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Author Aura Hernandez-Sabate; Debora Gil; David Roche; Monica M. S. Matsumoto; Sergio S. Furuie edit   pdf
url  openurl
  Title Inferring the Performance of Medical Imaging Algorithms Type Conference Article
  Year 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 6854 Issue Pages 520-528  
  Keywords Validation, Statistical Inference, Medical Imaging Algorithms.  
  Abstract Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.		  
  Address Sevilla  
  Corporate Author Thesis  
  Publisher Springer-Verlag Berlin Heidelberg Place of Publication Berlin Editor Pedro Real; Daniel Diaz-Pernil; Helena Molina-Abril; Ainhoa Berciano; Walter Kropatsch  
  Language Summary Language Original Title  
  Series Editor Series Title L Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference (down) CAIP  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ HGR2011 Serial 1676  
Permanent link to this record
 
 
Author Naveen Onkarappa; Angel Sappa edit  doi
isbn  openurl
  Title Space Variant Representations for Mobile Platform Vision Applications Type Conference Article
  Year 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 6855 Issue II Pages 146-154  
  Keywords  
  Abstract The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow.  
  Address Seville, Spain  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-23677-8 Medium  
  Area Expedition Conference (down) CAIP  
  Notes ADAS Approved no  
  Call Number NaS2011; ADAS @ adas @ Serial 1686  
Permanent link to this record
 
 
Author Naveen Onkarappa; Angel Sappa edit  doi
isbn  openurl
  Title Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario Type Conference Article
  Year 2013 Publication 15th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 8048 Issue Pages 483-490  
  Keywords Optical flow; regularization; Driver Assistance Systems; Performance Evaluation  
  Abstract Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).  
  Address York; UK; August 2013  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-40245-6 Medium  
  Area Expedition Conference (down) CAIP  
  Notes ADAS; 600.055; 601.215 Approved no  
  Call Number Admin @ si @ OnS2013b Serial 2244  
Permanent link to this record
 
 
Author Marcelo D. Pistarelli; Angel Sappa; Ricardo Toledo edit  doi
isbn  openurl
  Title Multispectral Stereo Image Correspondence Type Conference Article
  Year 2013 Publication 15th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 8048 Issue Pages 217-224  
  Keywords  
  Abstract This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach.  
  Address York; uk; August 2013  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-40245-6 Medium  
  Area Expedition Conference (down) CAIP  
  Notes ADAS; 600.055 Approved no  
  Call Number Admin @ si @ PST2013 Serial 2561  
Permanent link to this record
 
 
Author Jaume Amores; N. Sebe; Petia Radeva edit  openurl
  Title Class-Specific Binaryy Correlograms for Object Recognition Type Conference Article
  Year 2007 Publication British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Warwick (UK)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference (down) BMVC’07  
  Notes ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ ASR2007a Serial 923  
Permanent link to this record
 
 
Author German Ros; J. Guerrero; Angel Sappa; Daniel Ponsa; Antonio Lopez edit   pdf
openurl 
  Title Fast and Robust l1-averaging-based Pose Estimation for Driving Scenarios Type Conference Article
  Year 2013 Publication 24th British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords SLAM  
  Abstract Robust visual pose estimation is at the core of many computer vision applications, being fundamental for Visual SLAM and Visual Odometry problems. During the last decades, many approaches have been proposed to solve these problems, being RANSAC one of the most accepted and used. However, with the arrival of new challenges, such as large driving scenarios for autonomous vehicles, along with the improvements in the data gathering frameworks, new issues must be considered. One of these issues is the capability of a technique to deal with very large amounts of data while meeting the realtime
constraint. With this purpose in mind, we present a novel technique for the problem of robust camera-pose estimation that is more suitable for dealing with large amount of data, which additionally, helps improving the results. The method is based on a combination of a very fast coarse-evaluation function and a robust ℓ1-averaging procedure. Such scheme leads to high-quality results while taking considerably less time than RANSAC.
Experimental results on the challenging KITTI Vision Benchmark Suite are provided, showing the validity of the proposed approach.		  
  Address Bristol; UK; September 2013  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference (down) BMVC  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RGS2013b; ADAS @ adas @ Serial 2274  
Permanent link to this record
 
 
Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title Incremental Domain Adaptation of Deformable Part-based Models Type Conference Article
  Year 2014 Publication 25th British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords Pedestrian Detection; Part-based models; Domain Adaptation  
  Abstract Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data.		  
  Address Nottingham; uk; September 2014  
  Corporate Author Thesis  
  Publisher BMVA Press Place of Publication Editor Valstar, Michel and French, Andrew and Pridmore, Tony  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference (down) BMVC  
  Notes ADAS; 600.057; 600.054; 600.076 Approved no  
  Call Number XRV2014c; ADAS @ adas @ xrv2014c Serial 2455  
Permanent link to this record
 
 
Author Daniel Hernandez; Lukas Schneider; Antonio Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan C. Moure edit   pdf
openurl 
  Title Slanted Stixels: Representing San Francisco's Steepest Streets} Type Conference Article
  Year 2017 Publication 28th British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.  
  Address London; uk; September 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference (down) BMVC  
  Notes ADAS; 600.118 Approved no  
  Call Number ADAS @ adas @ HSE2017a Serial 2945  
Permanent link to this record
 
 
Author Vassileios Balntas; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk edit   pdf
openurl 
  Title Learning local feature descriptors with triplets and shallow convolutional neural networks Type Conference Article
  Year 2016 Publication 27th British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives.
We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets.		  
  Address York; UK; September 2016  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference (down) BMVC  
  Notes ADAS; 600.086 Approved no  
  Call Number Admin @ si @ BRP2016 Serial 2818  
Permanent link to this record
 
 
Author Marc Masana; Idoia Ruiz; Joan Serrat; Joost Van de Weijer; Antonio Lopez edit   pdf
openurl 
  Title Metric Learning for Novelty and Anomaly Detection Type Conference Article
  Year 2018 Publication 29th British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract 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.  
  Address Newcastle; uk; September 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference (down) BMVC  
  Notes LAMP; ADAS; 601.305; 600.124; 600.106; 602.200; 600.120; 600.118 Approved no  
  Call Number Admin @ si @ MRS2018 Serial 3156  
Permanent link to this record
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