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20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012

作者： Choi, Jongmoo Dumortier, Yann Prokaj, Jan Medioni, Gérard Computer Vision Lab. Institute for Robotics and Intelligent Systems University of Southern California United States

ISBN: (纸本)9781450316910

We present the design and implementation of an activity recognition system in wide area aerial video surveillance using Entity Relationship Models (ERM). In this approach, finding an activity is equivalent to sending a query to a Relational DataBase Management System (RDBMS). By incorporating reference imagery and Geographic Information System (GIS) data, tracked objects can be associated with physical meanings, and several high levels of reasoning, such as traffic patterns or abnormal activity detection, can be performed. We demonstrate that different types of activities, with hierarchical structure, multiple actors, and context information, are effectively and efficiently defined and inferred using the ERM framework. We also show how visual tracks can be better interpreted as activities by using geo information. Experimental results on both real visual tracks and GPS traces validate our approach. © 2012 Authors.

关键词： Security systems

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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 (IROS)

作者： Cid, Felipe Prado, Jose Augusto Bustos, Pablo Nunez, Pedro 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)9781467363587

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： Gabor filters belief networks edge detection emotion recognition face recognition feature extraction human-robot interaction image classification

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Simulation Study for Robot-based Single Point Incremental Forming

Simulation Study for Robot-based Single Point Incremental Fo...

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2023 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Shaker, Walid K. Klimchick, Alexandr Innopolis University Institute of Robotics and Computer Vision Innopolis Russia University of Lincoln School of Computer Science Lincoln United Kingdom

ISBN: (纸本)9781665475952

The present study investigates the toolpath definition for the numerical simulation of the Single Point Incremental Forming (SPIF). Since the toolpath definition greatly influences the geometrical accuracy of the formed part, substantial research has been taken to properly generate the toolpath. In this paper, two different toolpath strategies were defined: Z-level, and spiral toolpath. A robot program was created using RoboDK to simulate the SPIF process and to test the generated toolpath over a designed workpiece of a truncated cone. The paper also shows how the SPIF numerical simulation was conducted on Abaqus/CAE software. The results demonstrated that the toolpath was properly designed;however, 0.5% relative error was found between the desired and the simulated cone depth due to the SPIF geometric error. To improve the overall accuracy of the SPIF, a combined stiffness-based and closed loop control algorithm is proposed. © 2023 IEEE.

关键词： Numerical models

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NuSegDG: Integration of heterogeneous space and Gaussian kernel for domain-generalized nuclei segmentation

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Knowledge-Based Systems 2025年 322卷

作者： Lou, Zhenye Xu, Qing Jiang, Zekun He, Xiangjian Li, Chenxin Chen, Zhen Wang, Yi He, Maggie M. Duan, Wenting Sichuan University Pittsburgh Institute Sichuan University Chengdu China School of Computer Science Computer Vision and Intellifent Perception Laboratory and Municipal Digital Port Technology Key Laboratory University of Nottingham Ningbo Zhejiang China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Centre for Artificial Intelligence and Robotics (CAIR) Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Software Dalian University of Technology Dalian 116600 China Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong Department of Cardiology Gold Coast University Hospital QLD Australia School of Computer Science University of Lincoln Lincoln LN6 7TS United Kingdom

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment Anything Model (SAM) has made great success in universal image segmentation by interactive prompt modes (e.g., point and box). Despite its strengths, the original SAM presents limited adaptation to medical images. Moreover, SAM requires providing manual bounding box prompts for each object to produce satisfactory segmentation masks, so it is laborious in nuclei segmentation scenarios. To address these limitations, we propose a domain-generalizable framework for nuclei image segmentation, abbreviated to NuSegDG. Specifically, we first devise a Heterogeneous Space Adapter (HS-Adapter) to learn multi-dimensional feature representations of different nuclei domains by injecting a small number of trainable parameters into the image encoder of SAM. To alleviate the labour-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts. Furthermore, we present a Two-Stage Mask Decoder (TSM-Decoder) to effectively convert semantic masks to instance maps without the manual demand for morphological shape refinement. Based on our experimental evaluations, the proposed NuSegDG demonstrates state-of-the-art performance in nuclei semantic and instance segmentation, exhibiting superior domain generalization capabilities. The source code is available at https://***/xq141839/NuSegDG. © 2025 Elsevier B.V.

关键词： Domain generalization Foundation model Nuclei segmentation Parameter-efficient fine-tuning

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Reward Planning for Underactuated Robotic Systems: A Study on Pendubot with Parameters Uncertainty 7

Reward Planning for Underactuated Robotic Systems: A Study o...

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7th Scientific School: Dynamics of Complex Networks and their Applications, DCNA 2023

作者： Ibrahim, Sinan Khusainov, Ramil Jnadi, Ali Ahsan Kazmi, S.M. Innopolis University Institute of Robotics and Computer Vision Russia Innopolis University Center for Technologies in Robotics and Mechatronics Components Russia Computer Science and Creative Technologies Bristol United Kingdom

ISBN: (纸本)9798350305746

There is a burgeoning interest in the field of Reinforcement Learning (RL) across various domains, including robotics. Traditional control approaches for robotic systems, such as semi-definite programming (SDP) or mixed integer programming (MIP), typically rely on the assumption of a known and deterministic model of the environment. In contrast, this study aims to introduce a novel RL method for planning the reward function specifically tailored for underactuated robotic systems with parameter uncertainty. We propose a method with a single RL agent to address the challenge of swing up and balance of a Pendubot system with uncertain parameters. The ultimate objective is to enhance the system's ability to adapt and perform reliably in the face of varying uncertainties. © 2023 IEEE.

关键词： Reinforcement learning

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Real-time vision based auv navigation system using a complementary sensor suite

Real-time vision based auv navigation system using a complem...

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作者： Horgan, Jonathan Toal, Daniel Ridao, Pere Garcia, Rafael Mobile and Marine Robotics Research Centre Department of Electronic and Computer Engineering University of Limerick Ireland Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Spain

ISBN: (纸本)9783902661623

This paper proposes a real-time navigation system for an AUV that takes advantage of the complementary performance of a sensor suite including a DVL, a compass, a depth sensor and altimeter sensors with a feature based motion estimator using vision. To allow for real-time performance of the vision based motion estimator a simple but fast correlation algorithm is used for feature matching. The compass and the depth sensors are used to bound the drift of the heading and depth estimations respectively. The altimeter is required in order to translate the feature displacements measured from the images into the metric displacements of the robot. While the robot must rely on DVL navigation above a certain altitude where vision is useless, DVL measurements can be complemented with higher frequency accurate motion estimates from the vision system when navigating close to the seafloor. Copyright © 2007 IFAC.

关键词： Navigation systems

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Robot homing based on corner tracking in a sequence of panoramic images

Robot homing based on corner tracking in a sequence of panor...

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2001 IEEE computer Society Conference on computer vision and Pattern Recognition

作者： Argyros, Antonis A. Bekris, Kostas E. Orphanoudakis, Stelios C. Computer Vision and Robotics Lab. Institute of Computer Science Found. for Res./Technology - Hellas Heraklion Crete Greece

In robotics, homing can be defined as that behavior which enables a robot to return to its initial (home) position, after traveling a certain distance along an arbitrary path. Odometry has traditionally been used for the implementation of such a behavior, but it has been shown to be an unreliable source of information. In this work, a novel method for visual homing is proposed, based on a panoramic camera. As the robot departs from its initial position, it tracks characteristic features of the environment (corners). As soon as homing is activated, the robot selects intermediate target positions on the original path. These intermediate positions (IPs) are then visited sequentially, until the home position is reached. For the robot to move between two consecutive IPs, it is only required to establish correspondence among at least three corners. This correspondence is obtained through a feature tracking mechanism. The proposed homing scheme is based on the extraction of very low-level sensory information, namely the bearing angles of corners, and has been implemented on a robotic platform. Experimental results show that the proposed scheme achieves homing with a remarkable accuracy, which is not affected by the distance travelled by the robot.

关键词： robotics

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Backbone Based Feature Enhancement for Object Detection 15th

Backbone Based Feature Enhancement for Object Detection

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15th Asian Conference on computer vision, ACCV 2020

作者： Ji, Haoqin Lu, Weizeng Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030695347

FPN (Feature Pyramid Networks) and many of its variants have been widely used in state of the art object detectors and made remarkable progress in detection performance. However, almost all the architectures of feature pyramid are manually designed, which requires ad hoc design and prior knowledge. Meanwhile, existing methods focus on exploring more appropriate connections to generate features with strong semantics features from inherent pyramidal hierarchy of deep ConvNets (Convolutional Networks). In this paper, we propose a simple but effective approach, named BBFE (Backbone Based Feature Enhancement), to directly enhance the semantics of shallow features from backbone ConvNets. The proposed BBFE consists of two components: reusing backbone weight and personalized feature enhancement. We also proposed a fast version of BBFE, named Fast-BBFE, to achieve better trade-off between efficiency and accuracy. Without bells and whistles, our BBFE improves different baseline methods (both anchor-based and anchor-free) by a large margin (∼ 2.0 points higher AP) on COCO, surpassing common feature pyramid networks including FPN and PANet. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Planar Shape Control of Deformable Linear Objects

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IFAC-PapersOnLine 2022年第10期55卷 2469-2474页

作者： Karam Almaghout Alexandr Klimchik Robotics and Computer Vision Institute Innopolis University Innopolis Russia

Shape control of deformable linear objects (DLOs) is an open challenge, due to the difficulty in predicting the behavior of the DLOs during the manipulation. In this paper, we propose a new approach to achieve the shape control of a DLO grasped by two robotic manipulators on a plane. The proposed approach models the DLO as a set of points. Then it derives the Jacboian matrix that maps the velocity from the robots end-effectors to the DLO points. Moreover, A strategy to maintain the DLO length constraint during the manipulation is developed to avoid excessive stretching. The new algorithm is tested in simulation environment for different desired shapes. The experiments results prove the efficiency and accuracy of the proposed algorithm to place the DLO into the new shape.

关键词： Robotic manipulation Deformable objects Shape control Simulation

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ROV-aided dam inspection: Practical results 6

ROV-aided dam inspection: Practical results

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6th IFAC Conference on Manoeuvring and Control of Marine Craft, MCMC 2003

作者： Batlle, Joao Nicosevici, Tudor Garcia, Rafael Carreras, Marc Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona E.P.S. Girona17071 Spain

Nowadays Unmanned Underwater Vehicles (UlNs) 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. Copyright © 2003 IFAC

关键词： computer vision

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