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作者： Roger Pi Joan Esteba Patryk Cieslak Narcís Palomeras Pedro J. Sanz Raúl Marín Pere Ridao Computer Vision and Robotics Research Institute (VICOROB) Universitat de Girona Girona Interactive and Robotic Systems Lab (IRS-Lab) Universitat Jaume I Castelló de la Plana

OPTIHROV is a proof-of-concept project to demonstrate a telerobotic system, based on an untethered, OPTIcally-connected, Hybrid Autonomous-ROV, that aims to replace light intervention ROVs operating at close range to networked infrastructures. The system is designed to bridge the gap between current ROVs, tethered to a Docking Station, and the dream of the future Intervention Autonomous Underwater Vehicle (I-AUV). OPTIHROV is a telerobotic system to perform inspection and light intervention tasks in potentially unknown and unstructured environments. It uses human-robot interaction (HRI) methods that allow the user to remotely guide the robot by launching a set of sensory-motion primitives performed by the robot *** paper presents a proof of concept demonstration of the OPTIHROV concept, where the wireless Hybrid Remotely Operated Vehicle (HROV), located in the test tank of the Universitat de Girona (UdG), is controlled/supervised from the Universitat Jaume I (UJI) in Castelló de la Plana, about 330 km away.

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Exploiting Aggregation and Segregation of Representations for Domain Adaptive Human Pose Estimation

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arXiv 2024年

作者： Peng, Qucheng Zheng, Ce Ding, Zhengming Wang, Pu Chen, Chen Center for Research in Computer Vision University of Central Florida Orlando United States Robotics Institute Carnegie Mellon University Pittsburgh United States Department of Computer Science Tulane University New Orleans United States University of North Carolina at Charlotte Charlotte United States

Human pose estimation (HPE) has received increasing attention recently due to its wide application in motion analysis, virtual reality, healthcare, etc. However, it suffers from the lack of labeled diverse real-world datasets due to the time- and labor-intensive annotation. To cope with the label deficiency issue, one common solution is to train the HPE models with easily available synthetic datasets (source) and apply them to real-world data (target) through domain adaptation (DA). Unfortunately, prevailing domain adaptation techniques within the HPE domain remain predominantly fixated on effecting alignment and aggregation between source and target features, often sidestepping the crucial task of excluding domain-specific representations. To rectify this, we introduce a novel framework that capitalizes on both representation aggregation and segregation for domain adaptive human pose estimation. Within this framework, we address the network architecture aspect by disentangling representations into distinct domain-invariant and domain-specific components, facilitating aggregation of domain-invariant features while simultaneously segregating domain-specific ones. Moreover, we tackle the discrepancy measurement facet by delving into various keypoint relationships and applying separate aggregation or segregation mechanisms to enhance alignment. Extensive experiments on various benchmarks, e.g., Human3.6M, LSP, H3D, and FreiHand, show that our method consistently achieves state-of-the-art performance. The project is available at https: //***/davidpengucf/EPIC. © 2024, CC BY.

关键词： Virtual reality

Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping

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arXiv 2024年

作者： Lin, Qinliang Luo, Cheng Niu, Zenghao He, Xilin Xie, Weicheng Hou, Yuanbo Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing China WAVES Research Group Ghent University Belgium University of Leicester United Kingdom

Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems. To address this problem, many transferability enhancement approaches (e.g., input transformation and model augmentation) have been proposed. However, they show poor performances in attacking systems having different model genera from the surrogate model. In this paper, we propose a novel and generic attacking strategy, called Deformation-Constrained Warping Attack (DeCoWA), that can be effectively applied to cross model genus attack. Specifically, DeCoWA firstly augments input examples via an elastic deformation, namely Deformation-Constrained Warping (DeCoW), to obtain rich local details of the augmented input. To avoid severe distortion of global semantics led by random deformation, DeCoW further constrains the strength and direction of the warping transformation by a novel adaptive control strategy. Extensive experiments demonstrate that the transferable examples crafted by our DeCoWA on CNN surrogates can significantly hinder the performance of Transformers (and vice versa) on various tasks, including image classification, video action recognition, and audio recognition. Code is made available at https://***/LinQinLiang/DeCoWA. © 2024, CC BY-NC-SA.

关键词： Semantics

An Experimental Study of Keypoint Descriptor Fusion

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An Experimental Study of Keypoint Descriptor Fusion

IEEE International Conference on robotics and Biomimetics

作者： Yaling Pan Li He Yisheng Guan Hong Zhang Biomimetic Intelligent Robotics Research Laboratory (BIRL) Guangdong University of Technology Guangzhou China Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9781665481106

Local feature descriptors play a crucial role in computer vision problems, especially robot motion. Existing descriptors are highly accurate, but their performance de-pends on the influence of distracting factors, such as illumi-nation and viewpoint. There is room for further improvement of these descriptors. In this paper, we provide an in-depth analysis of several exciting features of the descriptor fusion model (DFM) we have proposed in our recent work, which uses an autoencoder to combine descriptors and exploit their respective advantages. With this DFM framework, we fur-ther validate that fused descriptors can retain advantageous properties and that our DFM is a generally applicable method with respect to various component descriptors. Specifically, we evaluate multiple combinations of hand-crafted and CNN descriptors concerning their performance on a benchmark dataset with illumination and viewpoint changes to obtain comprehensive experimental results. The results show that the fused descriptors have better matching accuracy than their component descriptors.

关键词： Robot motion computer vision Analytical models Biomimetics Lighting Benchmark testing

HPPS: A Hierarchical Progressive Perception System for Luggage Trolley Detection and Localization at Airports

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arXiv 2024年

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys at airports. However, existing methods for detecting and locating these luggage trolleys often fail when they are not fully visible. To address this, we introduce the Hierarchical Progressive Perception System (HPPS), which enhances the detection and localization of luggage trolleys under partial occlusion. The HPPS processes the luggage trolley’s position (x, y) and orientation (θ) separately, which requires only RGB images for labeling and training, eliminating the need for 3D coordinates and alignment. The HPPS can accurately determine the position of the luggage trolley with just one well-detected keypoint and estimate the luggage trolley’s orientation when it is partially occluded. Once the luggage trolley’s initial pose is detected, HPPS updates this information continuously to refine its accuracy until the robot begins grasping. The experiments on detection and localization demonstrate that HPPS is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. A website about this work is available at HPPS. Copyright © 2024, The Authors. All rights reserved.

关键词： robotics

A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation

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arXiv 2023年

作者： Zhao, Qitao Zheng, Ce Liu, Mengyuan Chen, Chen Robotics Institute Carnegie Mellon University United States Center for Research in Computer Vision University of Central Florida United States Key Laboratory of Machine Perception Peking University Shenzhen Graduate School China

The dominant paradigm in 3D human pose estimation that lifts a 2D pose sequence to 3D heavily relies on long-term temporal clues (i.e., using a daunting number of video frames) for improved accuracy, which incurs performance saturation, intractable computation and the non-causal problem. This can be attributed to their inherent inability to perceive spatial context as plain 2D joint coordinates carry no visual cues. To address this issue, we propose a straightforward yet powerful solution: leveraging the readily available intermediate visual representations produced by off-the-shelf (pre-trained) 2D pose detectors - no finetuning on the 3D task is even needed. The key observation is that, while the pose detector learns to localize 2D joints, such representations (e.g., feature maps) implicitly encode the joint-centric spatial context thanks to the regional operations in backbone networks. We design a simple baseline named Context-Aware PoseFormer to showcase its effectiveness. Without access to any temporal information, the proposed method significantly outperforms its context-agnostic counterpart, PoseFormer [77], and other state-of-the-art methods using up to hundreds of video frames regarding both speed and precision. Project page: ***/ContextAware-PoseFormer. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

High-resolution imaging system for intelligent total body scanner for early detection of melanoma

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High-resolution imaging system for intelligent total body sc...

Photonics in Dermatology and Plastic Surgery 2025

作者： Jütte, Lennart González-Villà, Sandra Quintana, Josep Garcia, Rafael Roth, Bernhard Hannover Centre for Optical Technologies Leibniz University Hannover Hannover Germany Coronis Computing S.L. Girona Spain Institute of Computer Vision and Robotics Research Universitat de Girona Girona Spain Cluster of Excellence PhoenixD Leibniz University Hannover Hannover Germany

ISBN: (纸本)9781510683327

Melanoma's increasing global incidence highlights the need for high-throughput, automatable early detection methods like total body scanners integrated with computer-aided diagnosis. This study presents a High-Resolution Imaging Module for non-contact dermoscopy, designed to acquire and process raw image data into high-resolution dermoscopic images using a focus stacking approach. By merging multiple images, each with limited depth of field, into a single hyperfocus image, this method ensures complete focus across all skin topographies. The system utilizes an electrically tunable liquid lens for rapid capture of differently focused images, supported by algorithms for image alignment, focus measurement, and fusion. Deep learning-based super-resolution techniques were applied to further enhance image quality, and an AI classification model was trained for lesion assessment. The developed system achieves a resolution of 27.84 μm and captures focus stacks at 50 FPS, ensuring rapid acquisition and patient comfort. Hyperfocus images produced demonstrate high clarity and detail, significantly improving diagnostic reliability. However, the application of super-resolution techniques necessitates careful consideration to maintain the authenticity of raw data. This work demonstrates the potential of integrating advanced imaging technologies into total body scanners, offering a non-invasive, high-accuracy solution for early melanoma detection in clinical practice. © 2025 SPIE.

关键词： Noninvasive medical procedures

Generative AI for enhanced skin cancer diagnosis, dermatologist training, and patient education

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Generative AI for enhanced skin cancer diagnosis, dermatolog...

Photonics in Dermatology and Plastic Surgery 2025

作者： Jütte, Lennart González-Villà, Sandra Quintana, Josep Steven, Martin Garcia, Rafael Roth, Bernhard Hannover Centre for Optical Technologies Leibniz University Hannover Hannover Germany Coronis Computing S.L. Girona Spain Institute of Computer Vision and Robotics Research Universitat de Girona Girona Spain Cluster of Excellence PhoenixD Leibniz University Hannover Hannover Germany

ISBN: (纸本)9781510683327

The early detection and monitoring of suspicious skin lesions are essential for effective dermatological diagnosis and treatment, particularly in understanding the progression of nevi to melanoma. To advance this understanding, we developed a simulation framework that models the transformation of nevi into melanoma using Cycle-Consistent Generative Adversarial Networks and frame interpolation to generate a detailed dataset of simulated lesion progressions. Optical flow analysis was applied to these dermoscopic image sequences, providing quantitative insights into lesion transformations and dynamic changes. Heatmap visualizations and optical flow vectors highlighted regions of significant activity and confirmed the fidelity of the simulated transformations. Additionally, we demonstrate how this new approach can visually complement existing textual explainable AI methods in dermatology, enhancing interpretability and trust in diagnostic outcomes. These findings represent a significant step toward improving dermatological diagnostics, patient education, and the early detection of melanoma. © 2025 SPIE.

关键词： Medical education

AI-based predictive and detection models for Avian Pox caused by Avipoxvirus Spp in the Galápagos Islands

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AI-based predictive and detection models for Avian Pox cause...

International Conference on Technological Innovation and AI research (ICTIAIR 2025)

作者： Jonathan Javier Loor-Duque Grace Rodríguez Ariana Deyaneira Jiménez-Narváez Juan David Moromenacho-Aguirre Fernando Patricio Carranco-Avila Clayanela Zambrano-Caicedo Iván Galo Reyes-Chacón Paulina Vizcaíno Manuel Eugenio Morocho-Cayamcela Yachay Tech University School of Mathematical and Computational Sciences Deep Learning for Autonomous Driving Robotics and Computer Vision Research Group DeepARC Research Urcuquí 100119 Ecuador Pontificia Universidad Católica del Ecuador Faculty of Exact and Natural Sciences Biology Quito Ecuador Universidad Internacional del Ecuador Faculty of Technical Sciences School of Computer Science Quito Ecuador

ISBN: (数字)9781837243235

Predicting infection by Avipoxvirus spp, the causative agent of avian pox, in endemic Galápagos Islands species such as the Geospiza fuliginosafinches, represents an innovative approach to controlling viral diseases in vulnerable ecosystems. This study uses artificial intelligence-based predictive models trained on a database compiled from previous research. The database was balanced and subjected to data augmentation like synthetic minority oversampling and tablediffusion. Compared with alternative studies, where the data is converted from tabular data into an image achieving a recall of 85%; our results achieve a recall of near 100% using a multi-state analysis over susceptible-infectious-recovered models in infectious diseases for populations of the European serins ( Serinus serinus), highlighting the anticipatory capacity for early identification of outbreaks and risk factors. This predictive tool not only strengthens biodiversity protection in the archipelago but also supports the development of targeted vaccines and treatments, ensuring the preservation of this unique ecosystem and providing a model applicable for monitoring other infectious diseases in bird populations.

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Achachay App: a community-driven innovation for flood data collection in urban and rural areas

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Achachay App: a community-driven innovation for flood data c...

International Conference on Technological Innovation and AI research (ICTIAIR 2025)

作者： Ariana Deyaneira Jiménez-Narváez Jonathan Javier Loor-Duque Diego Josue Andrade-Pelaez Manuel Eugenio Morocho-Cayamcela Raisa Torres-Ramírez Deep Learning for Autonomous Driving Robotics and Computer Vision Research Group (DeepARC) School of Mathematical and Computational Sciences Yachay Tech University San Miguel de Urcuquí Ecuador Applied Geology and Research Group School of Geological Sciences Energy and Environment Yachay Tech University San Miguel de Urcuquí Ecuador

ISBN: (数字)9781837243235

Floods are among the most frequent and devastating natural disasters, significantly impacting urban and rural communities worldwide. Achachay App is a mobile application designed to enhance flood data collection and analysis through direct community engagement. The app enables users to report real-time flood events by sharing detailed descriptions, geographic coordinates, and multimedia evidence such as photos or videos. researchers use these community-sourced data to analyze and reconstruct watercourse behaviors that lead to flooding. Ecuador was selected for implementation as a pilot initiative, where the app successfully mapped flood-prone areas and secondary flows. These features aim to deepen understanding of the interplay between geological and climatic phenomena, supporting disaster preparedness and prevention. By bridging technology and community collaboration, the Achachay Appoffers valuable insights for policymakers to design sustainable public policies that mitigate risks and promote social and economic development in Ecuador.

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