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Author |
Daniel Ponsa; Xavier Roca |
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Title |
Multiple Model Approach to Deformable Shape Tracking |
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2003 |
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1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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2652 |
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782-792 |
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Springer-Verlag |
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ADAS;ISE |
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ADAS @ adas @ PoR2003 |
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397 |
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Author |
J. Pladellorens; Joan Serrat; A. Castell; M.J. Yzuel |
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Title |
Using mathematical morphology to determine left ventricular contours. |
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1993 |
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Physics in Medicine and Biology. |
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38 |
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12 |
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1877––1894 |
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ADAS @ adas @ PSC1993 |
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146 |
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Author |
A. Pujol; Javier Varona; Joan Serrat |
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Title |
A machine vision system for the inspection of industrial sieves. |
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Conference Article |
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1997 |
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(SNRFAI’97) 7th Spanish National Symposium on Pattern Recognition and Image Analysis |
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ADAS @ adas @ PVS1997 |
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33 |
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Author |
Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
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Title |
Monocular 3D Human Body Reconstruction Towards Depth Augmentation of Television Sequences |
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Conference Article |
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2003 |
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IEEE International Conference on Image Processing, Barcelona, Spain, September 2003 |
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325-328 |
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Barcelona |
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ADAS @ adas @ SAM2003 |
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418 |
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Author |
Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez; Antonio Lopez; C. Elvira |
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Title |
Dynamic Comparison of Headlights |
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Journal Article |
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2008 |
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Journal of Automobile Engineering |
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222 |
Issue |
5 |
Pages |
643–656 |
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video alignment |
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ADAS |
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ADAS @ adas @ SDL2008a |
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958 |
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Author |
Joan Serrat |
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Title |
Aplicacion del analisis de imagenes en radiologia. |
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1995 |
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VI National Simposium on Pattern Recognition and image Analysis |
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IbPRIA |
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ADAS |
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ADAS @ adas @ Ser1995 |
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120 |
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Author |
A.F. Sole; S. Ngan; G. Sapiro; X. Hu; Antonio Lopez |
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Title |
Anisotropic 2-D and 3-D Averaging of fMRI Signals |
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Journal Article |
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Year |
2001 |
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IEEE Transactions on Medical Imaging |
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Volume |
2020 |
Issue |
2 |
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86-93 |
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ADAS |
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ADAS @ adas @ SNS2001 |
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165 |
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Author |
David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
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Title |
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
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Conference Article |
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Year |
2017 |
Publication |
31st International Congress and Exhibition on Computer Assisted Radiology and Surgery |
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Deep Learning; Medical Imaging |
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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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CARS |
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ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 |
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no |
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ADAS @ adas @ VBS2017a |
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2880 |
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Author |
J. Weickert; Bart M. Ter Haar Romeny; Antonio Lopez; W. Van Enk |
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Title |
Orientation Analysis by Coherence-Enhancing Diffusion. |
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1997 |
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Proc. of the Real World Computing Symposium. |
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ADAS |
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ADAS @ adas @ WTL1997 |
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211 |
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Author |
Marc Masana; Joost Van de Weijer; Luis Herranz;Andrew Bagdanov; Jose Manuel Alvarez |
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Title |
Domain-adaptive deep network compression |
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Conference Article |
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2017 |
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17th IEEE International Conference on Computer Vision |
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Deep Neural Networks trained on large datasets can be easily transferred to new domains with far fewer labeled examples by a process called fine-tuning. This has the advantage that representations learned in the large source domain can be exploited on smaller target domains. However, networks designed to be optimal for the source task are often prohibitively large for the target task. In this work we address the compression of networks after domain transfer.
We focus on compression algorithms based on low-rank matrix decomposition. Existing methods base compression solely on learned network weights and ignore the statistics of network activations. We show that domain transfer leads to large shifts in network activations and that it is desirable to take this into account when compressing.
We demonstrate that considering activation statistics when compressing weights leads to a rank-constrained regression problem with a closed-form solution. Because our method takes into account the target domain, it can more optimally
remove the redundancy in the weights. Experiments show that our Domain Adaptive Low Rank (DALR) method significantly outperforms existing low-rank compression techniques. With our approach, the fc6 layer of VGG19 can be compressed more than 4x more than using truncated SVD alone – with only a minor or no loss in accuracy. When applied to domain-transferred networks it allows for compression down to only 5-20% of the original number of parameters with only a minor drop in performance. |
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Venice; Italy; October 2017 |
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ICCV |
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LAMP; 601.305; 600.106; 600.120 |
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no |
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Call Number |
Admin @ si @ |
Serial |
3034 |
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Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
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Title |
Single view facial hair 3D reconstruction |
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Conference Article |
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Year |
2019 |
Publication |
9th Iberian Conference on Pattern Recognition and Image Analysis |
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11867 |
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423-436 |
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Keywords |
3D Vision; Shape Reconstruction; Facial Hair Modeling |
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Abstract |
n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. |
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Madrid; July 2019 |
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IbPRIA |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ |
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3707 |
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Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
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Title |
Detailed 3D face reconstruction from a single RGB image |
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2019 |
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Journal of WSCG |
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JWSCG |
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27 |
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2 |
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103-112 |
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3D Wrinkle Reconstruction; Face Analysis, Optimization. |
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This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial
expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles. |
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2019/11 |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ |
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3708 |
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Author |
Ricardo Toledo; S. Sallent; J. Paradell; Juan J. Villanueva |
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CARE: Computer Assisted Radiology Environment |
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1995 |
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Pattern Recognition and image analysis: preprints of the VI National Symposium on Pattern Recogniotion & Image Analysis |
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Admin @ si @ |
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4025 |
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Maryam Asadi-Aghbolaghi; Hugo Bertiche; Vicent Roig; Shohreh Kasaei; Sergio Escalera |
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Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-temporal Handcrafted Features and Deep Strategies |
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2017 |
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Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV |
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Venice; Italy; October 2017 |
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ICCVW |
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HUPBA; no menciona |
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no |
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Admin @ si @ ABR2017 |
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3068 |
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Author |
Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Gloria Fernandez Esparrach; Xavier Gray; Olivier Romain; F. Javier Sanchez; Aymeric Histace |
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Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis |
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Conference Article |
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2017 |
Publication |
4th International Workshop on Computer Assisted and Robotic Endoscopy |
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29-41 |
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Polyp detection; colonoscopy; real time; spatio temporal coherence |
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Colorectal cancer is the second cause of cancer death in United States: precursor lesions (polyps) detection is key for patient survival. Though colonoscopy is the gold standard screening tool, some polyps are still missed. Several computational systems have been proposed but none of them are used in the clinical room mainly due to computational constraints. Besides, most of them are built over still frame databases, decreasing their performance on video analysis due to the lack of output stability and not coping with associated variability on image quality and polyp appearance. We propose a strategy to adapt these methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability. We validate our strategy, incorporated on a real-time detection method, on a public video database. Resulting method detects all
polyps under real time constraints, increasing its performance due to our
adaptation strategy. |
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Quebec; Canada; September 2017 |
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Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
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Medium |
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|
| |
Area |
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Expedition |
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Conference |
CARE |
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| |
Notes |
MV; 600.096; 600.075 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ ABS2017b |
Serial |
2977 |
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