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2nd International Conference on computer and Information Sciences, ICCIS 2020

作者： Waheeb, Waddah Shah, Habib Jabreel, Mohammed Puig, Domenec School of Computing Asia Pacific University of Technology Innovation Kuala Lumpur Bukit Jalil57000 Malaysia College of Computer Science King Khalid University Department of Computer Science Abha62529 Saudi Arabia IRCV - Intelligent Robotics and Computer Vision Group Universitat Rovira i Virgili Tarragona43007 Spain

ISBN: (纸本)9781728154671

This paper presents a comparative study between statistical and machine learning methods in forecasting Bitcoin's closing prices. Thirteen forecasting methods namely average, naive, drift, auto-regressive integrated moving-average, simple exponential smoothing (SES), Holt, and damped exponential smoothing, the average of SES, Holt and damped methods, exponential smoothing (ETS), bagged ETS, Theta, multilayer perceptron, and extreme learning machines (ELM) were used to forecast the closing prices for the next 14 days. The findings of this study are three folds. First, there are seven forecasting methods outperformed the naive method namely MLP, ELM, damped exponential smoothing, simple exponential smoothing, Theta, ETS, and ARIMA. Second, MLP and ELM showed better forecasting accuracy on both validation and out-of-sample data among the forecasting methods used in this study. Third, the size of the training data is essential factor that should be considered when training forecasting methods. © 2020 IEEE.

关键词： Bitcoin

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On adapting the tensor voting framework to robust color image denoising

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13th International Conference on computer Analysis of Images and Patterns, CAIP 2009

作者： Moreno, Rodrigo Garcia, Miguel Angel Puig, Domenec Julià, Carme Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Av. Països Catalans 26 Tarragona 43007 Spain Department of Informatics Engineering Autonomous University of Madrid Cra. Colmenar Viejo Km 15 Madrid 28049 Spain

ISBN: (纸本)3642037666

This paper presents an adaptation of the tensor voting framework for color image denoising, while preserving edges. Tensors are used in order to encode the CIELAB color channels, the uniformity and the edginess of image pixels. A specific voting process is proposed in order to propagate color from a pixel to its neighbors by considering the distance between pixels, the perceptual color difference (by using an optimized version of CIEDE2000), a uniformity measurement and the likelihood of the pixels being impulse noise. The original colors are corrected with those encoded by the tensors obtained after the voting process. Peak to noise ratios and visual inspection show that the proposed methodology has a better performance than state-of-the-art techniques. © 2009 Springer Berlin Heidelberg.

关键词： Pixels

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On adapting pixel-based classification to unsupervised texture segmentation

On adapting pixel-based classification to unsupervised textu...

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2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Melendez, Jaime Puig, Domenec Angel Garcia, Miguel Intelligent Robotics and Computer Vision Group Rovira I Virgili University Dept. of Computer Science and Mathematics Av. Paisos Catalans 26 43007 Tarragona Spain Autonomous University of Madrid Dept. of Informatics Engineering Francisco Tomas y Valiente 11 28049 Madrid Spain

ISBN: (纸本)9780769541099

An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image segmentation based on texture is proposed. It takes advantage of a supervised pixel-based texture classifier trained with feature vectors associated with a set of texture patterns initially extracted through a clustering algorithm. Therefore, the final segmentation is achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multi-sized evaluation windows following a top-down approach are applied during pixel classification in order to improve accuracy. The proposed technique has been experimentally validated on MeasTex, VisTex and Brodatz compositions, as well as on complex ground and aerial outdoor images. Comparisons with state-of-the-art unsupervised texture segmenters are also provided. © 2010 IEEE.

关键词： Clustering algorithms

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Robust color image segmentation through tensor voting

Robust color image segmentation through tensor voting

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International Conference on Pattern Recognition

作者： Moreno, Rodrigo Garcia, Miguel Angel Puig, Domenec Rovira I Virgili University Intelligent Robotics and Computer Vision Group Dept. of Computer Science and Mathematics Av. Països Catalans 26 43007 Tarragona Spain Autonomous University of Madrid Dept. of Informatics Engineering Francisco Tomas y Valiente 11 28049 Madrid Spain

ISBN: (纸本)9780769541099

This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptation of tensor voting to both image denoising and robust edge detection is applied. Second, pixels in the filtered image are classified into likely-homogeneous and likely-inhomogeneous by means of the edginess maps generated in the first step. Third, the likely-homogeneous pixels are segmented through an efficient graph-based segmenter. Finally, a modified version of the same graph-based segmenter is applied to the likely-inhomogeneous pixels in order to obtain the final segmentation. Experiments show that the proposed algorithm has a better performance than the state-of-the-art. © 2010 IEEE.

关键词： Pixels

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Actuator fault detection in autonomous helicopters

Actuator fault detection in autonomous helicopters

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IFAC/EURON Symposium on intelligent Autonomous Vehicles

作者： Heredia, G. Remuß, Volker Ollero, A. Mahtani, R. Musial, Marek Robotics Computer Vision and Intelligent Control Group Dept. Ingeniería de Sistemas y Automática University of Seville Camino de los Descubrimientos s/n Seville41092 Spain Technical University of Berlin Einsteinufer 17 BerlinD-10587 Germany

This paper presents an actuator fault detection and diagnosis system for autonomous helicopters. The system has been tested with the MARVIN autonomous helicopter. Fault detection is accomplished by evaluating any significant change in the behaviour of the vehicle with respect to the fault-free behaviour, which is estimated by using an observer. The effectiveness of the proposed approach is demonstrated by means of MARVIN experimental results and simulations. © 2004 IFAC

关键词： Unmanned aerial vehicles (UAV)

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Conditional affordance learning for driving in urban environments

arXiv

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arXiv 2018年

作者： Sauer, Axel Savinov, Nikolay Geiger, Andreas Computer Vision and Geometry Group ETH Zürich Chair of Robotics Science and System Intelligence Technical University of Munich Autonomous Vision Group MPI for Intelligent Systems and University of Tübingen

Most existing approaches to autonomous driving fall into one of two categories: Modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. A recently proposed third paradigm, direct perception, aims to combine the advantages of both by using a neural network to learn appropriate low-dimensional intermediate representations. However, existing direct perception approaches are restricted to simple highway situations, lacking the ability to navigate intersections, stop at traffic lights or respect speed limits. In this work, we propose a direct perception approach which maps video input to intermediate representations suitable for autonomous navigation in complex urban environments given high-level directional inputs. Compared to state-of-the-art reinforcement and conditional imitation learning approaches, we achieve an improvement of up to 68 % in goal-directed navigation on the challenging CARLA simulation benchmark. In addition, our approach is the first to handle traffic lights and speed signs by using image-level labels only, as well as smooth car-following, resulting in a significant reduction of traffic accidents in simulation. Copyright © 2018, The Authors. All rights reserved.

关键词： Autonomous vehicles

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FIRE DETECTION USING AUTONOMOUS AERIAL VEHICLES WITH INFRARED AND VISUAL CAMERAS

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IFAC Proceedings Volumes 2005年第1期38卷 660-665页

作者： J. Ramiro Martínez-de Dios Luis Merino Aníbal Ollero Robotics Computer Vision and Intelligent Control Group. University of Seville. Escuela Superior de Ingenieros Camino de los Descubrimientos s/n 41092 Seville Spain

The paper presents a system for automatic fire detection based on the use of autonomous aerial vehicles. Particularly, the application of a helicopter with infrared and visual cameras is described. The paper presents the techniques used for fire segmentation in visual and infrared cameras, and the procedures to fuse the data obtained from both of them. Furthermore the paper presents the techniques for automatic geolocation of the detected fire alarms. Experimental results are shown.

关键词： data fusion infrared detectors autonomous vehicles helicopters

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Gabor-based texture classification through efficient prototype selection via normalized cut

Gabor-based texture classification through efficient prototy...

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IEEE International Conference on Image Processing

作者： Jaime Melendez Domenec Puig Miguel Angel Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Spain Department of Informatics Engineering Autonomous University of Madrid Spain

This paper presents a new efficient technique for supervised pixel-based texture classification. The proposed scheme first performs a selection process that automatically determines a subset of prototypes that characterize each texture class based on the outcome of a multichannel Gabor wavelet filter bank. Then, every image pixel is classified into one of the given texture classes by using a K-NN classifier fed with the prototypes determined previously. The proposed technique is compared to previous texture classifiers by using both Brodatz and real outdoor textured images.

关键词： Prototypes Gabor filters Pixel Feature extraction Filter bank Testing intelligent robots Image texture analysis Pattern recognition Image recognition

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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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Reducing the complexity of geometric selective disassembly

Reducing the complexity of geometric selective disassembly

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IEEE International Workshop on intelligent Robots and Systems (IROS)

作者： M.A. Garcia A. Larre B. Lopez A. Oller Department of Computer Science and Mathematics Intelligent Robotics and Computer Vision Group Tarragona Spain Department of Electrical Electronic and Automation Engineering Rovira i Virgili University Tarragona Spain

This paper presents an efficient technique for determining a low-cost disassembly sequence suitable to extract a subset of s components from an assembly containing n components, e of which are exterior (e/spl Lt/n). The most efficient solution to this so-called geometric selective disassembly problem is the wave propagation algorithm, which is reported to have a computational complexity of O(sn/sup 2/). Instead, the complexity of the proposed algorithm is O(enlogn) when s/spl Lt/n, and O(sn) when s/spl sime/n. Experimental results with synthetic 3D assemblies are presented.

关键词： Assembly intelligent robots robotics and automation Computational complexity Recycling Fasteners Polynomials Costs Robot vision systems computer vision

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