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4th IFAC Workshop on Navigation, Guidance and Control of Underwater Vehicles, NGCUV 2015

作者： Carrera, Arnau Palomeras, Narcís Hurtós, Natàlia Carreras, Marc Computer Vision and Robotics Institute Centre d'Investigació en Robòtica Submarina Universitat de Girona Girona17003 Spain

Recent efforts in the field of intervention-autonomous underwater vehicles (I-AUVs) have started to show promising results in simple manipulation tasks. However, there is still a long way to go to reach the complexity of the tasks carried out by ROV pilots. This paper proposes an intervention framework based on Learning by Demonstration (LbD) techniques in order to easily acquire the manipulation skills of a pilot and be able to reproduce similar tasks autonomously. We describe the learning algorithm as well as its interplay with the rest of the modules (navigation, perception, control) that take part in the proposed framework. We show results on a free-floating valve turning task, using Girona 500 AUV equipped with a manipulator, a passive gripper, a force/torque sensor, a camera and a haptic device to perform the demonstrations. Obtained results show the feasibility of the LbD algorithm to perform autonomous intervention even under the presence of perturbations. © 2015, IF AG (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Autonomous underwater vehicles

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Pose estimation for underwater vehicles using light beacons 4

Pose estimation for underwater vehicles using light beacons

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4th IFAC Workshop on Navigation, Guidance and Control of Underwater Vehicles, NGCUV 2015

作者： Gracias, Nuno Bosch, Josep Karim, Mohammad Ehsanul Computer Vision and Robotics Institute Centre d'Investigació en Robòtica Submarina Universitat de Girona Girona17003 Spain

This paper presents an approach for estimating the relative location and orientation between two or more underwater vehicles operating in tight formation. One of the vehicles is equipped with a camera of wide field of view. The other vehicle(s) are equipped with active light markers to enable the use of computer vision for pose estimation. The pose estimation addresses two scenarios, which are both important from the operational point of view. The first pertains to the availability of an acoustic communication channel which allows for exchanging attitude data and acoustic ranging, and use it in the pose estimation procedure. The second corresponds to the exclusive use of a set of 4 or more optical beacons with no acoustic information exchange, which is a capability that has not been yet proposed nor demonstrated in underwater vehicles. The contributions can be summarize as (1) a novel method of estimating the pose of an autonomous underwater vehicle using light beacons and other sensors when available, (2) an automated marker configuration analysis approach. Performance of the pose estimation approach is evaluated using synthetic and real data. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Autonomous underwater vehicles

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Lesion Detection in Digital Breast Tomosynthesis: Method, experiences and results of participating to the DBTex Challenge 16

Lesion Detection in Digital Breast Tomosynthesis: Method, ex...

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16th International Workshop on Breast Imaging, IWBI 2022

作者： Martí, Robert Del Campo, Pablo G. Vidal, Joel Cufí, Xavier Martí, Joan Chevalier, Margarita Freixenet, Jordi Computer Vision and Robotics Institute University of Girona Girona Spain Department of Radiology Rehabilitation and Fisiotherapy Universidad Complutense de Madrid Madrid Spain

ISBN: (纸本)9781510655843

The paper presents a framework for the detection of mass-like lesions in 3D digital breast tomosynthesis. It consists of several steps, including pre and post-processing, and a main detection block based on a Faster RCNN deep learning network. In addition to the framework, the paper describes different training steps to achieve better performance, including transfer learning using both mammographic and DBT data. The presented approach obtained third place in the recent DBT Lesion detection Challenge, DBTex, being the top approach without using an ensemble based method. © 2022 SPIE.

关键词： Deep learning

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Digital Mammography 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Joan Martí Arnau Oliver Jordi Freixenet Robert Martí

ISBN: (数字)9783642136665

ISBN: (纸本)9783642136658

This volume of Springer’s Lecture Notes in computer Science series comprises th the scienti?c proceedings of the 10 International Workshop on Digital Mammography (IWDM), which was held June 16–18, 2010 in Girona, Cata- nia. The IWDMmeetingstraditionallybringtogetheradiversesetofresearchers (physicists, mathematicians, computer scientists, engineers), clinicians (radi- ogists, surgeons) and representatives of industry, who are jointly committed to developing technology, not just for its own sake, but to support clinicians in the early detection and subsequent patient management of breast cancer. The IWDM conference series was initiated at a 1993 meeting of the SPIE Medical Imaging Symposium in San Jose, CA, with subsequent meetings hosted every two yearsbyresearchersaroundthe world. Former workshopswereheld in York, England (1994), Chicago, IL USA (1996), Nijmegen, The Netherlands (1998), Toronto, Canada (2000), Bremen, Germany (2002), Durham, NC, USA (2004), Manchester, UK (2006) and Tucson, AZ USA (2008). Each of these scienti?c events was combined with very successful and focused industrial and research exhibits, which demonstrated the milestones of digital mammography over the years. A total number of 141 paper submissions from 21 countries were received. Each of these four-page abstract submissions was reviewed in a blind process by at least two members of the Scienti?c Committee, which led to a ?nal selection of 46 oral presentations and 57 posters during the two and one-half days of scienti?c sessions.

关键词： Image Processing and computer vision Health Informatics Imaging / Radiology Information Storage and Retrieval Pattern Recognition Computational Biology/Bioinformatics

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Simultaneous pectoral muscle and nipple location in MLO mammograms, considering image quality assumptions 16

Simultaneous pectoral muscle and nipple location in MLO mamm...

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16th International Workshop on Breast Imaging, IWBI 2022

作者： Garcí, E. Martí, R. Martí, J. Del Riego, J. Aynes, C. Oliver, A. Diaza, O. Dep. of Mathematics and Computer Science University of Barcelona Spain Institute of Computer Vision and Robotics University of Girona Spain Universitat Autónoma de Barcelona Sabadell Spain

ISBN: (纸本)9781510655843

Feature-based registration algorithms can be used to establish spatial correspondence between two image. Therefore, anatomical landmarks such as the breast boundary, pectoral muscle, nipple, duct and vessels need to be considered. The aim of this paper is to introduce a new approach which combine the pectoral muscle segmentation and nipple location, considering mammography quality assumptions. Pectoral muscle is initialized as a straight line from the top of the image to the nipple level. Afterwards, both pectoral muscle boundary and nipple position are optimized using an iterative approach. The results show that the nipple is localized on the contour of the corresponding area (error smaller than 10 mm) while the Dice's coefficient of the pectoral muscle segmentation is equal to 0.84 ± 0.12 using a straight line which is improved using a Chan-Vese active contour approach, reaching 0.87 ± 0.13. Our algorithm is easily generalized and portable to a different mammographic system since it barely depends on images statistics -i.e. pixel intensity values-, and is just based on geometrical considerations. © 2022 SPIE.

关键词： Location

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Supervised local subspace learning for continuous head pose estimation

Supervised local subspace learning for continuous head pose ...

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作者： Huang, Dong Storer, Markus De La Torre, Fernando Bischof, Horst Robotics Institute Carnegie Mellon University United States Institute for Computer Graphics and Vision Graz University of Technology Austria University of Electronic Science and Technology of China China

ISBN: (纸本)9781457703942

Head pose estimation from images has recently attracted much attention in computer vision due to its diverse applications in face recognition, driver monitoring and human computer interaction. Most successful approaches to head pose estimation formulate the problem as a nonlinear regression between image features and continuous 3D angles (i.e. yaw, pitch and roll). However, regression-like methods suffer from three main drawbacks: (1) They typically lack generalization and overfit when trained using a few samples. (2) They fail to get reliable estimates over some regions of the output space (angles) when the training set is not uniformly sampled. For instance, if the training data contains under-sampled areas for some angles. (3) They are not robust to image noise or occlusion. To address these problems, this paper presents Supervised Local Subspace Learning (SL2), a method that learns a local linear model from a sparse and non-uniformly sampled training set. SL2 learns a mixture of local tangent spaces that is robust to under-sampled regions, and due to its regularization properties it is also robust to over-fitting. Moreover, because SL2 is a generative model, it can deal with image noise. Experimental results on the CMU Multi-PIE and BU-3DFE database show the effectiveness of our approach in terms of accuracy and computational complexity. © 2011 IEEE.

关键词： Face recognition

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Video Face Clustering with Self-Supervised Representation Learning

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2020年第2期2卷 145-157页

作者： Sharma, Vivek Tapaswi, Makarand Saquib Sarfraz, M. Stiefelhagen, Rainer Department of Informatics Institute for Anthropomatics and Robotics Computer Vision for Human-Computer Interaction Lab Karlsruhe Institute of Technology Karlsruhe76131 Germany University of Toronto TorontoONM5S 3G4 Canada Massachusetts Institute of Technology CambridgeMA02139 United States Vector Institute TorontoON Canada Inria Paris75012 France

Characters are a key component of understanding the story conveyed in TV series and movies. With the rise of advanced deep face models, identifying face images may seem like a solved problem. However, as face detectors get better, clustering and identification need to be revisited to address increasing diversity in facial appearance. In this paper, we propose unsupervised methods for feature refinement with application to video face clustering. Our emphasis is on distilling the essential information, identity, from the representations obtained using deep pre-trained face networks. We propose a self-supervised Siamese network that can be trained without the need for video/track based supervision, that can also be applied to image collections. We evaluate our methods on three video face clustering datasets. Thorough experiments including generalization studies show that our methods outperform current state-of-the-art methods on all datasets. The datasets and code are available at https://***/vivoutlaw/SSIAM. © 2019 IEEE.

关键词： computer vision

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Dichromatic-based color constancy using dichromatic slope and dichromatic line space

Dichromatic-based color constancy using dichromatic slope an...

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IEEE International Conference on Image Processing 2005, ICIP 2005

作者： Yoon, Kuk-Jin Choi, Yoo Jin Kweon, In-So Robotics and Computer Vision Lab. Dept. of EECS KAIST Korea Republic of Mobile Multimedia Lab. LG Electronics Institute of Technology

ISBN: (纸本)0780391349

In this paper, we present a new dichromatic-based method for estimating illuminant chromaticity using a single image. In the proposed method, specular pixels are roughly extracted from an input image and projected into the three-dimensional dichromatic line space according to their image chromaticities and dichromatic slopes. Reliable dichromatic lines are then obtained by detecting local maxima in the dichromatic line space. Illuminant chromaticity is finally estimated by finding the intersections of the resulting dichromatic lines. The proposed method has some advantages. The proposed method can deal with highly textured images since it does not require precise color segmentation. In addition, the proposed method does not suffer from nearly parallel dichromatic lines and it can estimate artificial illuminant chromaticity. © 2005 IEEE.

关键词： Image analysis

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Joint multi-view texture super-resolution and intrinsic decomposition 30

Joint multi-view texture super-resolution and intrinsic deco...

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30th British Machine vision Conference, BMVC 2019

作者： Tsiminaki, Vagia Dong, Wei Oswald, Martin R. Pollefeys, Marc Computer Vision and Geometry group ETH Zurich Zurich Switzerland Robotics Institute Carnegie Mellon University Pittsburgh United States Microsoft United States

We aim to recover a high resolution texture representation of objects observed from multiple view points under varying lighting conditions. For many applications the lighting conditions need to be changed and thus require a texture decomposition into shading and albedo components. Both texture super-resolution and intrinsic texture decomposition have been separately studied in the literature. Yet, no method has investigated how these methods can be combined. We propose a framework for joint texture map superresolution and intrinsic decomposition. To this end, we define shading and albedo maps of the 3D object as the intrinsic properties of its texture and introduce an image formation model to describe the physics of the image generation. Our approach accounts for surface geometry and camera calibation errors and is also applicable to spatio-temporal sequences. Our method achieves state-of-the-art results on a variety of datasets. © 2019. The copyright of this document resides with its authors.

关键词： Textures

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Acute and sub-acute stroke lesion segmentation from multimodal MRI

arXiv

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arXiv 2018年

作者： Clèrigues, Albert Valverde, Sergi Bernal, Jose Freixenet, Jordi Oliver, Arnau Lladó, Xavier Institute of Computer Vision and Robotics University of Girona Spain

Background and objective. Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctorsmake better informed time-critical treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered the gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment. Methods. We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. We pre-process the data to facilitate learning features based on the symmetry of brain hemispheres. The issue of class imbalance is tackled using small patches with a balanced training patch sampling strategy and a dynamically weighted loss function. Moreover, a combination of whole patch predictions, using a UNet based CNN architecture, and high degree of overlapping patches reduces the need for additional post-processing. Results. The proposed method is evaluated using two public datasets from the 2015 Ischemic Stroke Lesion Segmentation challenge (ISLES 2015). These involve the tasks of sub-acute stroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES) from multiple diffusion, perfusion and anatomical MRI modalities. The performance is compared against state-of-the-art methods with a blind online testing set evaluation on each of the challenges. At the time of submitting this manuscript, our approach is the first method in the online rankings for the SISS (DSC=0.59 ± 0.31) and SPES sub-tasks (DSC=0.84 ± 0.10). When compared with the rest of submitted strategies, we achieve top rank performance with a lower Hausdorff distance. Conclusions. Better segmentation results are obtained by leveraging the anatomy and pathophysiology of acute stroke lesions and using a combined approach to minimize the effects of clas

关键词： Magnetic resonance imaging

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