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Author |
Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Using the Average Landmark Vector Method for Robot Homing |
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2007 |
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Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA |
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163 |
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331–338 |
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978–1–58603–798–7 |
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CCIA’07 |
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RV;ADAS |
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Admin @ si @ GRL2007 |
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899 |
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Author |
Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Comparing Combinations of Feature Regions for Panoramic VSLAM |
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Conference Article |
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2007 |
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4th International Conference on Informatics in Control, Automation and Robotics |
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292–297 |
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Angers (France) |
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ICINCO |
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RV;ADAS |
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no |
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Admin @ si @ RLA2007 |
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900 |
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Author |
Arnau Ramisa; Adriana Tapus; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors |
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Conference Article |
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2008 |
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IEEE International Conference on Robotics and Automation, |
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538–543 |
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Pasadena; CA; USA |
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ICRA |
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RV;ADAS |
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Admin @ si @ RTL2008 |
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1144 |
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Author |
Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras |
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Title |
The IIIA30 MObile Robot Object Recognition Datset |
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Conference Article |
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2011 |
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11th Portuguese Robotics Open |
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Object perception is a key feature in order to make mobile robots able to perform high-level tasks. However, research aimed at addressing the constraints and limitations encountered in a mobile robotics scenario, like low image resolution, motion blur or tight computational constraints, is still very scarce. In order to facilitate future research in this direction, in this work we present an object detection and recognition dataset acquired using a mobile robotic platform. As a baseline for the dataset, we evaluated the cascade of weak classifiers object detection method from Viola and Jones. |
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Lisboa |
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Robotica |
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RV;ADAS |
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Admin @ si @ RAV2011 |
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1777 |
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Author |
Cristina Cañero; Petia Radeva; Oriol Pujol; Ricardo Toledo; Debora Gil; J. Saludes; Juan J. Villanueva; B. Garcia del Blanco; Josefina Mauri; Eduard Fernandez-Nofrerias; J.A. Gomez-Hospital; E. Iraculis; J. Comin; C. Quiles; F. Jara; A. Cequier; E.Esplugas |
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Three-dimensional reconstruction and quantification of the coronary tree using intravascular ultrasound images |
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1999 |
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Proceedings of International Conference on Computer in Cardiology (CIC´99) |
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In this paper we propose a new Computer Vision technique to reconstruct the vascular wall in space using a deformable model-based technique and compounding methods, based in biplane angiography and intravascular ultrasound data jicsion. It is also proposed a generalpurpose three-dimensional guided interpolation method. The three dimensional centerline of the vessel is reconstructed from geometrically corrected biplane angiographies using automatic segmentation methods and snakes. The IVUS image planes are located in the threedimensional space and correctly oriented. A led interpolation method based in B-SurJaces and snakes isused to fill the gaps among image planes |
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CINC99 |
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MILAB;RV;IAM;ADAS;HuPBA |
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IAM @ iam @ CRP1999b |
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1492 |
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Author |
Petia Radeva; Joan Serrat; Enric Marti |
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Title |
A snake for model-based segmentation |
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Conference Article |
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Year |
1995 |
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Proc. Conf. Fifth Int Computer Vision |
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816-821 |
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snakes; elastic matching; model-based segmenta tion |
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Despite the promising results of numerous applications, the hitherto proposed snake techniques share some common problems: snake attraction by spurious edge points, snake degeneration (shrinking and attening), convergence and stability of the deformation process, snake initialization and local determination of the parameters of elasticity. We argue here that these problems can be solved only when all the snake aspects are considered. The snakes proposed here implement a new potential eld and external force in order to provide a deformation convergence, attraction by both near and far edges as well as snake behaviour selective according to the edge orientation. Furthermore, we conclude that in the case of model-based seg mentation, the internal force should include structural information about the expected snake shape. Experiments using this kind of snakes for segmenting bones in complex hand radiographs show a signi cant improvement. |
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MILAB;ADAS;IAM |
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IAM @ iam @ RSM1995 |
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1634 |
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Cristina Cañero; Petia Radeva; Oriol Pujol; Ricardo Toledo; Debora Gil; J. Saludes; Juan J. Villanueva; B. Garcia del Blanco; J. Mauri; E. Fernandez-Nofrerias; J.A. Gomez-Hospital; E. Iraculis; J. Comin; C. Quiles; F. Jara; A. Cequier; E. Esplugas |
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Title |
Optimal Stent Implantation: Three-dimensional Evaluation of the Mutual Position of Stent and Vessel via Intracoronary Ecography |
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Conference Article |
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1999 |
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Proceedings of International Conference on Computer in Cardiology (CIC´99) |
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We present a new automatic technique to visualize and quantify the mutual position between the stent and the vessel wall by considering their three-dimensional reconstruction. Two deformable generalized cylinders adapt to the image features in all IVUS planes corresponding to the vessel wall and the stent in order to reconstruct the boundaries of the stent and the vessel in space. The image features that characterize the stent and the vessel wall are determined in terms of edge and ridge image detectors taking into account the gray level of the image pixels. We show that the 30 reconstruction by deformable cylinders is accurate and robust due to the spatial data coherence in the considered volumetric IVUS image. The main clinic utility of the stent and vessel reconstruction by deformable’ cylinders consists of its possibility to visualize and to assess the optimal stent introduction. |
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MILAB; RV; IAM; ADAS; HuPBA |
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IAM @ iam @ CRP1999a |
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1491 |
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Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio |
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Title |
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation |
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Conference Article |
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2017 |
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IEEE Conference on Computer Vision and Pattern Recognition Workshops |
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Semantic Segmentation |
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State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.
Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train.
In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. |
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Honolulu; USA; July 2017 |
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CVPRW |
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MILAB; ADAS; 600.076; 600.085; 601.281 |
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ADAS @ adas @ JDV2016 |
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2866 |
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Xialei Liu; Marc Masana; Luis Herranz; Joost Van de Weijer; Antonio Lopez; Andrew Bagdanov |
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Title |
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting |
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2018 |
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24th International Conference on Pattern Recognition |
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2262-2268 |
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In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of
a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and
Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting. |
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LAMP; ADAS; 601.305; 601.109; 600.124; 600.106; 602.200; 600.120; 600.118 |
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Admin @ si @ LMH2018 |
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3160 |
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Marc Masana; Idoia Ruiz; Joan Serrat; Joost Van de Weijer; Antonio Lopez |
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Title |
Metric Learning for Novelty and Anomaly Detection |
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2018 |
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29th British Machine Vision Conference |
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When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We divide out-of-distribution detection between novelty detection ---images of classes which are not in the training set but are related to those---, and anomaly detection ---images with classes which are unrelated to the training set. By related we mean they contain the same type of objects, like digits in MNIST and SVHN. Most existing work has focused on anomaly detection, and has addressed this problem considering networks trained with the cross-entropy loss. Differently from them, we propose to use metric learning which does not have the drawback of the softmax layer (inherent to cross-entropy methods), which forces the network to divide its prediction power over the learned classes. We perform extensive experiments and evaluate both novelty and anomaly detection, even in a relevant application such as traffic sign recognition, obtaining comparable or better results than previous works. |
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Newcastle; uk; September 2018 |
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BMVC |
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LAMP; ADAS; 601.305; 600.124; 600.106; 602.200; 600.120; 600.118 |
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Admin @ si @ MRS2018 |
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3156 |
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