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Exploiting autoencoder’s weakness to generate pseudo anomalies

Neural Computing and Applications 2024年第23期36卷 14075-14091页

作者： Astrid, Marcella Zaheer, Muhammad Zaigham Aouada, Djamila Lee, Seung-Ik Department of Artificial Intelligence University of Science and Technology Daejeon34113 Korea Republic of Field Robotics Research Section Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg1855 Luxembourg Department of Computer Vision Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE is expected to well reconstruct normal but to poorly reconstruct anomalous data. However, contrary to the expectation, anomalous data are often well reconstructed as well. In order to further separate the reconstruction quality between normal and anomalous data, we propose creating pseudo anomalies from learned adaptive noise by exploiting the aforementioned weakness of AE, i.e., reconstructing anomalies too well. The generated noise is added to the normal data to create pseudo anomalies. Extensive experiments on Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the effectiveness and generic applicability of our approach in improving the discriminative capability of AEs for anomaly detection. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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Traffic observation and situation assessment

Traffic observation and situation assessment

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15th International Workshop on Theoretical Foundations of computer vision

作者： Reulke, Ralf Rueß, Dominik Manthey, Kristian Luber, Andreas Humboldt-Universität Zu Berlin Computer Vision Unter den Linden 6 10099 Berlin Germany DLR German Aerospace Center Institute of Robotics and Mechatronics 12489 Berlin Germany DLR German Aerospace Center Institute of Transportation Systems Rutherfordstr. 2 12489 Berlin Germany

ISBN: (纸本)9783642340901

Utilization of camera systems for surveillance tasks (e. g. traffic monitoring) has become a standard procedure and has been in use for over 20 years. However, most of the cameras are operated locally and data analyzed manually. Locally means here a limited field of view and that the image sequences are processed independently from other cameras. For the enlargement of the observation area and to avoid occlusions and non-accessible areas multiple camera systems with overlapping and non-overlapping cameras are used. The joint processing of image sequences of a multi-camera system is a scientific and technical challenge. The processing is divided traditionally into camera calibration, object detection, tracking and interpretation. The fusion of information from different cameras is carried out in the world coordinate system. To reduce the network load, a distributed processing concept can be implemented. Object detection and tracking are fundamental image processing tasks for scene evaluation. Situation assessments are based mainly on characteristic local movement patterns (e.g. directions and speed), from which trajectories are derived. It is possible to recognize atypical movement patterns of each detected object by comparing local properties of the trajectories. Interaction of different objects can also be predicted with an additional classification algorithm. This presentation discusses trajectory based recognition algorithms for atypical event detection in multi object scenes to obtain area based types of information (e.g. maps of speed patterns, trajectory curvatures or erratic movements) and shows that two-dimensional areal data analysis of moving objects with multiple cameras offers new possibilities for situational analysis. © 2012 Springer-Verlag.

关键词： Trajectories

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Maize EfficientNet Fusion: Advancing Maize Disease Detection with MF-NET 25

Maize EfficientNet Fusion: Advancing Maize Disease Detection...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Khalid, Fatima Hanif, Muhammad Ul Ain, Qurat Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Faculty of Computer Science and Engineering KPK Topi Pakistan Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Aerial Robotics and Vision Lab KPK Topi Pakistan Shifa Tameer-E-Millat University Department of Computing Islamabad Pakistan

ISBN: (纸本)9798350379037

Maize is a vital global crop, essential for food security but highly susceptible to diseases that threaten yield and quality. Traditional methods for detecting these diseases are computationally intensive and rely on high-quality images, limiting their practical use in diverse field conditions. This research addresses these challenges by proposing MF-Net, a novel model that leverages two EfficientNet-b0 architectures with ReLU and Swish activation functions. Our approach involves partially training Model R with frozen early layers using ReLU, and fully training Model S with Swish activation for enhanced optimization and robustness. We employ model concatenation and feature fusion techniques to create a lightweight yet powerful model, complemented by additional layers for improved feature extraction and regularization. Experimental results on the Corn Disease and Severity (CD&S) dataset are compelling: MF-Net achieves a remarkable 95.7% accuracy and 96% precision in disease classification, and 88.3% accuracy and 88.46% precision in severity level detection. These results highlight the model's effectiveness under challenging conditions such as cluttered backgrounds and variable lighting, significantly reducing computation time without compromising accuracy. Hence, MF-Net presents an innovative and efficient solution for maize disease detection and severity assessment, offering significant advancements for agricultural practices and crop management. © 2024 IEEE.

关键词： CD&S Dataset EffiecientNet-b0 Fused Truncated EfficientNet Maize Disease Maize Severity Level

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The mapping of texture on VR polygonal models 2

The mapping of texture on VR polygonal models

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2nd International Conference on 3-D Digital Imaging and Modeling, 3DIM 1999

作者： Laurendeau, Denis Bertrand, Nathalie Houde, Régis Computer Vision and Systems Laboratory Department of Electrical Engineering and Computer Engineering Laval University QuebecQCG1K 7P4 Canada Robotics Division Hydro Quebec Research Institute VarennesQCJ3X 1S1 Canada

ISBN: (纸本)0769500625

The paper addresses the problem of choosing automatically, and without user intervention, the best possible 2D image that should be used to map as a texture on polygons of a geometric model of complex scenes made of several objects. The geometric models and the texture data are obtained separately: the models are built from range data while the texture information is obtained from 2D color images of the scene. The system does not make any assumption on the way the texture data has been acquired. Texture selection results are presented for an indoor scene. © 1999 IEEE.

关键词： Image texture

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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 Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong 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 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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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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Autonomous I-AUV Docking for Fixed-base Manipulation

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IFAC Proceedings Volumes 2014年第3期47卷 12160-12165页

作者： Narcís Palomeras Pere Ridao David Ribas Guillem Vallicrosa Computer Vision and Robotics Research Institute Scientific and Technological Park of the University of Girona CIRS Building C/ Pic de Peguera CO 17071 Girona Catalonia - Spain

While commercially available autonomous underwater vehicles (AUVs) are routinely used in survey missions, a new set of applications exist demanding intervention capabilities. This is the case, for instance, of the maintenance of permanent underwater observatories or submerged oil wells. These tasks, currently undertaken by remotely operated vehicles (ROVs), can be automated using intervention AUVs (I-AUVs) reducing their complexity and costs. The TRITON spanish funded project proposes the use of light I-AUV for autonomous intervention tasks, such as valve turning or connector pluging/unpluging, in adapted sub-sea infrastructures. To this aim, this paper presents the design and implementation of an I-AUV-friendly sub-sea docking panel, as well as the vision-based autonomous docking procedure for the Girona 500 lightweight I-AUV. The panel implements a funnel-based docking method for passive accommodation. It also includes a T valve and a custom designed hot stab connector. Once docked, the I-AUV and the panel become rigid and basic fixed-base manipulation strategies can be used for manipulation.

关键词： Autonomous vehicles Marine systems Robot navigation Robot vision Robot control Robotic manipulators

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Complexity based investigation in collaborative assembly scenarios via non intrusive techniques

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Procedia computer Science 2023年 217卷 478-485页

作者： Sotirios Panagou Monica Sileo Konstantinos Papoutsakis Fabio Fruggiero Ammar Qammaz Antonis Argyros School of Engineering University of Basilicata 85100 Potenza Italy Computational Vision and Robotics Laboratory Institute of Computer Science Foundation for Research and Technology – Hellas 70013 Heraklion Greece

Human and robot collaboration in assembly tasks is an integral part in modern manufactories. Robots provide advantages in both process and productivity with their repeatability and usability in different tasks, while human operators provide flexibility and can act as safeguards. However, process complexity increases which can lower the overall quality. Increased complexity can negatively influence decision making due to cognitive load on human operators, which can lead to lower quality, be it product, process or human work. Moreover, it can lead to safety risks, human-system error and accidents. In this work, we present the preliminary results on an experiment performed with student-participants, based on an assembly task. The experiment was set up to emulate an industrial assembly, and data collection was performed through qualitative and non-intrusive quantitative methods. Questionnaires were used to assess perceptual task complexity and cognitive load, while a stereo camera provided recordings for after-task analysis on process errors and human work quality based on a 3D skeleton-based human pose estimation and tracking method. The aim of the study is to investigate causes of errors and implications on quality. Future direction of the work is discussed.

关键词： collaboration assembly task complexity cognitive load human pose estimation tracking

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A Music-Therapy Robotic Platform for Children with Autism: A Pilot Study

arXiv

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arXiv 2022年

作者： Fengr, Huanghao Mahoor, Mohammad H. Dino, Francesca Changshu Institute of Technology Suzhou Changshu China Computer Vision and Social Robotics Labarotory Department of Electrical and Computer Engineering University of Denver United States

Children with Autism Spectrum Disorder (ASD) experience deficits in verbal and nonverbal communication skills including motor control, turn-taking, and emotion recognition. Innovative technology, such as socially assistive robots, has shown to be a viable method for Autism therapy. This paper presents a novel robot-based music-therapy platform for modeling and improving the social responses and behaviors of children with ASD. Our autonomous social interactive system consists of three modules. Module 1 provides an autonomous initiative positioning system for the robot, NAO, to properly localize and play the instrument (Xylophone) using the robot's arms. Module 2 allows NAO to play customized songs composed by individuals. Module 3 provides a real-life music therapy experience to the users. We adopted Short-time Fourier Transform and Levenshtein distance to fulfill the design requirements: a)"music detection" and b)"smart scoring and feedback", which allows NAO to understand music and provide additional practice and oral feedback to the users as applicable. We designed and implemented six Human-Robot-Interaction (HRI) sessions including four intervention sessions. Nine children with ASD and seven Typically Developing participated in a total of fifty HRI experimental sessions. Using our platform, we collected and analyzed data on social behavioral changes and emotion recognition using Electrodermal Activity (EDA) signals. The results of our experiments demonstrate most of the participants were able to complete motor control tasks with 70% accuracy. Six out of the 9 ASD participants showed stable turn-taking behavior when playing music. The results of automated emotion classification using Support Vector Machines illustrates that emotional arousal in the ASD group can be detected and well recognized via EDA bio-signals. In summary, the results of our data analyses, including emotion classification using EDA signals, indicate that the proposed robot-music based therapy p

关键词： Diseases

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Development of a Twisted String Actuator-based Exoskeleton for Hip Joint Assistance in Lifting Tasks

Development of a Twisted String Actuator-based Exoskeleton f...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Hyeon-Seok Seong Do-Hyeong Kim Igor Gaponov Jee-Hwan Ryu Department of Civil and Environmental Engineering KAIST Daejeon South Korea Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

This paper presents a study on a compliant cable-driven exoskeleton for hip assistance in lifting tasks that is aimed at preventing low-back pain and injuries in the vocational setting. In the proposed concept, we used twisted string actuator (TSA) to design a light-weight and powerful exoskeleton that benefits from inherent TSA advantages. We have noted that nonlinear nature of twisted strings' transmission ratio (decreasing with twisting) closely matched typical torque-speed requirements for hip assistance during lifting tasks and tried to use this fact in the exoskeleton design and motor selection. Hip-joint torque and speed required to lift a 10-kg load from stoop to stand were calculated, which gave us a baseline that we used to design and manufacture a practical exoskeleton prototype. Preliminary experimental trials demonstrated that the proposed device was capable of generating required torque and speed at the hip joint while weighing under 6 kg, including battery.

关键词： Hip Exoskeletons Torque Pulleys Actuators Kinematics Task analysis

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