Home | [181–190] << 191 192 193 194 195 196 197 198 199 200 >> [201–210] |
![]() |
Records | |||||
---|---|---|---|---|---|
Author | Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi | ||||
Title | Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light | Type | Conference Article | ||
Year | 2016 | Publication ![]() |
19th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | computer vision; video analysis; object recognition; tracking; behaviour; social; decapod; Nephrops norvegicus | ||||
Abstract | Automated video and image analysis can be a very efficient tool to analyze
animal behavior based on sociality, especially in hard access environments for researchers. The understanding of this social behavior can play a key role in the sustainable design of capture policies of many species. This paper proposes the use of computer vision algorithms to identify and track a specific specie, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior. This emergent behavior is modulated by the day-night cycle, but their social interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory pools, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-ofthe-art computer vision algorithms to distinguish and track the different animals’ movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community. |
||||
Address | Barcelona; Spain; October 2016 | ||||
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 | CCIA | ||
Notes | OR;MV; | Approved | no | ||
Call Number | Admin @ si @ GMS2016 | Serial | 2816 | ||
Permanent link to this record | |||||
Author | Petia Radeva | ||||
Title | Can Deep Learning and Egocentric Vision for Visual Lifelogging Help Us Eat Better? | Type | Conference Article | ||
Year | 2016 | Publication ![]() |
19th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
Volume | 4 | Issue | Pages | ||
Keywords | |||||
Abstract | |||||
Address | Barcelona; October 2016 | ||||
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 | CCIA | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ Rad2016 | Serial | 2832 | ||
Permanent link to this record | |||||
Author | Ekaterina Zaytseva; Jordi Vitria | ||||
Title | A search based approach to non maximum suppression in face detection | Type | Conference Article | ||
Year | 2012 | Publication ![]() |
19th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | Poster
paper TA.P5.12 Face detectors typically produce a large number of false positives and this leads to the need to have a further non maximum suppression stage to eliminate multiple and spurious responses. This stage is based on considering spatial heuristics: true positive responses are selected by implicitly considering several restrictions on the spatial distribution of detector responses in natural images. In this paper we analyze the limitations of this approach and propose an efficient search method to overcome them. Results show how the application of this new non-maximum suppression approach to a simple face detector boosts its performance to state of the art results. |
||||
Address | Orlando; USA; September 2012 | ||||
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 | 1522-4880 | ISBN | 978-1-4673-2534-9 | Medium | |
Area | Expedition | Conference | ICIP | ||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ ZaV2012 | Serial | 2060 | ||
Permanent link to this record | |||||
Author | Yaxing Wang; Hector Laria Mantecon; Joost Van de Weijer; Laura Lopez-Fuentes; Bogdan Raducanu | ||||
Title | TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets | Type | Conference Article | ||
Year | 2021 | Publication ![]() |
19th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 13990-13999 | ||
Keywords | |||||
Abstract | Image-to-image (I2I) translation has matured in recent years and is able to generate high-quality realistic images. However, despite current success, it still faces important challenges when applied to small domains. Existing methods use transfer learning for I2I translation, but they still require the learning of millions of parameters from scratch. This drawback severely limits its application on small domains. In this paper, we propose a new transfer learning for I2I translation (TransferI2I). We decouple our learning process into the image generation step and the I2I translation step. In the first step we propose two novel techniques: source-target initialization and self-initialization of the adaptor layer. The former finetunes the pretrained generative model (e.g., StyleGAN) on source and target data. The latter allows to initialize all non-pretrained network parameters without the need of any data. These techniques provide a better initialization for the I2I translation step. In addition, we introduce an auxiliary GAN that further facilitates the training of deep I2I systems even from small datasets. In extensive experiments on three datasets, (Animal faces, Birds, and Foods), we show that we outperform existing methods and that mFID improves on several datasets with over 25 points. | ||||
Address | Virtual; October 2021 | ||||
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 | ICCV | ||
Notes | LAMP; 600.147; 602.200; 600.120 | Approved | no | ||
Call Number | Admin @ si @ WLW2021 | Serial | 3604 | ||
Permanent link to this record | |||||
Author | Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui | ||||
Title | Generalized Source-free Domain Adaptation | Type | Conference Article | ||
Year | 2021 | Publication ![]() |
19th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 8958-8967 | ||
Keywords | |||||
Abstract | Domain adaptation (DA) aims to transfer the knowledge learned from a source domain to an unlabeled target domain. Some recent works tackle source-free domain adaptation (SFDA) where only a source pre-trained model is available for adaptation to the target domain. However, those methods do not consider keeping source performance which is of high practical value in real world applications. In this paper, we propose a new domain adaptation paradigm called Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both the target and source domains, with only access to current unlabeled target data during adaptation. First, we propose local structure clustering (LSC), aiming to cluster the target features with its semantically similar neighbors, which successfully adapts the model to the target domain in the absence of source data. Second, we propose sparse domain attention (SDA), it produces a binary domain specific attention to activate different feature channels for different domains, meanwhile the domain attention will be utilized to regularize the gradient during adaptation to keep source information. In the experiments, for target performance our method is on par with or better than existing DA and SFDA methods, specifically it achieves state-of-the-art performance (85.4%) on VisDA, and our method works well for all domains after adapting to single or multiple target domains. | ||||
Address | Virtual; October 2021 | ||||
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 | |||
Notes | LAMP; 600.120; 600.147 | Approved | no | ||
Call Number | Admin @ si @ YWW2021 | Serial | 3605 | ||
Permanent link to this record | |||||
Author | Hugo Bertiche; Meysam Madadi; Emilio Tylson; Sergio Escalera | ||||
Title | DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation | Type | Conference Article | ||
Year | 2021 | Publication ![]() |
19th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 5471-5480 | ||
Keywords | |||||
Abstract | We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. | ||||
Address | Virtual; October 2021 | ||||
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 | ICCV | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ BMT2021 | Serial | 3606 | ||
Permanent link to this record | |||||
Author | Rosa Maria Ortiz; Debora Gil; Elisa Minchole; Marta Diez-Ferrer; Noelia Cubero de Frutos | ||||
Title | Classification of Confolcal Endomicroscopy Patterns for Diagnosis of Lung Cancer | Type | Conference Article | ||
Year | 2017 | Publication ![]() |
18th World Conference on Lung Cancer | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%. We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
||||
Address | Yokohama; Japan; October 2017 | ||||
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 | IASLC WCLC | ||
Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
Call Number | Admin @ si @ OGM2017 | Serial | 3044 | ||
Permanent link to this record | |||||
Author | Carolina Malagelada; F.De Lorio; Fernando Azpiroz; Santiago Segui; Petia Radeva; Anna Accarino; J.Santos; Juan R. Malagelada | ||||
Title | Intestinal Dysmotility in Patients with Functional Intestinal Disorders Demonstrated by Computer Vision Analysis of Capsule Endoscopy Images | Type | Conference Article | ||
Year | 2010 | Publication ![]() |
18th United European Gastroenterology Week | Abbreviated Journal | |
Volume | 56 | Issue | 3 | Pages | A19-20 |
Keywords | |||||
Abstract | |||||
Address | Barcelona | ||||
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 | UEGW | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ MLA2010 | Serial | 1779 | ||
Permanent link to this record | |||||
Author | Angel Morera; Angel Sanchez; Angel Sappa; Jose F. Velez | ||||
Title | Robust Detection of Outdoor Urban Advertising Panels in Static Images | Type | Conference Article | ||
Year | 2019 | Publication ![]() |
18th International Conference on Practical Applications of Agents and Multi-Agent Systems | Abbreviated Journal | |
Volume | Issue | Pages | 246-256 | ||
Keywords | Object detection; Urban ads panels; Deep learning; Single Shot Detector (SSD) architecture; Intersection over Union (IoU) metric; Augmented Reality | ||||
Abstract | One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising panels. For such a purpose, a previous stage is to accurately detect and locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based on a deep neural network architecture that minimizes the number of false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection over Union (IoU) accuracy metric make this proposal applicable in real complex urban images. | ||||
Address | Aquila; Italia; June 2019 | ||||
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 | PAAMS | ||
Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ MSS2019 | Serial | 3270 | ||
Permanent link to this record | |||||
Author | Michael Villamizar; A. Sanfeliu; Juan Andrade | ||||
Title | Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection | Type | Miscellaneous | ||
Year | 2006 | Publication ![]() |
18th International Conference on Pattern Recognition, 81–85 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | Hong Kong | ||||
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 | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ VSA2006a | Serial | 663 | ||
Permanent link to this record | |||||
Author | Fadi Dornaika; Franck Davoine | ||||
Title | Facial expression recognition using auto-regressive models | Type | Miscellaneous | ||
Year | 2006 | Publication ![]() |
18th International Conference on Pattern Recognition (ICPR´06), ISBN: 0–7695–2521–0, 4: 520–523 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | Hong Kong | ||||
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 | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ DoD2006a | Serial | 734 | ||
Permanent link to this record | |||||
Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
Title | Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a novel framework to detect and classify objects in cluttered scenes | Type | Miscellaneous | ||
Year | 2006 | Publication ![]() |
18th International Conference on Pattern Recognition (ICPR´06), 4: 104–107, ISBN: 0–7695–2521–0 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | Hong Kong | ||||
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 | |||
Notes | MILAB;HuPBA | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ EPR2006a | Serial | 692 | ||
Permanent link to this record | |||||
Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
Title | ECOC-ONE: A novel coding and decoding strategy | Type | Miscellaneous | ||
Year | 2006 | Publication ![]() |
18th International Conference on Pattern Recognition (ICPR´06), 3: 578–581, ISBN: 0–7695–2521–0 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | Hong Kong | ||||
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 | |||
Notes | MILAB;HuPBA | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ EPR2006b | Serial | 693 | ||
Permanent link to this record | |||||
Author | Oriol Ramos Terrades; Salvatore Tabbone; Ernest Valveny | ||||
Title | Combination of shape descriptors using an adaptation of boosting | Type | Miscellaneous | ||
Year | 2006 | Publication ![]() |
18th International Conference on Pattern Recognition (ICPR´06), 2: 764–767 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | Hong Kong | ||||
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 | |||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ RTV2006 | Serial | 718 | ||
Permanent link to this record | |||||
Author | Joan Mas; B. Lamiroy; Gemma Sanchez; Josep Llados | ||||
Title | Automatic Adjacency Grammar Generation from User Drawn Sketches | Type | Miscellaneous | ||
Year | 2006 | Publication ![]() |
18th International Conference on Pattern Recognition (ICPR´06), 2: 1026–1029 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | |||||
Address | Hong Kong | ||||
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 | |||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ MLS2006a | Serial | 709 | ||
Permanent link to this record |