Home | [71–80] << 81 82 83 84 85 86 87 88 89 90 >> [91–100] |
![]() |
Records | |||||
---|---|---|---|---|---|
Author | P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes | ||||
Title | A Novel Learning-free Word Spotting Approach Based on Graph Representation | Type | Conference Article | ||
Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 207-211 | ||
Keywords | |||||
Abstract | Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods. | ||||
Address | Tours; France; April 2014 | ||||
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 | 978-1-4799-3243-6 | Medium | ||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ WEG2014b | Serial | 2517 | ||
Permanent link to this record | |||||
Author | Claudio Baecchi; Francesco Turchini; Lorenzo Seidenari; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | Fisher vectors over random density forest for object recognition | Type | Conference Article | ||
Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 4328-4333 | ||
Keywords | |||||
Abstract | |||||
Address | Stockholm; Sweden; August 2014 | ||||
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 | ICPR | ||
Notes | LAMP; 600.079 | Approved | no | ||
Call Number | Admin @ si @ BTS2014 | Serial | 2518 | ||
Permanent link to this record | |||||
Author | Federico Bartoli; Giuseppe Lisanti; Svebor Karaman; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | Unsupervised scene adaptation for faster multi- scale pedestrian detection | Type | Conference Article | ||
Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 3534 - 3539 | ||
Keywords | |||||
Abstract | |||||
Address | Stockholm; Sweden; August 2014 | ||||
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 | ICPR | ||
Notes | LAMP; 600.079 | Approved | no | ||
Call Number | Admin @ si @ BLK2014 | Serial | 2519 | ||
Permanent link to this record | |||||
Author | Antonio Hernandez; Stan Sclaroff; Sergio Escalera | ||||
Title | Contextual rescoring for Human Pose Estimation | Type | Conference Article | ||
Year | 2014 | Publication | 25th British Machine Vision Conference | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | |||
Keywords | |||||
Abstract | A contextual rescoring method is proposed for improving the detection of body joints of a pictorial structure model for human pose estimation. A set of mid-level parts is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body joint hypotheses. A technique is proposed for the automatic discovery of a compact subset of poselets that covers a set of validation images
while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for body joint detections, given its relationship to detections of other body joints and mid-level parts in the image. This new score complements the unary potential of a discriminatively trained pictorial structure model. Experiments on two benchmarks show performance improvements when considering the proposed mid-level image representation and rescoring approach in comparison with other pictorial structure-based approaches. |
||||
Address | Nottingham; 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 | BMVC | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | HSE2014 | Serial | 2525 | ||
Permanent link to this record | |||||
Author | Francisco Cruz; Oriol Ramos Terrades | ||||
Title | EM-Based Layout Analysis Method for Structured Documents | Type | Conference Article | ||
Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 315-320 | ||
Keywords | |||||
Abstract | In this paper we present a method to perform layout analysis in structured documents. We proposed an EM-based algorithm to fit a set of Gaussian mixtures to the different regions according to the logical distribution along the page. After the convergence, we estimate the final shape of the regions according
to the parameters computed for each component of the mixture. We evaluated our method in the task of record detection in a collection of historical structured documents and performed a comparison with other previous works in this task. |
||||
Address | |||||
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 | 1051-4651 | ISBN | Medium | ||
Area | Expedition | Conference | ICPR | ||
Notes | DAG; 602.006; 600.061; 600.077 | Approved | no | ||
Call Number | Admin @ si @ CrR2014 | Serial | 2530 | ||
Permanent link to this record | |||||
Author | Mohammad Rouhani; E. Boyer; Angel Sappa | ||||
Title | Non-Rigid Registration meets Surface Reconstruction | Type | Conference Article | ||
Year | 2014 | Publication | International Conference on 3D Vision | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 617-624 | ||
Keywords | |||||
Abstract | Non rigid registration is an important task in computer vision with many applications in shape and motion modeling. A fundamental step of the registration is the data association between the source and the target sets. Such association proves difficult in practice, due to the discrete nature of the information and its corruption by various types of noise, e.g. outliers and missing data. In this paper we investigate the benefit of the implicit representations for the non-rigid registration of 3D point clouds. First, the target points are described with small quadratic patches that are blended through partition of unity weighting. Then, the discrete association between the source and the target can be replaced by a continuous distance field induced by the interface. By combining this distance field with a proper deformation term, the registration energy can be expressed in a linear least square form that is easy and fast to solve. This significantly eases the registration by avoiding direct association between points. Moreover, a hierarchical approach can be easily implemented by employing coarse-to-fine representations. Experimental results are provided for point clouds from multi-view data sets. The qualitative and quantitative comparisons show the outperformance and robustness of our framework. %in presence of noise and outliers. | ||||
Address | Tokyo; Japan; December 2014 | ||||
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 | 3DV | ||
Notes | ADAS; 600.055; 600.076 | Approved | no | ||
Call Number | Admin @ si @ RBS2014 | Serial | 2534 | ||
Permanent link to this record | |||||
Author | Lluis Gomez; Dimosthenis Karatzas | ||||
Title | Scene Text Recognition: No Country for Old Men? | Type | Conference Article | ||
Year | 2014 | Publication | 1st International Workshop on Robust Reading | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | |||||
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 | IWRR | ||
Notes | DAG; 600.077 | Approved | no | ||
Call Number | Admin @ si @ GoK2014c | Serial | 2538 | ||
Permanent link to this record | |||||
Author | E. Bondi ; L. Sidenari; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | Real-time people counting from depth imagery of crowded environments | Type | Conference Article | ||
Year | 2014 | Publication | 11th IEEE International Conference on Advanced Video and Signal based Surveillance | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 337 - 342 | ||
Keywords | |||||
Abstract | In this paper we describe a system for automatic people counting in crowded environments. The approach we propose is a counting-by-detection method based on depth imagery. It is designed to be deployed as an autonomous appliance for crowd analysis in video surveillance application scenarios. Our system performs foreground/background segmentation on depth image streams in order to coarsely segment persons, then depth information is used to localize head candidates which are then tracked in time on an automatically estimated ground plane. The system runs in real-time, at a frame-rate of about 20 fps. We collected a dataset of RGB-D sequences representing three typical and challenging surveillance scenarios, including crowds, queuing and groups. An extensive comparative evaluation is given between our system and more complex, Latent SVM-based head localization for person counting applications. | ||||
Address | Seoul; Korea; August 2014 | ||||
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 | AVSS | ||
Notes | LAMP; 600.079 | Approved | no | ||
Call Number | Admin @ si @ BSB2014 | Serial | 2540 | ||
Permanent link to this record | |||||
Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
Title | Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors | Type | Conference Article | ||
Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 156-160 | ||
Keywords | |||||
Abstract | This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising. | ||||
Address | |||||
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 | 978-1-4799-3243-6 | Medium | ||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 600.077 | Approved | no | ||
Call Number | Admin @ si @ DTR2014 | Serial | 2543 | ||
Permanent link to this record | |||||
Author | Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier | ||||
Title | Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images | Type | Conference Article | ||
Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 181 - 185 | ||
Keywords | |||||
Abstract | Mobile document image acquisition is a new trend raising serious issues in business document processing workflows. Such digitization procedure is unreliable, and integrates many distortions which must be detected as soon as possible, on the mobile, to avoid paying data transmission fees, and losing information due to the inability to re-capture later a document with temporary availability. In this context, out-of-focus blur is major issue: users have no direct control over it, and it seriously degrades OCR recognition. In this paper, we concentrate on the estimation of focus quality, to ensure a sufficient legibility of a document image for OCR processing. We propose two contributions to improve OCR accuracy prediction for mobile-captured document images. First, we present 24 focus measures, never tested on document images, which are fast to compute and require no training. Second, we show that a combination of those measures enables state-of-the art performance regarding the correlation with OCR accuracy. The resulting approach is fast, robust, and easy to implement in a mobile device. Experiments are performed on a public dataset, and precise details about image processing are given. | ||||
Address | Tours; France; April 2014 | ||||
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 | 978-1-4799-3243-6 | Medium | ||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 601.223; 600.077 | Approved | no | ||
Call Number | Admin @ si @ RCO2014a | Serial | 2545 | ||
Permanent link to this record | |||||
Author | Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier | ||||
Title | Normalisation et validation d'images de documents capturées en mobilité | Type | Conference Article | ||
Year | 2014 | Publication | Colloque International Francophone sur l'Écrit et le Document | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 109-124 | ||
Keywords | mobile document image acquisition; perspective correction; illumination correction; quality assessment; focus measure; OCR accuracy prediction | ||||
Abstract | Mobile document image acquisition integrates many distortions which must be corrected or detected on the device, before the document becomes unavailable or paying data transmission fees. In this paper, we propose a system to correct perspective and illumination issues, and estimate the sharpness of the image for OCR recognition. The correction step relies on fast and accurate border detection followed by illumination normalization. Its evaluation on a private dataset shows a clear improvement on OCR accuracy. The quality assessment
step relies on a combination of focus measures. Its evaluation on a public dataset shows that this simple method compares well to state of the art, learning-based methods which cannot be embedded on a mobile, and outperforms metric-based methods. |
||||
Address | Nancy; France; March 2014 | ||||
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 | CIFED | ||
Notes | DAG; 601.223; 600.077 | Approved | no | ||
Call Number | Admin @ si @ RCO2014b | Serial | 2546 | ||
Permanent link to this record | |||||
Author | Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez | ||||
Title | Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection | Type | Conference Article | ||
Year | 2015 | Publication | IEEE Intelligent Vehicles Symposium IV2015 | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 356-361 | ||
Keywords | Pedestrian Detection | ||||
Abstract | Despite recent significant advances, pedestrian 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 multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, 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. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy. | ||||
Address | Seoul; Corea; 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 | IV | |
Notes | ADAS; 600.076; 600.057; 600.054 | Approved | no | ||
Call Number | ADAS @ adas @ GVX2015 | Serial | 2625 | ||
Permanent link to this record | |||||
Author | P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes | ||||
Title | Représentation par graphe de mots manuscrits dans les images pour la recherche par similarité | Type | Conference Article | ||
Year | 2014 | Publication | Colloque International Francophone sur l'Écrit et le Document | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 233-248 | ||
Keywords | word spotting; graph-based representation; shape context description; graph edit distance; DTW; block merging; query by example | ||||
Abstract | Effective information retrieval on handwritten document images has always been
a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labeled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment results introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods. |
||||
Address | Nancy; Francia; March 2014 | ||||
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 | CIFED | ||
Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ WEG2014c | Serial | 2564 | ||
Permanent link to this record | |||||
Author | Michal Drozdzal; Jordi Vitria; Santiago Segui; Carolina Malagelada; Fernando Azpiroz; Petia Radeva | ||||
Title | Intestinal event segmentation for endoluminal video analysis | Type | Conference Article | ||
Year | 2014 | Publication | 21st IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | 3592 - 3596 | ||
Keywords | |||||
Abstract | |||||
Address | Paris; Francia; October 2014 | ||||
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 | ICIP | ||
Notes | MILAB; OR;MV | Approved | no | ||
Call Number | Admin @ si @ DVS2014 | Serial | 2565 | ||
Permanent link to this record | |||||
Author | Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez | ||||
Title | DA-DPM Pedestrian Detection | Type | Conference Article | ||
Year | 2013 | Publication | ICCV Workshop on Reconstruction meets Recognition | Abbreviated Journal | |
Volume ![]() |
Issue | Pages | |||
Keywords | Domain Adaptation; Pedestrian Detection | ||||
Abstract | |||||
Address | |||||
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 | ICCVW-RR | ||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ XRV2013 | Serial | 2569 | ||
Permanent link to this record |