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1st International Workshop on Self-Adaptive Software, IWSAS 2000

作者： Karuppiah, Deepak Deegan, Patrick Araujo, Elizeth Yang, Yunlei Holness, Gary Zhu, Zhigang Lerner, Barbara Grupen, Roderic Riseman, Edward Dept. of Computer Science Computer Vision Research Laboratory University of Massachusetts AmherstMA01003 United States Dept. of Computer Science Laboratory for Perceptual Robotics University of Massachusetts AmherstMA01003 United States Dept. of Computer Science Lab. for Advanced Software Eng. Research University of Massachusetts AmherstMA01003 United States

ISBN: (纸本)3540416552

Robot control in nonlinear and nonstationary run-time environments presents challenges to traditional software methodologies. In particular, robot systems in "open" domains can only be modeled probabilistically and must rely on run-time feedback to detect whether hardware/software configurations are adequate. Modifications must be effected while guaranteeing critical performance properties. Moreover, in multi-robot systems, there are typically many ways in which to compensate for inadequate performance. The computational complexity of high dimensional sensorimotor systems prohibits the use of many traditional centralized methodologies. We present an application in which a redundant sensor array, distributed spatially over an office-like environment can be used to track and localize a human being while reacting at run-time to various kinds of faults, including: hardware failure, inadequate sensor geometries, occlusion, and bandwidth limitations. Responding at run-time requires a combination of knowledge regarding the physical sensorimotor device, its use in coordinated sensing operations, and high- level process descriptions. We present a distributed control architecture in which run-time behavior is both preanalyzed and recovered empirically to inform local scheduling agents that commit resources autonomously subject to process control specifications. Preliminary examples of system performance are presented from the UMass Self-Adaptive Software (SAS) platform. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Process control

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A Real Time and Robust Facial Expression Recognition and Imitation approach for Affective Human-Robot Interaction Using Gabor filtering

A Real Time and Robust Facial Expression Recognition and Imi...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Felipe Cid Jose Augusto Prado Pablo Bustos Pedro Nunez Robotics and Artificial Vision Lab. Robolab Group University of Extremadura Spain Institute of Systems and Robotics Dept. Electrical and Computer Engineering University of Coimbra Portugal

ISBN: (纸本)9781467363563

Facial expressions are a rich source of communicative information about human behavior and emotion. This paper presents a real-time system for recognition and imitation of facial expressions in the context of affective Human Robot Interaction. The proposed method achieves a fast and robust facial feature extraction based on consecutively applying filters to the gradient image. An efficient Gabor filter is used, along with a set of morphological and convolutional filters to reduce the noise and the light dependence of the image acquired by the robot. Then, a set of invariant edge-based features are extracted and used as input to a Dynamic Bayesian Network classifier in order to estimate a human emotion. The output of this classifier updates a geometric robotic head model, which is used as a bridge between the human expressiveness and the robotic head. Experimental results demonstrate the accuracy and robustness of the proposed approach compared to similar systems.

关键词： Imitation approach emotion context of affective Human Robot Interaction

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Object recognition using saliency maps and HTM learning

Object recognition using saliency maps and HTM learning

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2012 IEEE International Conference on Imaging Systems and Techniques, IST 2012

作者： Kostavelis, Ioannis Nalpantidis, Lazaros Gasteratos, Antonios Laboratory of Robotics and Automation Department of Production and Management Engineering Democritus University of Thrace Vas. Sofias 12 GR-67100 Xanthi Greece Computer Vision and Active Perception Lab. Centre for Autonomous Systems Royal Institute of Technology - KTH SE-100 44 Stockholm Sweden

ISBN: (纸本)9781457717741

In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for recognition and categorization tasks. The HTM comprises a hierarchical tree structure that exploits enhanced spatiotemporal modules to memorize objects appearing in various orientations. In accordance with HTM's biological inspiration, human vision mechanisms can be used to preprocess the input images. Therefore, the input images undergo a saliency computation step, revealing the plausible information of the scene, where a human might fixate. The adoption of the saliency detection module releases the HTM network from memorizing redundant information and augments the classification accuracy. The efficiency of the proposed framework has been experimentally evaluated in the ETH-80 dataset, and the classification accuracy has been found to be greater than other HTM systems. © 2012 IEEE.

关键词： Object recognition

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SESAME: Semantic editing of scenes by adding, manipulating or erasing objects

arXiv

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arXiv 2020年

作者： Ntavelis, Evangelos Romero, Andrés Kastanis, Iason van Gool, Luc Timofte, Radu Computer Vision Lab. ETH Zurich Switzerland Robotics and Machine Learning CSEM SA Switzerland PSI ESAT KU Leuven Belgium

Recent advances in image generation gave rise to powerful tools for semantic image editing. However, existing approaches can either operate on a single image or require an abundance of additional information. They are not capable of handling the complete set of editing operations, that is addition, manipulation or removal of semantic concepts. To address these limitations, we propose SESAME, a novel generator-discriminator pair for Semantic Editing of Scenes by Adding, Manipulating or Erasing objects. In our setup, the user provides the semantic lab.ls of the areas to be edited and the generator synthesizes the corresponding pixels. In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former. We evaluate our model on a diverse set of datasets and report state-of-the-art performance on two tasks: (a) image manipulation and (b) image generation conditioned on semantic lab.ls. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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What programmable vector fields can (and cannot) do: force field algorithms for MEMS and vibratory plate parts feeders

What programmable vector fields can (and cannot) do: force f...

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IEEE International Conference on robotics and Automation (ICRA)

作者： K.-F. Bohringer B.R. Donald N.C. MacDonald Robotics & Vision Lab. Cornell Univ. Ithaca NY USA Robotics & Vision Laboratory Department of Computer Science Cornell University Ithaca NY USA School of Electrical Engineering and Cornell Nanofabrication Facility Cornell University Ithaca NY USA

Programmable vector fields can be used to control a variety of flexible planar parts feeders. When a part is placed on our devices, the programmed vector field induces a force and moment upon it. Over time, the part may come to rest in a dynamic equilibrium state. We demonstrate lower bounds on what the devices cannot do, and results on a classification of control strategies. We suggest sufficient conditions for programmable fields to induce well-behaved equilibria on every part placed on our devices. We define composition operators to build complex strategies from simple ones, and show the resulting fields are also well-behaved. We discuss whether fields outside this class can be useful and free of pathology. Using these tools, we describe new manipulation algorithms, and improve existing planning algorithms by a quadratic factor, and the plan-length by a linear factor. We relax earlier dynamic and mechanical assumptions to obtain more robust and flexible strategies. Finally, we consider parts feeders that can only implement a very limited "vocabulary" of vector fields. We discuss the trade-off between mechanical complexity and planning complexity.

关键词： Micromechanical devices Automatic control Force control Feeds Assembly Robot vision systems lab.ratories computer science Nanofabrication Uniform resource locators

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DISC: A Large-scale Virtual Dataset for Simulating Disaster Scenarios

DISC: A Large-scale Virtual Dataset for Simulating Disaster ...

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

作者： Hae-Gon Jeon Sunghoon Im Byeong-Uk Lee Dong-Geol Choi Martial Hebert In So Kweon The Robotics Institute Carnegie Mellon University Pittsburgh PA USA The Robotics and Computer Vision Lab. KAIST Daejeon Republic of Korea Department of Information and Communication Engineering Hanbat National University Daejeon Republic of Korea

In this paper, we present the first large-scale synthetic dataset for visual perception in disaster scenarios, and analyze state-of-the-art methods for multiple computer vision tasks with reference baselines. We simulated before and after disaster scenarios such as fire and building collapse for fifteen different locations in realistic virtual worlds. The dataset consists of more than 300K high-resolution stereo image pairs, all annotated with ground-truth data for semantic segmentation, depth, optical flow, surface normal estimation and camera pose estimation. To create realistic disaster scenes, we manually augmented the effects with 3D models using physical-based graphics tools. We use our dataset to train state-of-the-art methods and evaluate how well these methods can recognize the disaster situations and produce reliable results on virtual scenes as well as real-world images. The results obtained from each task are then used as inputs to the proposed visual odometry network for generating 3D maps of buildings on fire. Finally, we discuss challenges for future research.

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Context-transformer: Tackling object confusion for few-shot detection

arXiv

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arXiv 2020年

作者： Yang, Ze Wang, Yali Chen, Xianyu Liu, Jianzhuang Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Huawei Noah’s Ark Lab. SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are availab.e for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches. The codes are availab.e at https://***/Ze-Yang/ContextTransformer. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

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Towards efficient human-robot cooperation for socially-aware robot navigation in human-populated environments: the SNAPE framework

Towards efficient human-robot cooperation for socially-aware...

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IEEE International Conference on robotics and Automation (ICRA)

作者： A. Vega-Magro R. Gondkar L.J. Manso P. Núñez Robotics and Artificial Vision Lab. RoboLab Group University of Extremadura Spain Pune Institute of Computer Technology Pune University India College of Engineering and Physical Sciences Aston University Birmingham UK

It is widely accepted that in the future, robots will cooperate with humans in everyday tasks. Robots interacting with humans will require social awareness when performing their tasks which will require navigation. While navigating, robots should aim to avoid distressing people in order to maximize their chance of social acceptance. For instance, avoiding getting too close to people or disrupting interactions. Most research approaches these problems by planning socially accepted paths, however, in everyday situations, there are many examples where a simple path planner cannot solve all of the predicted robots’ navigation problems. For instance, requesting permission to interrupt a conversation if an alternative path cannot be determined requires deliberative skills. This article presents the Social Navigation framework for Autonomous robots in Populated Environments (SNAPE), where different software agents are integrated within a robotics cognitive architecture. SNAPE addresses action planning aimed at social-awareness navigation in realistic situations: it plans socially accepted paths and conversations to negotiate its trajectory to reach targets. In this article, the framework is evaluated in different use-cases where the robot, during its navigation, has to interact with different people in order to reach its goal. The results show that participants report that the robot’s behavior was realistic and human-like.

关键词： Navigation Conferences computer architecture Software agents Planning Trajectory Task analysis

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TTPP: Temporal transformer with progressive prediction for efficient action anticipation

arXiv

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arXiv 2020年

作者： Wang, Wen Peng, Xiaojiang Su, Yanzhou Qiao, Yu Cheng, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

Video action anticipation aims to predict future action categories from observed frames. Current state-of-the-art approaches mainly resort to recurrent neural networks to encode history information into hidden states, and predict future actions from the hidden representations. It is well known that the recurrent pipeline is inefficient in capturing long-term information which may limit its performance in predication task. To address this problem, this paper proposes a simple yet efficient Temporal Transformer with Progressive Prediction (TTPP) framework, which repurposes a Transformer-style architecture to aggregate observed features, and then leverages a light-weight network to progressively predict future features and actions. Specifically, predicted features along with predicted probabilities are accumulated into the inputs of subsequent prediction. We evaluate our approach on three action datasets, namely TVSeries, THUMOS-14, and TV-Human-Interaction. Additionally we also conduct a comprehensive study for several popular aggregation and prediction strategies. Extensive results show that TTPP not only outperforms the state-of-the-art methods but also more efficient. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Edge-based algorithm for discontinuity adaptive color image smoothing

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Pattern Recognition 2001年第2期34卷 333-342页

作者： Kang, Dong Joong Kweon, In So Signal Processing Lab. Samsung Adv. Institute of Technology P.O. Box 111 Suwon 440-600 Korea Republic of Robotics and Computer Vision Lab. Department of Electrical Engineering Korea Adv. Inst. Sci. and Technol. 207-43 Cheong. Dongdaemoon-Gu Seoul United States Pusan National University Pusan Korea Republic of KAIST Seoul Korea Republic of Seoul National University Seoul Korea Republic of Carnegie Mellon University Pittsburgh PA United States

We present a new scheme to increase the performance of edge-preserving image smoothing algorithm from the parameter tuning of a Markov random field (MRF) function. This method is based on an automatic control of the image-smoothing strength in the MRF function in which the parameter control of the function depends on edge magnitude resulted from discontinuities of image intensity. Without any binary decision for the edge magnitude, direct use of full edge magnitude could improve the performance of discontinuity-preserving image smoothing technique.

关键词： Image enhancement

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