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Author |
David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados |
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Title |
Integrating Visual and Textual Cues for Query-by-String Word Spotting |
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Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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511 - 515 |
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In this paper, we present a word spotting framework that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character $n$-grams while the visual one is based on the bag-of-visual-words scheme. These two representations are merged together and projected to a sub-vector space. This transform allows to, given a textual query, retrieve word instances that were only represented by the visual modality. Moreover, this statistical representation can be used together with state-of-the-art indexation structures in order to deal with large-scale scenarios. The proposed method is evaluated using a collection of historical documents outperforming state-of-the-art performances. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; ADAS; 600.045; 600.055; 600.061 |
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no |
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Call Number |
Admin @ si @ ART2013 |
Serial |
2224 |
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Author |
Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
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Title |
Towards Query-by-Speech Handwritten Keyword Spotting |
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Conference Article |
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Year |
2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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501-505 |
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In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
dataset. |
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Nancy; France; August 2015 |
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DAG; 600.084; 600.061; 601.223; 600.077;ADAS |
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Call Number |
Admin @ si @ RAT2015b |
Serial |
2682 |
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Author |
Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers |
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Title |
Adapting Pedestrian Detection from Synthetic to Far Infrared Images |
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Conference Article |
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Year |
2013 |
Publication |
ICCV Workshop on Visual Domain Adaptation and Dataset Bias |
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Keywords |
Domain Adaptation; Far Infrared; Pedestrian Detection |
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We present different techniques to adapt a pedestrian classifier trained with synthetic images and the corresponding automatically generated annotations to operate with far infrared (FIR) images. The information contained in this kind of images allow us to develop a robust pedestrian detector invariant to extreme illumination changes. |
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Sydney; Australia; December 2013 |
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Sydney, Australy |
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English |
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ICCVW-VisDA |
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Notes |
ADAS; 600.054; 600.055; 600.057; 601.217;ISE |
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Call Number |
ADAS @ adas @ SRV2013 |
Serial |
2334 |
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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
DA-DPM Pedestrian Detection |
Type |
Conference Article |
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Year |
2013 |
Publication |
ICCV Workshop on Reconstruction meets Recognition |
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Keywords |
Domain Adaptation; Pedestrian Detection |
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ICCVW-RR |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ XRV2013 |
Serial |
2569 |
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Author |
Patricia Marquez; Debora Gil; Aura Hernandez-Sabate |
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Title |
A Confidence Measure for Assessing Optical Flow Accuracy in the Absence of Ground Truth |
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Conference Article |
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Year |
2011 |
Publication |
IEEE International Conference on Computer Vision – Workshops |
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Pages |
2042-2049 |
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IEEE International Conference on Computer Vision – Workshops |
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Abstract |
Optical flow is a valuable tool for motion analysis in autonomous navigation systems. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in real world sequences. This paper introduces a measure of optical flow accuracy for Lucas-Kanade based flows in terms of the numerical stability of the data-term. We call this measure optical flow condition number. A statistical analysis over ground-truth data show a good statistical correlation between the condition number and optical flow error. Experiments on driving sequences illustrate its potential for autonomous navigation systems. |
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IEEE |
Place of Publication |
Barcelona (Spain) |
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English |
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English |
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ICCVW |
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Notes |
IAM; ADAS |
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Call Number |
IAM @ iam @ MGH2011 |
Serial |
1682 |
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Author |
Javad Zolfaghari Bengar; Abel Gonzalez-Garcia; Gabriel Villalonga; Bogdan Raducanu; Hamed H. Aghdam; Mikhail Mozerov; Antonio Lopez; Joost Van de Weijer |
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Title |
Temporal Coherence for Active Learning in Videos |
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Conference Article |
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Year |
2019 |
Publication |
IEEE International Conference on Computer Vision Workshops |
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Pages |
914-923 |
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Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used to define the nodes of a graph and tracked forward and backward to temporally link the nodes. Minimizing an energy function defined on this graphical model provides estimates of both false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active learning for video object detection in road scenes. Finally, we show that our approach outperforms active learning baselines tested on two datasets. |
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Seul; Corea; October 2019 |
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ICCVW |
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Notes |
LAMP; ADAS; 600.124; 602.200; 600.118; 600.120; 600.141 |
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Call Number |
Admin @ si @ ZGV2019 |
Serial |
3294 |
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Author |
Xavier Boix; Josep M. Gonfaus; Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Marco Pedersoli; Jordi Gonzalez; Joan Serrat |
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Title |
Combining local and global bag-of-word representations for semantic segmentation |
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Conference Article |
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Year |
2009 |
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Workshop on The PASCAL Visual Object Classes Challenge |
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Kyoto (Japan) |
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ICCV |
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ADAS;ISE |
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Call Number |
ADAS @ adas @ BGS2009 |
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1273 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Correspondence Free Registration through a Point-to-Model Distance Minimization |
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Conference Article |
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Year |
2011 |
Publication |
13th IEEE International Conference on Computer Vision |
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Pages |
2150-2157 |
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This paper presents a novel formulation, which derives in a smooth minimization problem, to tackle the rigid registration between a given point set and a model set. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, we propose to describe the model set by means of an implicit representation. It allows a new definition of the registration error, which works beyond the point level representation. Moreover, it could be used in a gradient-based optimization framework. The proposed approach consists of two stages. Firstly, a novel formulation is proposed that relates the registration parameters with the distance between the model and data set. Secondly, the registration parameters are obtained by means of the Levengberg-Marquardt algorithm. Experimental results and comparisons with state of the art show the validity of the proposed framework. |
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Barcelona |
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1550-5499 |
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978-1-4577-1101-5 |
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ICCV |
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ADAS |
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Admin @ si @ RoS2011b; ADAS @ adas @ |
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1832 |
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Author |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe |
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Title |
Random Forests of Local Experts for Pedestrian Detection |
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Conference Article |
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Year |
2013 |
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15th IEEE International Conference on Computer Vision |
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2592 - 2599 |
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ADAS; Random Forest; Pedestrian Detection |
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Abstract |
Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one. |
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Sydney; Australia; December 2013 |
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IEEE |
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1550-5499 |
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ICCV |
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ADAS; 600.057; 600.054 |
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Call Number |
ADAS @ adas @ MVL2013 |
Serial |
2333 |
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Author |
Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool |
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Title |
Active MAP Inference in CRFs for Efficient Semantic Segmentation |
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Conference Article |
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Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
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2312 - 2319 |
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Semantic Segmentation |
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Abstract |
Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains. |
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Sydney; Australia; December 2013 |
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1550-5499 |
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ICCV |
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Notes |
ADAS; 600.057 |
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Call Number |
ADAS @ adas @ RBN2013 |
Serial |
2377 |
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