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Author |
German Barquero; Sergio Escalera; Cristina Palmero |
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Title  |
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction |
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Conference Article |
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Year |
2023 |
Publication |
IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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Pages |
2317-2327 |
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Abstract |
Stochastic human motion prediction (HMP) has generally been tackled with generative adversarial networks and variational autoencoders. Most prior works aim at predicting highly diverse movements in terms of the skeleton joints’ dispersion. This has led to methods predicting fast and motion-divergent movements, which are often unrealistic and incoherent with past motion. Such methods also neglect contexts that need to anticipate diverse low-range behaviors, or actions, with subtle joint displacements. To address these issues, we present BeLFusion, a model that, for the first time, leverages latent diffusion models in HMP to sample from a latent space where behavior is disentangled from pose and motion. As a result, diversity is encouraged from a behavioral perspective. Thanks to our behavior
coupler’s ability to transfer sampled behavior to ongoing motion, BeLFusion’s predictions display a variety of behaviors that are significantly more realistic than the state of the art. To support it, we introduce two metrics, the Area of
the Cumulative Motion Distribution, and the Average Pairwise Distance Error, which are correlated to our definition of realism according to a qualitative study with 126 participants. Finally, we prove BeLFusion’s generalization power in a new cross-dataset scenario for stochastic HMP. |
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2-6 October 2023. Paris (France) |
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ICCV |
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Notes |
HUPBA; no menciona |
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no |
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Call Number |
Admin @ si @ BEP2023 |
Serial |
3829 |
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Author |
E. Royer; J. Chazalon; Marçal Rusiñol; F. Bouchara |
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Title |
Benchmarking Keypoint Filtering Approaches for Document Image Matching |
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Conference Article |
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Year |
2017 |
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14th International Conference on Document Analysis and Recognition |
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Best Poster Award.
Reducing the amount of keypoints used to index an image is particularly interesting to control processing time and memory usage in real-time document image matching applications, like augmented documents or smartphone applications. This paper benchmarks two keypoint selection methods on a task consisting of reducing keypoint sets extracted from document images, while preserving detection and segmentation accuracy. We first study the different forms of keypoint filtering, and we introduce the use of the CORE selection method on
keypoints extracted from document images. Then, we extend a previously published benchmark by including evaluations of the new method, by adding the SURF-BRISK detection/description scheme, and by reporting processing speeds. Evaluations are conducted on the publicly available dataset of ICDAR2015 SmartDOC challenge 1. Finally, we prove that reducing the original keypoint set is always feasible and can be beneficial
not only to processing speed but also to accuracy. |
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Kyoto; Japan; November 2017 |
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ICDAR |
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Notes |
DAG; 600.084; 600.121 |
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no |
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Call Number |
Admin @ si @ RCR2017 |
Serial |
3000 |
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Author |
Galadrielle Humblot-Renaux; Sergio Escalera; Thomas B. Moeslund |
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Title |
Beyond AUROC & co. for evaluating out-of-distribution detection performance |
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Conference Article |
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Year |
2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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3880-3889 |
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While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric – Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://github.com/glhr/beyond-auroc |
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Vancouver; Canada; June 2023 |
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CVPRW |
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Notes |
HUPBA |
Approved |
no |
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Call Number |
Admin @ si @ HEM2023 |
Serial |
3918 |
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Author |
David Fernandez; Jon Almazan; Nuria Cirera; Alicia Fornes; Josep Llados |
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Title |
BH2M: the Barcelona Historical Handwritten Marriages database |
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Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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256 - 261 |
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Abstract |
This paper presents an image database of historical handwritten marriages records stored in the archives of Barcelona cathedral, and the corresponding meta-data addressed to evaluate the performance of document analysis algorithms. The contribution of this paper is twofold. First, it presents a complete ground truth which covers the whole pipeline of handwriting
recognition research, from layout analysis to recognition and understanding. Second, it is the first dataset in the emerging area of genealogical document analysis, where documents are manuscripts pseudo-structured with specific lexicons and the interest is beyond pure transcriptions but context dependent. |
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Creete Island; Grecia; September 2014 |
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1051-4651 |
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ICPR |
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Notes |
DAG; 600.056; 600.061; 602.006; 600.077 |
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no |
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Call Number |
Admin @ si @ FAC2014 |
Serial |
2461 |
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Author |
Reza Azad; Maryam Asadi Aghbolaghi; Mahmood Fathy; Sergio Escalera |
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Title |
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions |
Type |
Conference Article |
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Year |
2019 |
Publication |
Visual Recognition for Medical Images workshop |
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406-415 |
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Abstract |
In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance. |
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Seul; Korea; October 2019 |
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ICCVW |
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Notes |
HUPBA; no proj |
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no |
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Call Number |
Admin @ si @ AAF2019 |
Serial |
3324 |
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Author |
Volkmar Frinken; Alicia Fornes; Josep Llados; Jean-Marc Ogier |
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Title |
Bidirectional Language Model for Handwriting Recognition |
Type |
Conference Article |
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Year |
2012 |
Publication |
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
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Volume |
7626 |
Issue |
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Pages |
611-619 |
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Abstract |
In order to improve the results of automatically recognized handwritten text, information about the language is commonly included in the recognition process. A common approach is to represent a text line as a sequence. It is processed in one direction and the language information via n-grams is directly included in the decoding. This approach, however, only uses context on one side to estimate a word’s probability. Therefore, we propose a bidirectional recognition in this paper, using distinct forward and a backward language models. By combining decoding hypotheses from both directions, we achieve a significant increase in recognition accuracy for the off-line writer independent handwriting recognition task. Both language models are of the same type and can be estimated on the same corpus. Hence, the increase in recognition accuracy comes without any additional need for training data or language modeling complexity. |
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Japan |
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Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-34165-6 |
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SSPR&SPR |
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DAG |
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no |
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Call Number |
Admin @ si @ FFL2012 |
Serial |
2057 |
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Author |
Carlo Gatta; Petia Radeva |
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Title |
Bilateral Enhancers |
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Conference Article |
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Year |
2009 |
Publication |
16th IEEE International Conference on Image Processing |
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3161-3165 |
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Abstract |
Ten years ago the concept of bilateral filtering (BF) became popular in the image processing community. The core of the idea is to blend the effect of a spatial filter, as e.g. the Gaussian filter, with the effect of a filter that acts on image values. The two filters acts on orthogonal domains of a picture: the 2D lattice of the image support and the intensity (or color) domain. The BF approach is an intuitive way to blend these two filters giving rise to algorithms that perform difficult tasks requiring a relatively simple design. In this paper we extend the concept of BF, proposing the bilateral enhancers (BE). We show how to design proper functions to obtain an edge-preserving smoothing and a selective sharpening. Moreover, we show that the proposed algorithm can perform edge-preserving smoothing and selective sharpening simultaneously in a single filtering. |
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Cairo, Egypt |
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1522-4880 |
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978-1-4244-5653-6 |
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ICIP |
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MILAB |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ GaR2009b |
Serial |
1243 |
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Author |
Xavier Perez Sala; Cecilio Angulo; Sergio Escalera |
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Title |
Biologically Inspired Path Execution Using SURF Flow in Robot Navigation |
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Conference Article |
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2011 |
Publication |
11th International Work Conference on Artificial Neural Networks |
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II |
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581--588 |
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Abstract |
An exportable and robust system using only camera images is proposed for path execution in robot navigation. Motion information is extracted in the form of optical flow from SURF robust descriptors of consecutive frames, so the method is called SURF flow. This information is used to correct robot displacement when a straight forward path command is sent to the robot, but it is not really executed due to several robot and environmental concerns. The proposed system has been successfully tested on the legged robot Aibo. |
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Malaga |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-21497-4 |
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IWANN |
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HuPBA;MILAB |
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no |
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Call Number |
Admin @ si @ PAE2011b |
Serial |
1773 |
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Author |
Xavier Perez Sala; Cecilio Angulo; Sergio Escalera |
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Title |
Biologically Inspired Turn Control in Robot Navigation |
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Conference Article |
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Year |
2011 |
Publication |
14th Congrès Català en Intel·ligencia Artificial |
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187-196 |
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An exportable and robust system for turn control using only camera images is proposed for path execution in robot navigation. Robot motion information is extracted in the form of optical flow from SURF robust descriptors of consecutive frames in the image sequence. This information is used to compute the instantaneous rotation angle. Finally, control loop is closed correcting robot displacements when it is requested for a turn command. The proposed system has been successfully tested on the four-legged Sony Aibo robot. |
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Lleida |
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978-1-60750-841-0 |
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CCIA |
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HuPBA;MILAB |
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no |
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Admin @ si @ PAE2011a |
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1753 |
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Author |
Arash Akbarinia; C. Alejandro Parraga |
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Title |
Biologically plausible boundary detection |
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Conference Article |
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2016 |
Publication |
27th British Machine Vision Conference |
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Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. |
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York; UK; September 2016 |
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BMVC |
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NEUROBIT; 600.068; 600.072 |
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no |
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Admin @ si @ AkP2016a |
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2867 |
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Author |
Arash Akbarinia; C. Alejandro Parraga |
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Title |
Biologically Plausible Colour Naming Model |
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Conference Article |
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2015 |
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European Conference on Visual Perception ECVP2015 |
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Poster |
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Liverpool; UK; August 2015 |
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ECVP |
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NEUROBIT; 600.068 |
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Admin @ si @ AkP2015 |
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2660 |
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Author |
Anton Cervantes; Gemma Sanchez; Josep Llados; Agnes Borras; A. Rodriguez |
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Title |
Biometric Recognition Based on Line Shape Descriptors |
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Conference Article |
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2005 |
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Sixth IAPR International Workshop on Graphics Recognition (GREC 2005) |
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335–344 |
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Hong Kong (China) |
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DAG |
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DAG @ dag @ CSL2005 |
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596 |
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Author |
Sonia Baeza; Debora Gil; Carles Sanchez; Guillermo Torres; Ignasi Garcia Olive; Ignasi Guasch; Samuel Garcia Reina; Felipe Andreo; Jose Luis Mate; Jose Luis Vercher; Antonio Rosell |
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Title |
Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung |
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Conference Article |
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Year |
2023 |
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SEPAR |
Abbreviated Journal |
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Pòster |
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Granada; Spain; June 2023 |
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SEPAR |
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IAM |
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no |
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Admin @ si @ BGS2023 |
Serial |
3951 |
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Author |
David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri |
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Title |
Blood Detection in IVUS Images for 3D Volume of Lumen Changes Measurement Due to Different Drugs Administration |
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Conference Article |
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Year |
2007 |
Publication |
Computer Analysis of Images and Patterns, 12th International Conference |
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Volume |
4673 |
Issue |
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Pages |
285–292 |
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Address |
Vienna (Austria) |
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LNCS |
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ISBN |
978-3-540-74271-5 |
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CAIP |
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Notes |
MILAB |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ RRF2007b |
Serial |
832 |
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Author |
David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri |
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Title |
Blood Detection In IVUS Longitudinal Cuts Using AdaBoost With a Novel Feature Stability Criterion |
Type |
Conference Article |
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Year |
2007 |
Publication |
Artificial Intelligence Research and Development. Proceedings of the 10th International Conference of the ACIA |
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Volume |
163 |
Issue |
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Pages |
197–204 |
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ISBN |
978-1-58603-798-7 |
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Conference |
CCIA’07 |
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Notes |
MILAB |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ RRF2007a |
Serial |
831 |
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