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Author Petia Radeva; Cristina Cañero; Juan J. Villanueva; J. Mauri; E Fernandez-Nofrerias
Title (up) 3D Reconstruction of a Stent by Deformable Models. Type Miscellaneous
Year 2001 Publication Proceedings of the IASTED International Conference, Visualization, Imaging and Image Processing, 417–422. Abbreviated Journal
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Abstract
Address Marbella.
Corporate Author Thesis
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ RCV2001 Serial 158
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Author Petia Radeva; M. Scoccianti
Title (up) 3D Reconstruction of Abdominal Aortic Aneurysm Type Miscellaneous
Year 2000 Publication Elsevier Science B.V., Ed. H.U. Lemke, M.W. Vannier, K. Inamura, A.G.Farman and K.Doi, CARS 2000, pp.1014, ISBN:0–444–50536–9 Abbreviated Journal
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Address San Francisco, USA
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ RaS2000 Serial 439
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Author Fernando Vilariño
Title (up) 3D Scanning of Capitals at Library Living Lab Type Book Whole
Year 2019 Publication “Living Lab Projects 2019”. ENoLL. Abbreviated Journal
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Address
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Notes MV; DAG; 600.140; 600.121;SIAI Approved no
Call Number Admin @ si @ Vil2019c Serial 3463
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Author Jose Manuel Alvarez; Theo Gevers; Antonio Lopez
Title (up) 3D Scene Priors for Road Detection Type Conference Article
Year 2010 Publication 23rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 57–64
Keywords road detection
Abstract Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods.
Address San Francisco; CA; USA; June 2010
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 1063-6919 ISBN 978-1-4244-6984-0 Medium
Area Expedition Conference CVPR
Notes ADAS;ISE Approved no
Call Number ADAS @ adas @ AGL2010a Serial 1302
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Author Shun Yao; Fei Yang; Yongmei Cheng; Mikhail Mozerov
Title (up) 3D Shapes Local Geometry Codes Learning with SDF Type Conference Article
Year 2021 Publication International Conference on Computer Vision Workshops Abbreviated Journal
Volume Issue Pages 2110-2117
Keywords
Abstract A signed distance function (SDF) as the 3D shape description is one of the most effective approaches to represent 3D geometry for rendering and reconstruction. Our work is inspired by the state-of-the-art method DeepSDF [17] that learns and analyzes the 3D shape as the iso-surface of its shell and this method has shown promising results especially in the 3D shape reconstruction and compression domain. In this paper, we consider the degeneration problem of reconstruction coming from the capacity decrease of the DeepSDF model, which approximates the SDF with a neural network and a single latent code. We propose Local Geometry Code Learning (LGCL), a model that improves the original DeepSDF results by learning from a local shape geometry of the full 3D shape. We add an extra graph neural network to split the single transmittable latent code into a set of local latent codes distributed on the 3D shape. Mentioned latent codes are used to approximate the SDF in their local regions, which will alleviate the complexity of the approximation compared to the original DeepSDF. Furthermore, we introduce a new geometric loss function to facilitate the training of these local latent codes. Note that other local shape adjusting methods use the 3D voxel representation, which in turn is a problem highly difficult to solve or even is insolvable. In contrast, our architecture is based on graph processing implicitly and performs the learning regression process directly in the latent code space, thus make the proposed architecture more flexible and also simple for realization. Our experiments on 3D shape reconstruction demonstrate that our LGCL method can keep more details with a significantly smaller size of the SDF decoder and outperforms considerably the original DeepSDF method under the most important quantitative metrics.
Address VIRTUAL; October 2021
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Publisher Place of Publication Editor
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Area Expedition Conference ICCVW
Notes LAMP Approved no
Call Number Admin @ si @ YYC2021 Serial 3681
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Author Debora Gil; F. Javier Sanchez; Gloria Fernandez Esparrach; Jorge Bernal
Title (up) 3D Stable Spatio-temporal Polyp Localization in Colonoscopy Videos Type Book Chapter
Year 2015 Publication Computer-Assisted and Robotic Endoscopy. Revised selected papers of Second International Workshop, CARE 2015, Held in Conjunction with MICCAI 2015 Abbreviated Journal
Volume 9515 Issue Pages 140-152
Keywords Colonoscopy, Polyp Detection, Polyp Localization, Region Extraction, Watersheds
Abstract Computational intelligent systems could reduce polyp miss rate in colonoscopy for colon cancer diagnosis and, thus, increase the efficiency of the procedure. One of the main problems of existing polyp localization methods is a lack of spatio-temporal stability in their response. We propose to explore the response of a given polyp localization across temporal windows in order to select
those image regions presenting the highest stable spatio-temporal response.
Spatio-temporal stability is achieved by extracting 3D watershed regions on the
temporal window. Stability in localization response is statistically determined by analysis of the variance of the output of the localization method inside each 3D region. We have explored the benefits of considering spatio-temporal stability in two different tasks: polyp localization and polyp detection. Experimental results indicate an average improvement of 21:5% in polyp localization and 43:78% in polyp detection.
Address
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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 CARE
Notes IAM; MV; 600.075 Approved no
Call Number Admin @ si @ GSF2015 Serial 2733
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Author Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu
Title (up) 3D Texton Spaces for color-texture retrieval Type Conference Article
Year 2010 Publication 7th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 6111 Issue Pages 354–363
Keywords
Abstract Color and texture are visual cues of different nature, their integration in an useful visual descriptor is not an easy problem. One way to combine both features is to compute spatial texture descriptors independently on each color channel. Another way is to do the integration at the descriptor level. In this case the problem of normalizing both cues arises. In this paper we solve the latest problem by fusing color and texture through distances in texton spaces. Textons are the attributes of image blobs and they are responsible for texture discrimination as defined in Julesz’s Texton theory. We describe them in two low-dimensional and uniform spaces, namely, shape and color. The dissimilarity between color texture images is computed by combining the distances in these two spaces. Following this approach, we propose our TCD descriptor which outperforms current state of art methods in the two different approaches mentioned above, early combination with LBP and late combination with MPEG-7. This is done on an image retrieval experiment over a highly diverse texture dataset from Corel.
Address
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Publisher Springer Berlin Heidelberg Place of Publication Editor A.C. Campilho and M.S. Kamel
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-13771-6 Medium
Area Expedition Conference ICIAR
Notes CIC Approved no
Call Number CAT @ cat @ ASV2010a Serial 1325
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Author Daniel Ponsa; Antonio Lopez; Felipe Lumbreras; Joan Serrat; T. Graf
Title (up) 3D Vehicle Sensor based on Monocular Vision Type Miscellaneous
Year 2005 Publication Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 1096–1101, ISBN:0–7803–9216–7 Abbreviated Journal
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Abstract
Address Vienna (Austria)
Corporate Author Thesis
Publisher Place of Publication Editor
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Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ PLL2005 Serial 614
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Author Petia Radeva; Ricardo Toledo; Craig Von Land; Juan J. Villanueva
Title (up) 3D Vessel Reconstruction from Biplane Angiograms using Snakes. Type Miscellaneous
Year 1998 Publication Abbreviated Journal
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Address Cleveland, OH
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Notes MILAB;ADAS Approved no
Call Number BCNPCL @ bcnpcl @ RTV1998a Serial 198
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Author Senmao Li; Joost Van de Weijer; Yaxing Wang; Fahad Shahbaz Khan; Meiqin Liu; Jian Yang
Title (up) 3D-Aware Multi-Class Image-to-Image Translation with NeRFs Type Conference Article
Year 2023 Publication 36th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 12652-12662
Keywords
Abstract Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multiclass image-to-image (3D-aware 121) translation. Naively using 2D-121 translation methods suffers from unrealistic shape/identity change. To perform 3D-aware multiclass 121 translation, we decouple this learning process into a multiclass 3D-aware GAN step and a 3D-aware 121 translation step. In the first step, we propose two novel techniques: a new conditional architecture and an effective training strategy. In the second step, based on the well-trained multiclass 3D-aware GAN architecture, that preserves view-consistency, we construct a 3D-aware 121 translation system. To further reduce the view-consistency problems, we propose several new techniques, including a U-net-like adaptor network design, a hierarchical representation constrain and a relative regularization loss. In exten-sive experiments on two datasets, quantitative and qualitative results demonstrate that we successfully perform 3D-aware 121 translation with multi-view consistency. Code is available in 3DI2I.
Address Vancouver; Canada; June 2023
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Area Expedition Conference CVPR
Notes LAMP Approved no
Call Number Admin @ si @ LWW2023b Serial 3920
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Author Alejandro Gonzalez Alzate; Gabriel Villalonga; German Ros; David Vazquez; Antonio Lopez
Title (up) 3D-Guided Multiscale Sliding Window for Pedestrian Detection Type Conference Article
Year 2015 Publication Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 Abbreviated Journal
Volume 9117 Issue Pages 560-568
Keywords Pedestrian Detection
Abstract The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy.
Address Santiago de Compostela; España; June 2015
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area ACDC Expedition Conference IbPRIA
Notes ADAS; 600.076; 600.057; 600.054 Approved no
Call Number ADAS @ adas @ GVR2015 Serial 2585
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Author Mohamed Ramzy Ibrahim; Robert Benavente; Felipe Lumbreras; Daniel Ponsa
Title (up) 3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D Residual in Residual Dense Blocks Type Conference Article
Year 2022 Publication CVPR 2022 Workshop on IEEE Perception Beyond the Visible Spectrum workshop series (PBVS, 18th Edition) Abbreviated Journal
Volume Issue Pages
Keywords Training; Solid modeling; Three-dimensional displays; PSNR; Convolution; Superresolution; Pattern recognition
Abstract The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively.
Address New Orleans, USA; 19 June 2022
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference CVPRW
Notes MSIAU; 600.130 Approved no
Call Number Admin @ si @ IBL2022 Serial 3693
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Author Ignasi Rius; Dani Rowe; Jordi Gonzalez; Xavier Roca
Title (up) A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking Type Book Chapter
Year 2005 Publication Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522: 529–536 Abbreviated Journal
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Address Estoril (Portugal)
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Notes ISE Approved no
Call Number ISE @ ise @ RRG2005b Serial 544
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Author Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa
Title (up) A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers Type Conference Article
Year 2017 Publication IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In general, robot grasping approaches are based on the usage of multi-finger grippers. However, when large size objects need to be manipulated vacuum grippers are preferred, instead of finger based grippers. This paper aims to estimate the best picking place for a two suction cups vacuum gripper,
when planar objects with an unknown size and geometry are considered. The approach is based on the estimation of geometric properties of object’s shape from a partial cloud of points (a single 3D view), in such a way that combine with considerations of a theoretical model to generate an optimal contact point
that minimizes the vacuum force needed to guarantee a grasp.
Experimental results in real scenarios are presented to show the validity of the proposed approach.
Address San Sebastian; Spain; May 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference ECMSM
Notes ADAS; 600.086; 600.118 Approved no
Call Number Admin @ si @ VIS2017 Serial 2917
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Author Albert Gordo; Alicia Fornes; Ernest Valveny; Josep Llados
Title (up) A Bag of Notes Approach to Writer Identification in Old Handwritten Music Scores Type Conference Article
Year 2010 Publication 9th IAPR International Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages 247–254
Keywords
Abstract Determining the authorship of a document, namely writer identification, can be an important source of information for document categorization. Contrary to text documents, the identification of the writer of graphical documents is still a challenge. In this paper we present a robust approach for writer identification in a particular kind of graphical documents, old music scores. This approach adapts the bag of visual terms method for coping with graphic documents. The identification is performed only using the graphical music notation. For this purpose, we generate a graphic vocabulary without recognizing any music symbols, and consequently, avoiding the difficulties in the recognition of hand-drawn symbols in old and degraded documents. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving very high identification rates.
Address Boston; USA;
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-60558-773-8 Medium
Area Expedition Conference DAS
Notes DAG Approved no
Call Number DAG @ dag @ GFV2010 Serial 1320
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