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| Author | Marc Bolaños; Maite Garolera; Petia Radeva | ||||
| Title | Object Discovery using CNN Features in Egocentric Videos | Type | Conference Article | ||
| Year | 2015 | Publication | Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 | Abbreviated Journal | |
| Volume | 9117 | Issue | Pages | 67-74 | |
| Keywords | Object discovery; Egocentric videos; Lifelogging; CNN | ||||
Abstract  |
Lifelogging devices based on photo/video are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. The egocentric video sequence acquired by the camera, uses both the appearance extracted by means of a deep convolutional neural network and an object refill methodology that allow to discover objects even in case of small amount of object appearance in the collection of images. We validate our method on a sequence of 1000 egocentric daily images and obtain results with an F-measure of 0.5, 0.17 better than the state of the art approach. | ||||
| 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 | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-319-19389-2 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGR2015 | Serial | 2596 | ||
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| Author | Santiago Segui; Oriol Pujol; Jordi Vitria | ||||
| Title | Learning to count with deep object features | Type | Conference Article | ||
| Year | 2015 | Publication | Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 90-96 | ||
| Keywords | |||||
| Abstract |
Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural
network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene. |
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| Address | Boston; USA; 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 | Expedition | Conference | CVPRW | ||
| Notes | MILAB; HuPBA; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ SPV2015 | Serial | 2636 | ||
| Permanent link to this record | |||||
| Author | Pau Riba; Alicia Fornes; Josep Llados | ||||
| Title | Towards the Alignment of Handwritten Music Scores | Type | Conference Article | ||
| Year | 2015 | Publication | 11th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract |
It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study. This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such differences. Given the difficulties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the staff lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results. | ||||
| Address | Nancy; France; August 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor | Bart Lamiroy; Rafael Dueire Lins | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-319-52158-9 | Medium | ||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 2874 | ||
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| Author | Hongxing Gao | ||||
| Title | Focused Structural Document Image Retrieval in Digital Mailroom Applications | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract |
In this work, we develop a generic framework that is able to handle the document retrieval problem in various scenarios such as searching for full page matches or retrieving the counterparts for specific document areas, focusing on their structural similarity or letting their visual resemblance to play a dominant role. Based on the spatial indexing technique, we propose to search for matches of local key-region pairs carrying both structural and visual information from the collection while a scheme allowing to adjust the relative contribution of structural and visual similarity is presented.
Based on the fact that the structure of documents is tightly linked with the distance among their elements, we firstly introduce an efficient detector named Distance Transform based Maximally Stable Extremal Regions (DTMSER). We illustrate that this detector is able to efficiently extract the structure of a document image as a dendrogram (hierarchical tree) of multi-scale key-regions that roughly correspond to letters, words and paragraphs. We demonstrate that, without benefiting from the structure information, the key-regions extracted by the DTMSER algorithm achieve better results comparing with state-of-the-art methods while much less amount of key-regions are employed. We subsequently propose a pair-wise Bag of Words (BoW) framework to efficiently embed the explicit structure extracted by the DTMSER algorithm. We represent each document as a list of key-region pairs that correspond to the edges in the dendrogram where inclusion relationship is encoded. By employing those structural key-region pairs as the pooling elements for generating the histogram of features, the proposed method is able to encode the explicit inclusion relations into a BoW representation. The experimental results illustrate that the pair-wise BoW, powered by the embedded structural information, achieves remarkable improvement over the conventional BoW and spatial pyramidal BoW methods. To handle various retrieval scenarios in one framework, we propose to directly query a series of key-region pairs, carrying both structure and visual information, from the collection. We introduce the spatial indexing techniques to the document retrieval community to speed up the structural relationship computation for key-region pairs. We firstly test the proposed framework in a full page retrieval scenario where structurally similar matches are expected. In this case, the pair-wise querying method achieves notable improvement over the BoW and spatial pyramidal BoW frameworks. Furthermore, we illustrate that the proposed method is also able to handle focused retrieval situations where the queries are defined as a specific interesting partial areas of the images. We examine our method on two types of focused queries: structure-focused and exact queries. The experimental results show that, the proposed generic framework obtains nearly perfect precision on both types of focused queries while it is the first framework able to tackle structure-focused queries, setting a new state of the art in the field. Besides, we introduce a line verification method to check the spatial consistency among the matched key-region pairs. We propose a computationally efficient version of line verification through a two step implementation. We first compute tentative localizations of the query and subsequently employ them to divide the matched key-region pairs into several groups, then line verification is performed within each group while more precise bounding boxes are computed. We demonstrate that, comparing with the standard approach (based on RANSAC), the line verification proposed generally achieves much higher recall with slight loss on precision on specific queries. |
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| Address | January 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Josep Llados;Dimosthenis Karatzas;Marçal Rusiñol | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-943427-0-7 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ Gao2015 | Serial | 2577 | ||
| Permanent link to this record | |||||
| Author | Frederic Sampedro; Sergio Escalera | ||||
| Title | Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation | Type | Journal Article | ||
| Year | 2015 | Publication | IET Computer Vision | Abbreviated Journal | IETCV |
| Volume | 9 | Issue | 3 | Pages | 439 - 446 |
| Keywords | |||||
| Abstract |
In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches. | ||||
| Address | |||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1751-9632 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ SaE2015 | Serial | 2551 | ||
| Permanent link to this record | |||||
| Author | Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados | ||||
| Title | Document Analysis Techniques for Automatic Electoral Document Processing: A Survey | Type | Conference Article | ||
| Year | 2015 | Publication | E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 139-141 | ||
| Keywords | Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally | ||||
| Abstract |
In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents. | ||||
| Address | Bern; Switzerland; September 2015 | ||||
| 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 | VoteID | ||
| Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ TCP2015 | Serial | 2641 | ||
| Permanent link to this record | |||||
| Author | Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados | ||||
| Title | Attributed Graph Grammar for floor plan analysis | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 726 - 730 | ||
| Keywords | |||||
| Abstract |
In this paper, we propose the use of an Attributed Graph Grammar as unique framework to model and recognize the structure of floor plans. This grammar represents a building as a hierarchical composition of structurally and semantically related elements, where common representations are learned stochastically from annotated data. Given an input image, the parsing consists on constructing that graph representation that better agrees with the probabilistic model defined by the grammar. The proposed method provides several advantages with respect to the traditional floor plan analysis techniques. It uses an unsupervised statistical approach for detecting walls that adapts to different graphical notations and relaxes strong structural assumptions such are straightness and orthogonality. Moreover, the independence between the knowledge model and the parsing implementation allows the method to learn automatically different building configurations and thus, to cope the existing variability. These advantages are clearly demonstrated by comparing it with the most recent floor plan interpretation techniques on 4 datasets of real floor plans with different notations. | ||||
| Address | Nancy; France; August 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 | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.077; 600.061 | Approved | no | ||
| Call Number | Admin @ si @ HRL2015b | Serial | 2727 | ||
| Permanent link to this record | |||||
| Author | Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias | ||||
| Title | Scene Representations for Autonomous Driving: an approach based on polygonal primitives | Type | Conference Article | ||
| Year | 2015 | Publication | 2nd Iberian Robotics Conference ROBOT2015 | Abbreviated Journal | |
| Volume | 417 | Issue | Pages | 503-515 | |
| Keywords | Scene reconstruction; Point cloud; Autonomous vehicles | ||||
| Abstract |
In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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| Address | Lisboa; Portugal; November 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 | Expedition | Conference | ROBOT | ||
| Notes | ADAS; 600.076; 600.086 | Approved | no | ||
| Call Number | Admin @ si @ OSS2015a | Serial | 2662 | ||
| Permanent link to this record | |||||
| Author | Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados | ||||
| Title | Towards Query-by-Speech Handwritten Keyword Spotting | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 501-505 | ||
| Keywords | |||||
| Abstract |
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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| Address | Nancy; France; August 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 | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.061; 601.223; 600.077;ADAS | Approved | no | ||
| Call Number | Admin @ si @ RAT2015b | Serial | 2682 | ||
| Permanent link to this record | |||||
| Author | E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva | ||||
| Title | Regularized Clustering for Egocentric Video Segmentation | Type | Book Chapter | ||
| Year | 2015 | Publication | Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | 327-336 | ||
| Keywords | Temporal video segmentation ; Egocentric videos ; Clustering | ||||
| Abstract |
In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. | ||||
| Address | |||||
| 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 | ISBN | 978-3-319-19390-8 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @TDB2015a | Serial | 2781 | ||
| Permanent link to this record | |||||
| Author | Estefania Talavera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva | ||||
| Title | R-clustering for egocentric video segmentation | Type | Conference Article | ||
| Year | 2015 | Publication | Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 | Abbreviated Journal | |
| Volume | 9117 | Issue | Pages | 327-336 | |
| Keywords | Temporal video segmentation; Egocentric videos; Clustering | ||||
| Abstract |
In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate both techniques in an energy-minimization framework that serves to disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames descriptors. We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. | ||||
| Address | Santiago de Compostela; España; 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 | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-319-19389-2 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ TDB2015 | Serial | 2597 | ||
| Permanent link to this record | |||||
| Author | Lluis Garrido; M.Guerrieri; Laura Igual | ||||
| Title | Image Segmentation with Cage Active Contours | Type | Journal Article | ||
| Year | 2015 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 24 | Issue | 12 | Pages | 5557 - 5566 |
| Keywords | Level sets; Mean value coordinates; Parametrized active contours; level sets; mean value coordinates | ||||
| Abstract |
In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods. | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ GGI2015 | Serial | 2673 | ||
| Permanent link to this record | |||||
| Author | T. Mouats; N. Aouf; Angel Sappa; Cristhian A. Aguilera-Carrasco; Ricardo Toledo | ||||
| Title | Multi-Spectral Stereo Odometry | Type | Journal Article | ||
| Year | 2015 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | 16 | Issue | 3 | Pages | 1210-1224 |
| Keywords | Egomotion estimation; feature matching; multispectral odometry (MO); optical flow; stereo odometry; thermal imagery | ||||
| Abstract |
In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather
than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based on the descriptors. Pyramidal Lucas–Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating Gauss–Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated. |
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1524-9050 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.055; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ MAS2015a | Serial | 2533 | ||
| Permanent link to this record | |||||
| Author | Mariella Dimiccoli; Petia Radeva | ||||
| Title | Lifelogging in the era of outstanding digitization | Type | Conference Article | ||
| Year | 2015 | Publication | International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract |
In this paper, we give an overview on the emerging trend of the digitized self, focusing on visual lifelogging through wearable cameras. This is about continuously recording our life from a first-person view by wearing a camera that passively captures images. On one hand, visual lifelogging has opened the door to a large number of applications, including health. On the other, it has also boosted new challenges in the field of data analysis as well as new ethical concerns. While currently increasing efforts are being devoted to exploit lifelogging data for the improvement of personal well-being, we believe there are still many interesting applications to explore, ranging from tourism to the digitization of human behavior. | ||||
| Address | Verliko Tarmovo; Bulgaria; September 2015 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | DiPP | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @DiR2016 | Serial | 2792 | ||
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| Author | J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier | ||||
| Title | Improving Document Matching Performance by Local Descriptor Filtering | Type | Conference Article | ||
| Year | 2015 | Publication | 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 1216 - 1220 | ||
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| Abstract |
In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using
ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements. |
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| Address | Nancy; France; August 2015 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CBDAR | ||
| Notes | DAG; 600.077; 601.223; 600.084 | Approved | no | ||
| Call Number | Admin @ si @ CRO2015a | Serial | 2680 | ||
| Permanent link to this record | |||||