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IEEE International Conference on robotics and Automation (ICRA)

作者： Simeon Nedelchev Daniil Kirsanov Igor Gaponov Hyeonseok Seong Jee-Hwan Ryu Institute of Robotics and Computer Vision Innopolis University Innopolis Russia KAIST Daejeon South Korea

Twisted string actuators (TSA) are efficient, compliant cable actuators with high power density that have found their way into many engineering and robotics applications in recent years. They are generally used in the scenarios involving comparatively slow positioning, since quick motions in TSAs often result in large overshoot values, undesired oscillations and loss of cable tension. This work discusses time optimal trajectory generation for point-to-point transitions in TSA. We propose a method to generate smooth trajectories by directly solving an optimal control problem while respecting constraints on motor torque and speed and preventing tension loss on cables. We have conducted an experimental evaluation demonstrating that, in comparison to classical, constrained ‘bangbang’ transitions, the proposed trajectories result in much faster settling time, nearly-zero overshoot and require almost 60% less motor power for tracking. The proposed method can help TSAs find their way into highly-dynamical applications that have been previously deemed to be too demanding, which include parallel manipulators and antagonistically-controlled systems.

关键词： Actuators Power system measurements Torque Trajectory planning Density measurement Conferences Optimal control

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Evaluating Open-Universe Face Identification on the Web

Evaluating Open-Universe Face Identification on the Web

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IEEE Conference on computer vision and Pattern Recognition Workshops

作者： Brian C. Becker Enrique G. Ortiz Robotics Institute Carnegie Mellon University Center for Research in Computer Vision University of Central Florida

ISBN: (纸本)9781479909940

Face recognition is becoming a widely used technique to organize and tag photos. Whether searching, viewing, or organizing photos on the web or in personal photo albums, there is a growing demand to index real-world photos by the subjects in them. Even consumer platforms such as Google Picasa, Microsoft Photo Gallery, and social network sites such as Facebook have integrated forms of automated face tagging and recognition;furthermore, a number of libraries and cloud-based APIs that perform face recognition have become available. With such a plethora of choices, comparisons of recent advances become more important to gauge the state of progress in the field. This paper evaluates face identification in the context of not only research algorithms, but also considers consumer photo products, client-side libraries, and cloud-based APIs on a new, large-scale dataset derived from PubFig83 and LFW in a realistic open-universe scenario.

关键词： Facial Recognition webs World Wide Web congenital web Web search engines Facebook Libraries Plethora Social Media Apis integrated forming Social networking web sites websites

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Attention-Driven Dynamic Graph Convolutional Network for Multi-label Image Recognition 16th

Attention-Driven Dynamic Graph Convolutional Network for Mul...

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16th European Conference on computer vision, ECCV 2020

作者： Ye, Jin He, Junjun Peng, Xiaojiang Wu, Wenhao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Biomedical Engineering the Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783030585884

Recent studies often exploit Graph Convolutional Network (GCN) to model label dependencies to improve recognition accuracy for multi-label image recognition. However, constructing a graph by counting the label co-occurrence possibilities of the training data may degrade model generalizability, especially when there exist occasional co-occurrence objects in test images. Our goal is to eliminate such bias and enhance the robustness of the learnt features. To this end, we propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image. ADD-GCN adopts a Dynamic Graph Convolutional Network (D-GCN) to model the relation of content-aware category representations that are generated by a Semantic Attention Module (SAM). Extensive experiments on public multi-label benchmarks demonstrate the effectiveness of our method, which achieves mAPs of 85.2%, 96.0%, and 95.5% on MS-COCO, VOC2007, and VOC2012, respectively, and outperforms current state-of-the-art methods with a clear margin. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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Active regions for colour texture segmentation integrating region and boundary information

Active regions for colour texture segmentation integrating r...

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IEEE International Conference on Image Processing

作者： X. Munoz J. Freixenet X. Cufi J. Marti Computer Vision and Robotics Group. Institute of Informatics and Applications University of Girona Girona Spain

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method.

关键词： Image segmentation Proposals Data mining Image edge detection Pixel computer vision Robot vision systems Informatics Application software Kernel

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Learning to infer the depth map of a hand from its color image

arXiv

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arXiv 2018年

作者： Nicodemou, Vassilis C. Oikonomidis, Iason Tzimiropoulos, Georgios Argyros, Antonis Computational Vision and Robotics Laboratory Institute of Computer Science FORTH Greece Computer Science Department University of Crete Greece Computer Vision Laboratory University of Nottingham United Kingdom

We propose the first approach to the problem of inferring the depth map of a human hand based on a single RGB image. We achieve this with a Convolutional Neural Network (CNN) that employs a stacked hourglass model as its main building block. Intermediate supervision is used in several outputs of the proposed architecture in a staged approach. To aid the process of training and inference, hand segmentation masks are also estimated in such an intermediate supervision step, and used to guide the subsequent depth estimation process. In order to train and evaluate the proposed method we compile and make publicly available HandRGBD, a new dataset of 20,601 views of hands, each consisting of an RGB image and an aligned depth map. Based on HandRGBD, we explore variants of the proposed approach in an ablative study and determine the best performing one. The results of an extensive experimental evaluation demonstrate that hand depth estimation from a single RGB frame can be achieved with an accuracy of 22mm, which is comparable to the accuracy achieved by contemporary low-cost depth cameras. Such a 3D reconstruction of hands based on RGB information is valuable as a final result on its own right, but also as an input to several other hand analysis and perception algorithms that require depth input. Essentially, in such a context, the proposed approach bridges the gap between RGB and RGBD, by making all existing RGBD-based methods applicable to RGB input. Copyright © 2018, The Authors. All rights reserved.

关键词： Bridge approaches

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A Toolkit to Generate Social Navigation Datasets 21st

A Toolkit to Generate Social Navigation Datasets

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21st International Workshop of Physical Agents, WAF 2020

作者： Baghel, Rishabh Kapoor, Aditya Bachiller, Pilar Jorvekar, Ronit R. Rodriguez-Criado, Daniel Manso, Luis J. Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India Department of Computer Science and Information Systems Birla Institute of Technology and Science Goa Pilani India Robotics and Artificial Vision Laboratory University of Extremadura Badajoz Spain Department of Computer Engineering Pune Institute of Computer Technology Pune India Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham United Kingdom

ISBN: (纸本)9783030625788

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians’ movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Navigation

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Underwater image super-resolution using deep residual multipliers

arXiv

引用

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

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Explicit Model Predictive Control Design based on Constrained Zonotope Propagation

Explicit Model Predictive Control Design based on Constraine...

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School on Dynamics of Complex Networks and their Application in Intellectual robotics (DCNAIR)

作者： Ali Jnadi Ramil Khusainov Simeon Nedelchev Sergei Savin Institute of Robotics and Computer Vision Innopolis University Innopolis Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

Zonotopes have become a widely used tool in control design, demonstrating their effectiveness in various applications ranging from state estimation to explicit model predictive control. However, the traditional use of zonotopes is limited by a set certain geometric shapes that restrict the range of state sets that can be considered. This paper proposes a novel approach to control design using constrained zonotopes that can encode arbitrary polytopes. We present a propagation scheme and an affine approximation of the exact control law for this representation, and discuss issues related to center identification for constrained zonotopes. A fast method for detecting set membership is also proposed based on lifting the constrained zonotopes into a higher dimensional space. The proposed method extends the range of shapes that can be considered in control design and offers a promising alternative to the limitations of regular zonotopes. It has the potential for wide-ranging impact in various areas of control, providing a valuable tool for solving complex control problems.

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ROV-Aided Dam Inspection: Practical Results

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IFAC Proceedings Volumes 2003年第21期36卷 271-274页

作者： Joan Batlle Tudor Nicosevici Rafael Garcia Marc Carreras Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona E.P.S. 17071 Girona Spain

Nowadays Unmanned Underwater Vehicles (UUVs) are used in a growing number of underwater missions, mostly inspections of different types of installations such as: telecommunication cables, pipes or oil extraction installations. A field where UUVs are still starting to prove their utility is dam inspection. This paper presents details our first approach in dam inspection. A set of experiments carried out at a dam in Romania is described. Advantages and drawbacks of using a small-class underwater vehicle are analyzed for this type of missions.

关键词： computer vision teleoperation fault detection motion estimation

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