| Records |
| Author |
Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados |
| Title |
Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling |
Type |
Conference Article |
| Year |
2016 |
Publication |
Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
Abbreviated Journal |
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| Volume |
10029 |
Issue |
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Pages |
543-552 |
| Keywords |
Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection |
| Abstract |
The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results. |
| Address |
Merida; Mexico; December 2016 |
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| Publisher |
Springer International Publishing |
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LNCS |
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Edition |
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ISBN |
978-3-319-49054-0 |
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Expedition |
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Conference |
S+SSPR |
| Notes |
DAG; 600.097; 602.006 |
Approved |
no |
| Call Number |
Admin @ si @ TSF2016 |
Serial |
2877 |
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| Author |
Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure |
| Title |
Embedded Real-time Stixel Computation |
Type |
Conference Article |
| Year |
2017 |
Publication |
GPU Technology Conference |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
GPU; CUDA; Stixels; Autonomous Driving |
| Abstract |
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| Address |
Silicon Valley; USA; May 2017 |
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Conference |
GTC |
| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
ADAS @ adas @ HEV2017a |
Serial |
2879 |
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| Author |
David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
| Title |
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
Type |
Conference Article |
| Year |
2017 |
Publication |
31st International Congress and Exhibition on Computer Assisted Radiology and Surgery |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
Deep Learning; Medical Imaging |
| Abstract |
Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation. |
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Expedition |
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Conference |
CARS |
| Notes |
ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 |
Approved |
no |
| Call Number |
ADAS @ adas @ VBS2017a |
Serial |
2880 |
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| Author |
David Geronimo; David Vazquez; Arturo de la Escalera |
| Title |
Vision-Based Advanced Driver Assistance Systems |
Type |
Book Chapter |
| Year |
2017 |
Publication |
Computer Vision in Vehicle Technology: Land, Sea, and Air |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
ADAS; Autonomous Driving |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
ADAS @ adas @ GVE2017 |
Serial |
2881 |
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| Author |
German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez |
| Title |
Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA |
Type |
Book Chapter |
| Year |
2017 |
Publication |
Domain Adaptation in Computer Vision Applications |
Abbreviated Journal |
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| Volume |
12 |
Issue |
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Pages |
227-241 |
| Keywords |
SYNTHIA; Virtual worlds; Autonomous Driving |
| Abstract |
Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
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Editor |
Gabriela Csurka |
| Language |
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Summary Language |
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Original Title |
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Edition |
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Medium |
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Expedition |
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Conference |
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| Notes |
ADAS; 600.085; 600.082; 600.076; 600.118 |
Approved |
no |
| Call Number |
ADAS @ adas @ RSV2017 |
Serial |
2882 |
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| Author |
Antoni Gurgui; Debora Gil; Enric Marti; Vicente Grau |
| Title |
Left-Ventricle Basal Region Constrained Parametric Mapping to Unitary Domain |
Type |
Conference Article |
| Year |
2016 |
Publication |
7th International Workshop on Statistical Atlases & Computational Modelling of the Heart |
Abbreviated Journal |
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| Volume |
10124 |
Issue |
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Pages |
163-171 |
| Keywords |
Laplacian; Constrained maps; Parameterization; Basal ring |
| Abstract |
Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries. |
| Address |
Athens; October 2016 |
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Place of Publication |
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Original Title |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
STACOM |
| Notes |
IAM; |
Approved |
no |
| Call Number |
Admin @ si @ GGM2016 |
Serial |
2884 |
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| Author |
Carles Sanchez; Debora Gil; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell |
| Title |
Navigation Path Retrieval from Videobronchoscopy using Bronchial Branches |
Type |
Conference Article |
| Year |
2016 |
Publication |
19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops |
Abbreviated Journal |
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| Volume |
9401 |
Issue |
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Pages |
62-70 |
| Keywords |
Bronchoscopy navigation; Lumen center; Brochial branches; Navigation path; Videobronchoscopy |
| Abstract |
Bronchoscopy biopsy can be used to diagnose lung cancer without risking complications of other interventions like transthoracic needle aspiration. During bronchoscopy, the clinician has to navigate through the bronchial tree to the target lesion. A main drawback is the difficulty to check whether the exploration is following the correct path. The usual guidance using fluoroscopy implies repeated radiation of the clinician, while alternative systems (like electromagnetic navigation) require specific equipment that increases intervention costs. We propose to compute the navigated path using anatomical landmarks extracted from the sole analysis of videobronchoscopy images. Such landmarks allow matching the current exploration to the path previously planned on a CT to indicate clinician whether the planning is being correctly followed or not. We present a feasibility study of our landmark based CT-video matching using bronchoscopic videos simulated on a virtual bronchoscopy interactive interface. |
| Address |
Quebec; Canada; September 2016 |
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Abbreviated Series Title |
LNCS |
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Edition |
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Expedition |
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Conference |
MICCAIW |
| Notes |
IAM; MV; 600.060; 600.075 |
Approved |
no |
| Call Number |
Admin @ si @ SGB2016 |
Serial |
2885 |
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| Author |
Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa |
| Title |
Fine-tuning based deep convolutional networks for lepidopterous genus recognition |
Type |
Conference Article |
| Year |
2016 |
Publication |
21st Ibero American Congress on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
467-475 |
| Keywords |
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| Abstract |
This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%. |
| Address |
Lima; Perú; November 2016 |
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LNCS |
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Conference |
CIARP |
| Notes |
ADAS; 600.086 |
Approved |
no |
| Call Number |
Admin @ si @ CRS2016 |
Serial |
2913 |
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| Author |
H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester; Rasmus R. Paulsena |
| Title |
Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior |
Type |
Journal Article |
| Year |
2016 |
Publication |
Patter Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
76 |
Issue |
1 |
Pages |
76-82 |
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Conference |
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| Notes |
IAM; 600.060 |
Approved |
no |
| Call Number |
Admin @ si @ MFV2017b |
Serial |
2941 |
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| Author |
Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
| Title |
Banknote counterfeit detection through background texture printing analysis |
Type |
Conference Article |
| Year |
2016 |
Publication |
12th IAPR Workshop on Document Analysis Systems |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark. |
| Address |
Rumania; May 2016 |
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DAS |
| Notes |
DAG; 600.061; 601.269; 600.097 |
Approved |
no |
| Call Number |
Admin @ si @ BRL2016 |
Serial |
2950 |
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| Author |
Lluis Gomez; Dimosthenis Karatzas |
| Title |
TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild |
Type |
Journal Article |
| Year |
2017 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
70 |
Issue |
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Pages |
60-74 |
| Keywords |
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| Abstract |
Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
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| Notes |
DAG; 600.084; 601.197; 600.121; 600.129 |
Approved |
no |
| Call Number |
Admin @ si @ GoK2017 |
Serial |
2886 |
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| Author |
Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas |
| Title |
Improving patch‐based scene text script identification with ensembles of conjoined networks |
Type |
Journal Article |
| Year |
2017 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
67 |
Issue |
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Pages |
85-96 |
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| Notes |
DAG; 600.084; 600.121; 600.129 |
Approved |
no |
| Call Number |
Admin @ si @ GNK2017 |
Serial |
2887 |
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| Author |
Lluis Gomez; Y. Patel; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas |
| Title |
Self‐supervised learning of visual features through embedding images into text topic spaces |
Type |
Conference Article |
| Year |
2017 |
Publication |
30th IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multi-modal (text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches. |
| Address |
Honolulu; Hawaii; July 2017 |
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Conference |
CVPR |
| Notes |
DAG; 600.084; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ GPR2017 |
Serial |
2889 |
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| Author |
Marc Sunset Perez; Marc Comino Trinidad; Dimosthenis Karatzas; Antonio Chica Calaf; Pere Pau Vazquez Alcocer |
| Title |
Development of general‐purpose projection‐based augmented reality systems |
Type |
Journal |
| Year |
2016 |
Publication |
IADIs international journal on computer science and information systems |
Abbreviated Journal |
IADIs |
| Volume |
11 |
Issue |
2 |
Pages |
1-18 |
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| Abstract |
Despite the large amount of methods and applications of augmented reality, there is little homogenizatio n on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more co ncerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection – based AR. The developed framework is suitable and has been tested in AR applications using camera – projector pairs, for both fixed and nomadic setups |
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| Notes |
DAG; 600.084 |
Approved |
no |
| Call Number |
Admin @ si @ SCK2016 |
Serial |
2890 |
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| Author |
Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez |
| Title |
Enhancing spatio-chromatic representation with more-than-three color coding for image description |
Type |
Journal Article |
| Year |
2017 |
Publication |
Journal of the Optical Society of America A |
Abbreviated Journal |
JOSA A |
| Volume |
34 |
Issue |
5 |
Pages |
827-837 |
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| Abstract |
Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding. |
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Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
CIC; 600.087 |
Approved |
no |
| Call Number |
Admin @ si @ RVB2017 |
Serial |
2892 |
| Permanent link to this record |
