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Augmented Cognition via Brainwave Entrainment in Virtual Reality: An Open, Integrated Brain Augmentation in a Neuroscience System Approach

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Augmented Human Research 2017年第1期2卷 1-14页

作者： Emanuele Argento George Papagiannakis Eva Baka Michail Maniadakis Panos Trahanias Michael Sfakianakis Ioannis Nestoros Computational Vision and Robotics Laboratory Institute of Computer Science Foundation for Research and Technology Heraklion Greece Department of Computer Science University of Crete Heraklion Greece Department of Medicine University of Greece Heraklion Greece Department of Business Administration University of Piraeus Piraeus Greece Synchronal Amphiaraia University of Crete Snip-off Company Athens Greece

Building on augmented cognition theory and technology, our novel contribution in this work enables accelerated, certain brain functions related to task performance as well as their enhancement. We integrated in an open-source framework, latest immersive virtual reality (VR) head-mounted displays, with the Emotiv EPOC EEG headset in an open neuro- and biofeedback system for cognitive state detection and augmentation. Our novel methodology allows to significantly accelerate content presentation in immersive VR, while lowering brain frequency at alpha level—without losing content retention by the user. In our pilot experiments, we tested our innovative VR platform by presenting to N = 25 subjects a complex 3D maze test and different learning procedures for them on how to exit it. The subjects exposed to our VR-induced entrainment learning technology performed significantly better than those exposed to other “classical” learning procedures. In particular, cognitive task performance augmentation was measured for: learning time, complex navigational skills and decision-making abilities, orientation ability.

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Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots

arXiv

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arXiv 2017年

作者： Turan, Mehmet Almalioglu, Yasin Araujo, Helder Konukoglu, Ender Sitti, Metin Physical Intelligence Department Max Planck Institute for Intelligent Systems Germany Computer Engineering Department Bogazici Univesity Turkey Institute for Systems and Robotics Universidade de Coimbra Portugal Computer Vision Laboratory Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories. Copyright © 2017, The Authors. All rights reserved.

关键词： vision

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A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot

arXiv

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arXiv 2017年

作者： Turan, Mehmet Pilavci, Yusuf Yigit Jamiruddin, Redhwan Araujo, Helder Konukoglu, Ender Sitti, Metin Physical Intelligence Department Max-Planck Institute Stuttgart Germany Computer Vision Laboratory Eth Zentrum Zurich Switzerland Biomedical Engineering Department Martin-Luther Univeristy Halle-Wittenberg Halle Germany Robotics Institute Coimbro University

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is emerging as a novel, minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Since the development of this technology, medical device companies and many research groups have made substantial progress in converting passive capsule endoscopes to robotic active capsule endoscopes with most of the functionality of current active flexible endoscopes. However, robotic capsule endoscopy still has some challenges. In particular, the use of such devices to generate a precise three-dimensional (3D) mapping of the entire inner organ remains an unsolved problem. Such global 3D maps of inner organs would help doctors to detect the location and size of diseased areas more accurately and intuitively, thus permitting more reliable diagnoses. To our knowledge, this paper presents the first complete pipeline for a complete 3D visual map reconstruction of the stomach. The proposed pipeline is modular and includes a preprocessing module, an image registration module, and a final shape-from-shading-based 3D reconstruction module;the 3D map is primarily generated by a combination of image stitching and shape-from-shading techniques, and is updated in a frame-by-frame iterative fashion via capsule motion inside the stomach. A comprehensive quantitative analysis of the proposed 3D reconstruction method is performed using an esophagus gastro duodenoscopy simulator, three different endoscopic cameras, and a 3D optical scanner. Copyright © 2017, The Authors. All rights reserved.

关键词： Endoscopy

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Mobile beacon control algorithm that ensures observability in single range navigation

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IFAC-PapersOnLine 2016年第23期49卷 48-53页

作者： Mandi, Filip Mikovi, Nikola Palomeras, Narcís Carreras, Marc Vallicrosa, Guillem University of Zagreb Faculty of Electrical Engineering and Computing LABUST - Laboratory for Underwater Systems and Technologies Unska 3 Zagreb Croatia Computer Vision and Robotics Institute Universitat de Girona Parc Cientfic i Tecnolgic UdG Girona17003 Spain

Mobile beacon vehicles are used as navigational aid for autonomous underwater vehicles when performing navigation using single range measurements. They remove the constraints imposed on the underwater vehicle trajectory by executing trajectory that provides persistent range measurements. In this paper, control algorithm for beacon vehicle which ensures observability of the underwater vehicle's navigation filter is presented. It is characterized by small communication overhead, low computational complexity and it is deployable on both fully actuated and underactuated vehicles. The algorithm was tested in real-life environment and the acquired experimental results show that despite the presence of uncertainties and communication delays, the algorithm accomplishes its task. © 2016

关键词： Marine navigation Autonomous underwater vehicles Kalman filters Navigation Observability Autonomous Vehicles Communication delays Communication overheads Low computational complexity Marine systems Navigation filters Underactuated vehicles Underwater vehicles

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Detection and localization with multi-scale models

Detection and localization with multi-scale models

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International Conference on Pattern Recognition

作者： Eshed Ohn-Bar Mohan M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego

ISBN: (纸本)9781509048489

Object detection and localization in images involve a multi-scale reasoning process. First, responses of object detectors are known to vary with image scale. Second, contextual relationships on a part-level, object-level, and scene-level appear at different scales of the image. This paper studies efficient modeling of these two components by training multi-scale template models. The input to the proposed algorithm involves image features computed at varying image scales, hence operating on volumes in the feature pyramid. The approach generalizes single-scale, local-region detection approaches (e.g. sliding window or region proposals), jointly learning detection and localization cues. Extending the common single-scale detection to a multi-scale volume allows learning scale-specific models as well as analyzing the importance of contextual information at different scales. Experimental analysis on the PASCAL VOC dataset shows the method to considerably improve both detection and localization performance for different type of features, histogram of oriented gradients and deep convolutional neural network features.

关键词： Feature extraction Training Automobiles Pipelines Computational modeling Analytical models Image resolution

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What makes an on-road object important?

What makes an on-road object important?

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International Conference on Pattern Recognition

作者： Eshed Ohn-Bar Mohan M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego

ISBN: (纸本)9781509048489

Human drivers continuously attend to important scene elements in order to safely and smoothly navigate in intricate environments and under uncertainty. This paper develops a human-centric framework for object recognition by analyzing a notion of object importance, as measured in a spatio-temporal context of driving a vehicle. Given a video, a main research question in this paper is - which of the surrounding agents are most important? The answer inherently requires complex reasoning over the current driving task, object properties, scene context, intent, and possible future actions. Therefore, we find that various spatio-temporal cues are relevant for the importance classification task. Furthermore, we demonstrate the usefulness of the importance annotations in evaluating vision algorithms (specifically, for the task of object detection) in an application where trust in automation is imperative and errors are costly. Finally, we show that importance-guided training of object detection models results in improved detection performance of surrounding objects of higher importance. Hence, such models may be better suited for use in representing safety-critical situations, predicting surrounding agents' intentions, and in human-robot interactivity. The dataset and code will be made publicly available.

关键词： Context Predictive models Navigation Training Computational modeling Three-dimensional displays Visualization

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Thermal Image Enhancement using Convolutional Neural Network

Thermal Image Enhancement using Convolutional Neural Network

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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yukyung Choi Namil Kim Soonmin Hwang In So Kweon Dep. of EE Robotics and Computer Vision (RCV) Laboratory Daejeon Korea

With the advent of commodity autonomous mobiles, it is becoming increasingly prevalent to recognize under extreme conditions such as night, erratic illumination conditions. This need has caused the approaches using multi-modal sensors, which could be complementary to each other. The choice for the thermal camera provides a rich source of temperature information, less affected by changing illumination or background clutters. However, existing thermal cameras have a relatively smaller resolution than RGB cameras that has trouble for fully utilizing the information in recognition tasks. To mitigate this, we aim to enhance the low-resolution thermal image according to the extensive analysis of existing approaches. To this end, we introduce Thermal Image Enhancement using Convolutional Neural Network (CNN), called in TEN, which directly learns an end-to-end mapping a single low resolution image to the desired high resolution image. In addition, we examine various image domains to find the best representative of the thermal enhancement. Overall, we propose the first thermal image enhancement method based on CNN guided on RGB data. We provide extensive experiments designed to evaluate the quality of image and the performance of several object recognition tasks such as pedestrian detection, visual odometry, and image registration.

关键词： Image enhancement Cameras Image resolution Training Feature extraction Image reconstruction Robot vision systems

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Dynamic perimeter surveillance with a team of robots

Dynamic perimeter surveillance with a team of robots

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IEEE International Conference on robotics and Automation (ICRA)

作者： David Saldaña Reza Javanmard Alitappeh Luciano C. A. Pimenta Renato Assunção Mario F. M. Campos Universidade Federal de Minas Gerais (UFMG) MG Brazil Computer Science Department Computer Vision and Robotics Laboratory (VeRLab)

In this paper, we propose a motion planning method to escort a set of agents from one place to a goal in an environment with obstacles. The agents are distributed in a finite area, with a time-varying perimeter, in which we put multiple robots to patrol around it with a desired velocity. Our proposal is composed of two parts. The first one generates a plan to move and deform the perimeter smoothly, and as a result, we obtain a twice differentiable boundary function. The second part uses the boundary function to compute a trajectory for each robot, we obtain each resultant trajectory by first solving a differential equation. After receiving the boundary function, the robots do not need to communicate among themselves until they finish their trajectories. We validate our proposal with simulations and experiments with actual robots.

关键词： Shape Planning Trajectory Robot kinematics Dynamics Surveillance

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Real-time monocular obstacle avoidance using underwater dark channel prior

Real-time monocular obstacle avoidance using underwater dark...

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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016

作者： Drews, Paulo Hernández, Emili Elfes, Alberto Nascimento, Erickson R. Campos, Mario Autonomous Systems Laboratory Data61-CSIRO Brisbane Australia Computer Vision and Robotics Laboratory Dep. de Ciência da Computação Univ. Federal de Minas Gerais - UFMG Belo Horizonte Brazil NAUTEC Intelligent Robotics and Automation Group Univ. Federal do Rio Grande - FURG Rio Grande Brazil

ISBN: (纸本)9781509037629

In this paper we propose a new vision-based obstacle avoidance strategy using the Underwater Dark Channel Prior (UDCP) that can be applied to any Unmanned Underwater Vehicle (UUV) equipped with a simple monocular camera and minimal on-board processing capabilities. For each incoming image, our method first computes a relative depth map to estimate the obstacles nearby. Then, the map is segmented and the most promising Region of Interest (RoI) is identified. Finally, an escape direction is computed within the RoI and a control action is performed accordingly to avoid the obstacles. We tested our approach on a video sequence in a natural environment and compared it against a stateofthe-art method showing better performance, specially in light changing conditions. We also provide online results on a low-cost Remotely Operated Vehicle (ROV) in a controlled environment. © 2016 IEEE.

关键词： Remotely operated vehicles

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A real-time supervised learning approach for sky segmentation onboard unmanned aerial vehicles

A real-time supervised learning approach for sky segmentatio...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Adrian Carrio Carlos Sampedro Changhong Fu Jean-François Collumeau Pascual Campoy Computer Vision Group Center for Automation and Robotics (UPM-CSIC) Madrid Spain School of Mechanical and Aerospace Engineering and ST Engineering-NTU Corp Laboratory Nanyang Technological University Singapore

vision-based sky segmentation and horizon line detection can be extremely useful to perform important tasks onboard Unmanned Aerial Vehicles (UAVs), such as pose estimation and collision avoidance. Most of the existing vision-based solutions use traditional image processing methods to identify the horizon line. This results in good overall accuracy and fast computation times. However, difficult environmental conditions such as a foggy or cloudy skies hinder correct sky segmentation. This paper proposes a solution for sky segmentation in RGB images using a supervised Machine Learning approach by first splitting the image into fixed-size patches, extracting and classifying color descriptors for each patch and performing a final post-processing stage to improve segmentation quality. A method for automatic horizon line detection is also proposed. The performance of our approach was evaluated on flight images captured onboard UAVs, achieving performance accuracies above 93% at real-time frame rates.

关键词： Image color analysis Image segmentation Aircraft Image edge detection Histograms Real-time systems Unmanned aerial vehicles

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