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| Author | Naveen Onkarappa; Angel Sappa | ||||
Title  |
On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow | Type | Conference Article | ||
| Year | 2010 | Publication | 7th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 6111 | Issue | Pages | 230-239 | |
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| Abstract | This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach. | ||||
| Address | Povoa de Varzim (Portugal) | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-13771-6 | Medium | |
| Area | Expedition | Conference | ICIAR | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ OnS2010 | Serial | 1342 | ||
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| Author | Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores | ||||
| Title |
On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on cybernetics | Abbreviated Journal | Cyber |
| Volume | 47 | Issue | 11 | Pages | 3980 - 3990 |
| Keywords | Multicue; multimodal; multiview; object detection | ||||
| Abstract | Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. | ||||
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| ISSN | 2168-2267 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.085; 600.082; 600.076; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 2810 | ||
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| Author | Jose Manuel Alvarez | ||||
| Title |
On-Board Road Surface Segmentation | Type | Report | ||
| Year | 2007 | Publication | CVC Technical Report #108 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Alv2007 | Serial | 820 | ||
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| Author | N. Zakaria; Jean-Marc Ogier; Josep Llados | ||||
| Title |
On-line Graphics Recognition based on Invariant Spatio-Sequential Descriptor: Fuzzy Matrix | Type | Miscellaneous | ||
| Year | 2005 | Publication | Sixth IAPR International Workshop on Graphics Recognition (GREC 2005), 248–259 | Abbreviated Journal | |
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| Address | Hong Kong (China) | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ YFY2005b | Serial | 622 | ||
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| Author | Jordi Gonzalez; Javier Varona; Juan J. Villanueva; Xavier Roca | ||||
| Title |
On-line Human Activity Recognition for Video Surveillance. | Type | Miscellaneous | ||
| Year | 2001 | Publication | Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 2:255–260. | Abbreviated Journal | |
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| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ GVV2001 | Serial | 111 | ||
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| Author | Carles Sanchez; Jorge Bernal; Debora Gil; F. Javier Sanchez | ||||
| Title |
On-line lumen centre detection in gastrointestinal and respiratory endoscopy | Type | Conference Article | ||
| Year | 2013 | Publication | Second International Workshop Clinical Image-Based Procedures | Abbreviated Journal | |
| Volume | 8361 | Issue | Pages | 31-38 | |
| Keywords | Lumen centre detection; Bronchoscopy; Colonoscopy | ||||
| Abstract | We present in this paper a novel lumen centre detection for gastrointestinal and respiratory endoscopic images. The proposed method is based on the appearance and geometry of the lumen, which we defined as the darkest image region which centre is a hub of image gradients. Experimental results validated on the first public annotated gastro-respiratory database prove the reliability of the method for a wide range of images (with precision over 95 %). | ||||
| Address | Nagoya; Japan; September 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor | Erdt, Marius and Linguraru, Marius George and Oyarzun Laura, Cristina and Shekhar, Raj and Wesarg, Stefan and González Ballester, Miguel Angel and Drechsler, Klaus | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-319-05665-4 | Medium | ||
| Area | 800 | Expedition | Conference | CLIP | |
| Notes | MV; IAM; 600.047; 600.044; 600.060 | Approved | no | ||
| Call Number | Admin @ si @ SBG2013 | Serial | 2302 | ||
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| Author | E. Sanchez | ||||
| Title |
On-line recognition of handwritten symbols | Type | Report | ||
| Year | 2001 | Publication | CVC Technical Report #52 | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ San2001 | Serial | 209 | ||
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| Author | R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. | ||||
| Title |
On-line Semantic Perception Using Uncertainty | Type | Conference Article | ||
| Year | 2012 | Publication | International Conference on Intelligent Robots and Systems | Abbreviated Journal | IROS |
| Volume | Issue | Pages | 4185-4191 | ||
| Keywords | Semantic Segmentation | ||||
| Abstract | Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not beaccurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance | ||||
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| Area | Expedition | Conference | IROS | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ NRR2012 | Serial | 2378 | ||
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| Author | Marc Masana; Joost Van de Weijer; Andrew Bagdanov | ||||
| Title |
On-the-fly Network pruning for object detection | Type | Conference Article | ||
| Year | 2016 | Publication | International conference on learning representations | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Object detection with deep neural networks is often performed by passing a few
thousand candidate bounding boxes through a deep neural network for each image. These bounding boxes are highly correlated since they originate from the same image. In this paper we investigate how to exploit feature occurrence at the image scale to prune the neural network which is subsequently applied to all bounding boxes. We show that removing units which have near-zero activation in the image allows us to significantly reduce the number of parameters in the network. Results on the PASCAL 2007 Object Detection Challenge demonstrate that up to 40% of units in some fully-connected layers can be entirely eliminated with little change in the detection result. |
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| Address | Puerto Rico; May 2016 | ||||
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| Area | Expedition | Conference | ICLR | ||
| Notes | LAMP; 600.068; 600.106; 600.079 | Approved | no | ||
| Call Number | Admin @ si @MWB2016 | Serial | 2758 | ||
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| Author | Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer | ||||
| Title |
One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | In this paper, we investigate model adaptation under domain and category shift, where the final goal is to achieve
(SF-UNDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-UNDA setting, the model cannot access source data anymore during target adaptation, which aims to address data privacy concerns. We propose a novel training scheme to learn a ( +1)-way classifier to predict the source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show: After source training, the resulting source model can get excellent performance for ; After target adaptation, our method surpasses current UNDA approaches which demand source data during adaptation. The versatility to several different tasks strongly proves the efficacy and generalization ability of our method. When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art UNDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively. |
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| Notes | LAMP; no proj | Approved | no | ||
| Call Number | Admin @ si @ YWW2022c | Serial | 3818 | ||
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| Author | Mohamed Ali Souibgui; Ali Furkan Biten; Sounak Dey; Alicia Fornes; Yousri Kessentini; Lluis Gomez; Dimosthenis Karatzas; Josep Llados | ||||
| Title |
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition | Type | Conference Article | ||
| Year | 2022 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Document Analysis | ||||
| Abstract | Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). This appears, for example, in the case of historical ciphered manuscripts, which are usually written with invented alphabets to hide the content. Thus, in this paper we address this problem through a data generation technique based on Bayesian Program Learning (BPL). Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol from the desired alphabet. After generating symbols, we create synthetic lines to train state-of-the-art HTR architectures in a segmentation free fashion. Quantitative and qualitative analyses were carried out and confirm the effectiveness of the proposed method, achieving competitive results compared to the usage of real annotated data. | ||||
| Address | Virtual; January 2022 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 602.230; 600.140 | Approved | no | ||
| Call Number | Admin @ si @ SBD2022 | Serial | 3615 | ||
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| Author | Mikhail Mozerov; Joost Van de Weijer | ||||
| Title |
One-view occlusion detection for stereo matching with a fully connected CRF model | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 28 | Issue | 6 | Pages | 2936-2947 |
| Keywords | Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter | ||||
| Abstract | In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method [15] to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only
one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-ofthe-art especially for median, average and mean squared error metrics. |
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| Notes | LAMP; 600.098; 600.109; 602.133; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ MoW2019 | Serial | 3221 | ||
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| Author | Sergio Escalera; David Masip; Eloi Puertas; Petia Radeva; Oriol Pujol | ||||
| Title |
Online Error-Correcting Output Codes | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 32 | Issue | 3 | Pages | 458-467 |
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| Abstract | IF JCR CCIA 1.303 2009 54/103
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier. |
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| Publisher | Elsevier | Place of Publication | North Holland | Editor | |
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| ISSN | 0167-8655 | ISBN | Medium | ||
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| Notes | MILAB;OR;HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ EMP2011 | Serial | 1714 | ||
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| Author | Bogdan Raducanu; Jordi Vitria | ||||
| Title |
Online Learning for Human-Robot Interaction | Type | Conference Article | ||
| Year | 2007 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshop on | Abbreviated Journal | |
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| Address | Minneapolis (USA) | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | OR; MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ RaV2007a | Serial | 791 | ||
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| Author | Bogdan Raducanu; Jordi Vitria | ||||
| Title |
Online Nonparametric Discriminant Analysis for Incremental Subspace Learning and Recognition | Type | Journal | ||
| Year | 2008 | Publication | Pattern Analysis and Applications. Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications | Abbreviated Journal | |
| Volume | 11 | Issue | 3-4 | Pages | 259–268 |
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| Publisher | Springer | Place of Publication | Editor | ||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ RaV2008c | Serial | 997 | ||
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