track these features (in this case windows in an urban environment) in real time. The results obtained from flight trials in a real world scenario demonstrate that the algorithm for tracking features in an urban environment, used for visual servoing of an autonomous helicopter is reliable and robust

关键词： Visual servoing Urban areas Helicopters Aircraft navigation Machine vision Control systems Unmanned aerial vehicles Global Positioning System computer vision Robot vision systems

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Omni-directional vision for robot navigation

Omni-directional vision for robot navigation

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IEEE Workshop on Omnidirectional vision

作者： N. Winters J. Gaspar G. Lacey J. Santos-Victor Computer Vision and Robotics Group. Department of Computer Science University of Dublin Dublin Ireland Instituto Superior Técnico Lisboa Portugal Instituto de Sistemas e Robotica Instituto Superior Tecnico Lisboa Portugal

We describe a method for visual based robot navigation with a single omni-directional (catadioptic) camera. We show how omni-directional images can be used to generate the representations needed for two main navigation modalities: Topological Navigation and Visual Path Following. Topological Navigation relies on the robot's qualitative global position, estimated from a set of omni-directional images obtained during a training stage (compressed using PCA). To deal with illumination changes, an eigenspace approximation to the Hausdorff measure is exploited. We present a method to transform omni-directional images to Bird's Eye Views that correspond to scaled orthographic views of the ground plane. These images are used to locally control the orientation of the robot, through visual servoing. Visual Path Following is used to accurately control the robot along a prescribed trajectory, by using bird's eye views to track landmarks on the ground plane. Due to the simplified geometry of these images, the robot's pose can be estimated easily and used for accurate trajectory following. Omni-directional images facilitate landmark based navigation, since landmarks remain visible in all images, as opposed to a small field-of-view standard camera. Also, omni-directional images provide the means of having adequate representations to support both accurate or qualitative navigation. Results are described in the paper.

关键词： Robot vision systems Navigation Cameras Image coding Principal component analysis Lighting Visual servoing Robot control Trajectory Computational geometry

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Bitcoin Price Forecasting: A Comparative Study Between Statistical and Machine Learning Methods

Bitcoin Price Forecasting: A Comparative Study Between Stati...

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International Conference on computer & Information science (ICCIS)

作者： Waddah Waheeb Habib Shah Mohammed Jabreel Domenec Puig School of Computing Asia Pacific University of Technology & Innovation Kuala Lumpur Bukit Jalil Malaysia College of Computer Science King Khalid University Abha Saudi Arabia IRCV - Intelligent Robotics and Computer Vision Group Universitat Rovira i Virgili Tarragona Spain

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154688

This paper presents a comparative study between statistical and machine learning methods in forecasting Bitcoin's closing prices. Thirteen forecasting methods namely average, naive, drift, auto-regressive integrated moving-average, simple exponential smoothing (SES), Holt, and damped exponential smoothing, the average of SES, Holt and damped methods, exponential smoothing (ETS), bagged ETS, Theta, multilayer perceptron, and extreme learning machines (ELM) were used to forecast the closing prices for the next 14 days. The findings of this study are three folds. First, there are seven forecasting methods outperformed the naive method namely MLP, ELM, damped exponential smoothing, simple exponential smoothing, Theta, ETS, and ARIMA. Second, MLP and ELM showed better forecasting accuracy on both validation and out-of-sample data among the forecasting methods used in this study. Third, the size of the training data is essential factor that should be considered when training forecasting methods.

关键词： Forecasting Bitcoin Training data Smoothing methods Training Time series analysis Support vector machines

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