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Author Gioacchino Vino; Angel Sappa edit  doi
isbn  openurl
  Title Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach Type Conference Article
  Year 2013 Publication 10th International Conference on Image Analysis and Recognition Abbreviated Journal  
  Volume (up) 7950 Issue Pages 354-363  
  Keywords  
  Abstract This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach.  
  Address Póvoa de Varzim; Portugal; June 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 978-3-642-39093-7 Medium  
  Area Expedition Conference ICIAR  
  Notes ADAS; 600.055 Approved no  
  Call Number Admin @ si @ ViS2013 Serial 2562  
Permanent link to this record
 
 
Author Patricia Marquez; Debora Gil; Aura Hernandez-Sabate; Daniel Kondermann edit   pdf
url  doi
isbn  openurl
  Title When Is A Confidence Measure Good Enough? Type Conference Article
  Year 2013 Publication 9th International Conference on Computer Vision Systems Abbreviated Journal  
  Volume (up) 7963 Issue Pages 344-353  
  Keywords Optical flow, confidence measure, performance evaluation  
  Abstract Confidence estimation has recently become a hot topic in image processing and computer vision.Yet, several definitions exist of the term “confidence” which are sometimes used interchangeably. This is a position paper, in which we aim to give an overview on existing definitions,
thereby clarifying the meaning of the used terms to facilitate further research in this field. Based on these clarifications, we develop a theory to compare confidence measures with respect to their quality.		  
  Address St Petersburg; Russia; July 2013  
  Corporate Author Thesis  
  Publisher Springer Link 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 978-3-642-39401-0 Medium  
  Area Expedition Conference ICVS  
  Notes IAM;ADAS; 600.044; 600.057; 600.060; 601.145 Approved no  
  Call Number IAM @ iam @ MGH2013a Serial 2218  
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 (up) 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 CAIP  
  Notes ADAS; 600.055; 601.215 Approved no  
  Call Number Admin @ si @ OnS2013b Serial 2244  
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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 (up) 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 CAIP  
  Notes ADAS; 600.055 Approved no  
  Call Number Admin @ si @ PST2013 Serial 2561  
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Author Katerine Diaz; Francesc J. Ferri; W. Diaz edit  doi
isbn  openurl
  Title Fast Approximated Discriminative Common Vectors using rank-one SVD updates Type Conference Article
  Year 2013 Publication 20th International Conference On Neural Information Processing Abbreviated Journal  
  Volume (up) 8228 Issue III Pages 368-375  
  Keywords  
  Abstract An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz		  
  Address Daegu; Korea; November 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 978-3-642-42050-4 Medium  
  Area Expedition Conference ICONIP  
  Notes ADAS Approved no  
  Call Number Admin @ si @ DFD2013 Serial 2439  
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Author Patricia Marquez; H. Kause; A. Fuster; Aura Hernandez-Sabate; L. Florack; Debora Gil; Hans van Assen edit   pdf
doi  isbn
openurl 
  Title Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging Type Conference Article
  Year 2014 Publication 17th International Conference on Medical Image Computing and Computer Assisted Intervention Abbreviated Journal  
  Volume (up) 8896 Issue Pages 231-238  
  Keywords Optical flow; Performance Evaluation; Synthetic Database; ANOVA; Tagging Magnetic Resonance Imaging  
  Abstract Changes in cardiac deformation patterns are correlated with cardiac pathologies. Deformation can be extracted from tagging Magnetic Resonance Imaging (tMRI) using Optical Flow (OF) techniques. For applications of OF in a clinical setting it is important to assess to what extent the performance of a particular OF method is stable across di erent clinical acquisition artifacts. This paper presents a statistical validation framework, based on ANOVA, to assess the motion and appearance factors that have the largest in uence on OF accuracy drop.
In order to validate this framework, we created a database of simulated tMRI data including the most common artifacts of MRI and test three di erent OF methods, including HARP.		  
  Address Boston; USA; September 2014  
  Corporate Author Thesis  
  Publisher Springer International Publishing 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 978-3-319-14677-5 Medium  
  Area Expedition Conference STACOM  
  Notes IAM; ADAS; 600.060; 601.145; 600.076; 600.075 Approved no  
  Call Number Admin @ si @ MKF2014 Serial 2495  
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Author Alejandro Gonzalez Alzate; Gabriel Villalonga; German Ros; David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title 3D-Guided Multiscale Sliding Window for Pedestrian Detection Type Conference Article
  Year 2015 Publication Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 Abbreviated Journal  
  Volume (up) 9117 Issue Pages 560-568  
  Keywords Pedestrian Detection  
  Abstract The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy.  
  Address Santiago de Compostela; España; June 2015  
  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 ACDC Expedition Conference IbPRIA  
  Notes ADAS; 600.076; 600.057; 600.054 Approved no  
  Call Number ADAS @ adas @ GVR2015 Serial 2585  
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Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen edit  url
doi  isbn
openurl 
  Title Deep semantic pyramids for human attributes and action recognition Type Conference Article
  Year 2015 Publication Image Analysis, Proceedings of 19th Scandinavian Conference , SCIA 2015 Abbreviated Journal  
  Volume (up) 9127 Issue Pages 341-353  
  Keywords Action recognition; Human attributes; Semantic pyramids  
  Abstract Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features.
We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature.		  
  Address Denmark; Copenhagen; June 2015  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-319-19664-0 Medium  
  Area Expedition Conference SCIA  
  Notes LAMP; 600.068; 600.079;ADAS Approved no  
  Call Number Admin @ si @ KRW2015b Serial 2672  
Permanent link to this record
 
 
Author Dennis G.Romero; Anselmo Frizera; Angel Sappa; Boris X. Vintimilla; Teodiano F.Bastos edit   pdf
url  doi
isbn  openurl
  Title A predictive model for human activity recognition by observing actions and context Type Conference Article
  Year 2015 Publication Advanced Concepts for Intelligent Vision Systems, Proceedings of 16th International Conference, ACIVS 2015 Abbreviated Journal  
  Volume (up) 9386 Issue Pages 323-333  
  Keywords  
  Abstract This paper presents a novel model to estimate human activities — a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach.  
  Address Catania; Italy; October 2015  
  Corporate Author Thesis  
  Publisher Springer International Publishing 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 978-3-319-25902-4 Medium  
  Area Expedition Conference ACIVS  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ RFS2015 Serial 2661  
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Author Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier edit   pdf
openurl 
  Title LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode Type Conference Article
  Year 2016 Publication 14th European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume (up) 9915 Issue Pages 894-900  
  Keywords Simulation environment; Automated Driving; Driver-Vehicle interaction  
  Abstract Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.  
  Address Amsterdam; The Netherlands; October 2016  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECCVW  
  Notes ADAS;IAM; 600.085; 600.076 Approved no  
  Call Number MHE2016 Serial 2865  
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